2025-01-16 12:28:05 INFO Date: 2025-01-16 ======================================== Time: 12:28:05 Logger Data: This is Insight lab log data. ---------------------------------------- 2025-01-16 12:28:05 INFO Rendering menu. 2025-01-16 12:28:06 INFO Rendering unauthenticated menu. 2025-01-16 12:28:06 INFO Rendering unauthenticated menu. 2025-01-16 12:28:28 INFO Rendering menu. 2025-01-16 12:28:28 INFO Rendering menu. 2025-01-16 12:28:28 INFO Login button clicked. 2025-01-16 12:28:28 INFO Login button clicked. 2025-01-16 12:28:31 INFO Login successful for user: nanthinisri.l 2025-01-16 12:28:31 INFO Login successful for user: nanthinisri.l 2025-01-16 12:28:31 INFO Rendering menu. 2025-01-16 12:28:31 INFO Rendering menu. 2025-01-16 12:28:36 INFO Rendering menu. 2025-01-16 12:28:36 INFO Rendering menu. 2025-01-16 12:29:15 INFO Rendering menu. 2025-01-16 12:29:15 INFO Rendering menu. 2025-01-16 12:29:17 INFO Database names fetched successfully. 2025-01-16 12:29:17 INFO Database names fetched successfully. 2025-01-16 12:29:18 INFO Rendering menu. 2025-01-16 12:29:18 INFO Rendering menu. 2025-01-16 12:29:21 INFO Rendering menu. 2025-01-16 12:29:21 INFO Rendering menu. 2025-01-16 12:29:23 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:23 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:25 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/1.json 2025-01-16 12:29:25 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/1.json 2025-01-16 12:29:25 INFO Insight list generated successfully. 2025-01-16 12:29:25 INFO Insight list generated successfully. 2025-01-16 12:29:46 INFO Rendering menu. 2025-01-16 12:29:46 INFO Rendering menu. 2025-01-16 12:29:49 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:49 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/1.json 2025-01-16 12:29:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/1.json 2025-01-16 12:29:50 INFO Insight list generated successfully. 2025-01-16 12:29:50 INFO Insight list generated successfully. 2025-01-16 12:29:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:29:51 INFO Connected to the database Insightlab. 2025-01-16 12:29:51 INFO Query executed successfully. 2025-01-16 12:29:51 INFO Query executed successfully. 2025-01-16 12:30:23 INFO Rendering menu. 2025-01-16 12:30:23 INFO Rendering menu. 2025-01-16 12:30:26 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 12:30:26 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 12:30:27 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:30:27 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 12:30:31 INFO Rendering menu. 2025-01-16 12:30:31 INFO Rendering menu. 2025-01-16 12:46:32 INFO Date: 2025-01-16 ======================================== Time: 12:46:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 12:46:32 INFO Rendering menu. 2025-01-16 12:46:37 INFO Rendering unauthenticated menu. 2025-01-16 13:31:20 INFO Rendering menu. 2025-01-16 13:31:20 INFO Login button clicked. 2025-01-16 13:31:24 INFO Login successful for user: maheshsr 2025-01-16 13:31:24 INFO Rendering menu. 2025-01-16 13:31:52 INFO Rendering menu. 2025-01-16 13:31:54 INFO Database names fetched successfully. 2025-01-16 13:32:16 INFO Rendering menu. 2025-01-16 13:32:16 INFO Database names fetched successfully. 2025-01-16 13:32:17 INFO Table details fetched successfully. 2025-01-16 13:32:33 INFO Rendering menu. 2025-01-16 13:32:33 INFO Database names fetched successfully. 2025-01-16 13:32:33 INFO Table details fetched successfully. 2025-01-16 13:32:37 INFO Rendering menu. 2025-01-16 13:32:37 INFO Database names fetched successfully. 2025-01-16 13:32:37 INFO Metadata fetched for table: NewAppointment 2025-01-16 13:33:32 INFO Rendering menu. 2025-01-16 13:33:32 INFO Database names fetched successfully. 2025-01-16 13:33:32 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 13:33:34 INFO Tokens consumed: 2970 2025-01-16 13:33:35 INFO Existing token_consumed found for month: 2025-01 2025-01-16 13:33:36 INFO token updated successfully: 2025-01 2025-01-16 13:33:36 INFO token updated successfully. 2025-01-16 13:33:36 INFO Connected to the database MHealth_Dev. 2025-01-16 13:33:36 INFO Query executed successfully. 2025-01-16 13:33:38 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 85 2025-01-16 13:33:40 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-16 13:33:41 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/86.json 2025-01-16 13:34:01 INFO Rendering menu. 2025-01-16 13:34:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:34:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:34:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:34:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:34:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:34:18 INFO Rendering menu. 2025-01-16 13:34:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:34:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:34:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:34:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:34:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:34:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:34:25 INFO Connected to the database Insightlab. 2025-01-16 13:34:25 INFO Query executed successfully. 2025-01-16 13:34:25 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:35:02 INFO Rendering menu. 2025-01-16 13:35:02 INFO Rendering menu. 2025-01-16 13:35:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:35:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:35:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:35:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:35:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:35:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:35:09 INFO Connected to the database Insightlab. 2025-01-16 13:35:09 INFO Query executed successfully. 2025-01-16 13:35:09 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:35:09 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all male patient `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 13:35:13 INFO Tokens consumed: 962 2025-01-16 13:35:14 INFO Existing token_consumed found for month: 2025-01 2025-01-16 13:35:15 INFO token updated successfully: 2025-01 2025-01-16 13:35:15 INFO token updated successfully. 2025-01-16 13:35:18 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 171 2025-01-16 13:35:20 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-16 13:35:21 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/172.py 2025-01-16 13:35:21 INFO Code generated and written in generated_method//171.py 2025-01-16 13:35:21 INFO Insight generated and displayed using AG Grid. 2025-01-16 13:35:27 INFO Rendering menu. 2025-01-16 13:35:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:35:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:35:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:35:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:35:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:35:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:35:33 INFO Connected to the database Insightlab. 2025-01-16 13:35:33 INFO Query executed successfully. 2025-01-16 13:35:33 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:35:57 INFO Rendering menu. 2025-01-16 13:35:57 INFO Rendering menu. 2025-01-16 13:35:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:35:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:35:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:35:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:36:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:36:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:36:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:36:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:36:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:36:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:36:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:36:03 INFO Connected to the database Insightlab. 2025-01-16 13:36:03 INFO Query executed successfully. 2025-01-16 13:36:03 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:36:03 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar as no of male and female patients``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 13:36:41 INFO Tokens consumed: 1966 2025-01-16 13:36:42 INFO Existing token_consumed found for month: 2025-01 2025-01-16 13:36:43 INFO token updated successfully: 2025-01 2025-01-16 13:36:43 INFO token updated successfully. 2025-01-16 13:36:47 INFO Plotly chart object created successfully. 2025-01-16 13:36:47 INFO Graph generated and displayed using Plotly. 2025-01-16 13:37:09 INFO Rendering menu. 2025-01-16 13:37:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:37:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:37:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:37:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:37:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:37:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:37:14 INFO Connected to the database Insightlab. 2025-01-16 13:37:14 INFO Query executed successfully. 2025-01-16 13:37:14 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:37:36 INFO Rendering menu. 2025-01-16 13:37:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:37:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:37:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:37:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:37:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:37:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:37:42 INFO Connected to the database Insightlab. 2025-01-16 13:37:42 INFO Query executed successfully. 2025-01-16 13:37:42 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:37:44 INFO Existing insight found for base code: SELECT * FROM Patient WHERE Age > 50; 2025-01-16 13:37:44 INFO Insight updated successfully: 3 2025-01-16 13:37:44 INFO Insight updated successfully. 2025-01-16 13:37:52 INFO Rendering menu. 2025-01-16 13:37:55 INFO Rendering menu. 2025-01-16 13:37:57 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:37:58 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:37:59 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:38:00 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:38:00 INFO Insight list generated successfully. 2025-01-16 13:38:05 INFO Rendering menu. 2025-01-16 13:38:07 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:38:08 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:38:09 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:38:10 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:38:10 INFO Insight list generated successfully. 2025-01-16 13:38:11 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:38:11 INFO Connected to the database Insightlab. 2025-01-16 13:38:11 INFO Query executed successfully. 2025-01-16 13:38:11 ERROR Error executing generated insight code: AttributeError("module 'streamlit.components.v1' has no attribute 'components'") 2025-01-16 13:38:12 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-16 13:38:34 INFO Rendering menu. 2025-01-16 13:38:36 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:38:37 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:38:38 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:38:39 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:38:39 INFO Insight list generated successfully. 2025-01-16 13:38:40 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:38:40 INFO Connected to the database MHealth_Dev. 2025-01-16 13:38:40 INFO Query executed successfully. 2025-01-16 13:38:41 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-16 13:40:04 INFO Rendering menu. 2025-01-16 13:40:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:40:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:40:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:40:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:40:21 INFO Rendering menu. 2025-01-16 13:40:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:40:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:40:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:40:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:40:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:27 INFO Connected to the database Insightlab. 2025-01-16 13:40:27 INFO Query executed successfully. 2025-01-16 13:40:27 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:40:34 INFO Rendering menu. 2025-01-16 13:40:34 INFO Rendering menu. 2025-01-16 13:40:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:40:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:40:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:40:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:40:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:40:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:40:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:40:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:40:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:40:40 INFO Connected to the database Insightlab. 2025-01-16 13:40:40 INFO Query executed successfully. 2025-01-16 13:40:40 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:40:40 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all female patient `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 13:40:44 INFO Tokens consumed: 931 2025-01-16 13:40:45 INFO Existing token_consumed found for month: 2025-01 2025-01-16 13:40:46 INFO token updated successfully: 2025-01 2025-01-16 13:40:46 INFO token updated successfully. 2025-01-16 13:40:49 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 172 2025-01-16 13:40:50 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-16 13:40:51 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/173.py 2025-01-16 13:40:51 INFO Code generated and written in generated_method//172.py 2025-01-16 13:40:51 INFO Insight generated and displayed using AG Grid. 2025-01-16 13:41:09 INFO Rendering menu. 2025-01-16 13:41:10 INFO Rendering menu. 2025-01-16 13:41:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:41:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:41:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:41:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:41:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:41:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:41:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:41:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:41:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:41:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:41:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:41:16 INFO Connected to the database Insightlab. 2025-01-16 13:41:16 INFO Query executed successfully. 2025-01-16 13:41:16 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:41:16 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a scattered graph of no of male and female patients``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 13:41:25 INFO Tokens consumed: 1966 2025-01-16 13:41:27 INFO Existing token_consumed found for month: 2025-01 2025-01-16 13:41:27 INFO token updated successfully: 2025-01 2025-01-16 13:41:27 INFO token updated successfully. 2025-01-16 13:41:28 INFO Plotly chart object created successfully. 2025-01-16 13:41:28 INFO Graph generated and displayed using Plotly. 2025-01-16 13:41:52 INFO Rendering menu. 2025-01-16 13:41:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:41:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:41:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:41:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:41:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:41:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:41:57 INFO Connected to the database Insightlab. 2025-01-16 13:41:57 INFO Query executed successfully. 2025-01-16 13:41:57 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:42:07 INFO Rendering menu. 2025-01-16 13:42:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-16 13:42:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-16 13:42:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:42:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:42:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:42:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-16 13:42:13 INFO Connected to the database Insightlab. 2025-01-16 13:42:13 INFO Query executed successfully. 2025-01-16 13:42:13 INFO Dataset columns displayed using AG Grid. 2025-01-16 13:42:15 INFO Existing insight found for base code: SELECT * FROM Patient WHERE Age > 50; 2025-01-16 13:42:16 INFO Insight updated successfully: 3 2025-01-16 13:42:16 INFO Insight updated successfully. 2025-01-16 13:42:40 INFO Rendering menu. 2025-01-16 13:42:47 INFO Rendering menu. 2025-01-16 13:42:49 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:42:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:42:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:42:52 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:42:52 INFO Insight list generated successfully. 2025-01-16 13:42:55 INFO Rendering menu. 2025-01-16 13:42:57 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:42:58 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:43:00 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:43:00 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:43:00 INFO Insight list generated successfully. 2025-01-16 13:43:02 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:43:02 INFO Connected to the database MHealth_Dev. 2025-01-16 13:43:02 INFO Query executed successfully. 2025-01-16 13:43:02 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-16 13:43:33 INFO Rendering menu. 2025-01-16 13:43:35 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:43:36 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:43:37 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:43:38 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:43:38 INFO Insight list generated successfully. 2025-01-16 13:43:39 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:43:39 INFO Connected to the database Insightlab. 2025-01-16 13:43:39 INFO Query executed successfully. 2025-01-16 13:43:39 ERROR Error executing generated insight code: AttributeError("module 'streamlit.components.v1' has no attribute 'components'") 2025-01-16 13:43:39 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-16 13:44:41 INFO Rendering menu. 2025-01-16 13:44:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-16 13:44:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-16 13:44:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-16 13:44:47 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:44:47 INFO Insight list generated successfully. 2025-01-16 13:44:48 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-16 13:44:48 INFO Connected to the database Insightlab. 2025-01-16 13:44:48 INFO Query executed successfully. 2025-01-16 13:45:45 INFO Rendering menu. 2025-01-16 13:45:45 INFO User logged out. 2025-01-16 13:45:46 INFO Date: 2025-01-16 ======================================== Time: 13:45:46 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:45:46 INFO Date: 2025-01-16 ======================================== Time: 13:45:46 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:45:46 INFO Rendering menu. 2025-01-16 13:45:46 INFO Rendering unauthenticated menu. 2025-01-16 13:45:46 INFO Rendering unauthenticated menu. 2025-01-16 13:45:50 INFO Rendering menu. 2025-01-16 13:45:50 INFO Rendering menu. 2025-01-16 13:45:50 INFO Login button clicked. 2025-01-16 13:45:50 INFO Login button clicked. 2025-01-16 13:45:53 INFO Login successful for user: nanthinisri.l 2025-01-16 13:45:53 INFO Login successful for user: nanthinisri.l 2025-01-16 13:45:53 INFO Rendering menu. 2025-01-16 13:45:53 INFO Rendering menu. 2025-01-16 13:46:00 INFO Rendering menu. 2025-01-16 13:46:00 INFO Rendering menu. 2025-01-16 13:46:00 INFO Database names fetched successfully. 2025-01-16 13:46:00 INFO Database names fetched successfully. 2025-01-16 13:46:02 INFO Rendering menu. 2025-01-16 13:46:02 INFO Rendering menu. 2025-01-16 13:46:04 INFO Rendering menu. 2025-01-16 13:46:04 INFO Rendering menu. 2025-01-16 13:46:04 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 13:46:04 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 13:46:05 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 13:46:05 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 13:46:06 INFO Rendering menu. 2025-01-16 13:46:06 INFO Rendering menu. 2025-01-16 13:46:09 INFO Rendering menu. 2025-01-16 13:46:09 INFO Rendering menu. 2025-01-16 13:46:10 INFO Rendering menu. 2025-01-16 13:46:10 INFO Rendering menu. 2025-01-16 13:48:57 INFO Rendering menu. 2025-01-16 13:48:57 INFO Rendering menu. 2025-01-16 13:48:58 INFO User logged out. 2025-01-16 13:48:58 INFO User logged out. 2025-01-16 13:48:58 INFO Date: 2025-01-16 ======================================== Time: 13:48:58 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:48:58 INFO Date: 2025-01-16 ======================================== Time: 13:48:58 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:48:58 INFO Date: 2025-01-16 ======================================== Time: 13:48:58 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:48:58 INFO Rendering menu. 2025-01-16 13:48:58 INFO Rendering menu. 2025-01-16 13:48:58 INFO Rendering menu. 2025-01-16 13:48:58 INFO Rendering unauthenticated menu. 2025-01-16 13:48:58 INFO Rendering unauthenticated menu. 2025-01-16 13:48:58 INFO Rendering unauthenticated menu. 2025-01-16 13:49:12 INFO Rendering menu. 2025-01-16 13:49:12 INFO Rendering menu. 2025-01-16 13:49:12 INFO Rendering menu. 2025-01-16 13:49:12 INFO Login button clicked. 2025-01-16 13:49:12 INFO Login button clicked. 2025-01-16 13:49:12 INFO Login button clicked. 2025-01-16 13:49:15 INFO Login successful for user: abhishek 2025-01-16 13:49:15 INFO Login successful for user: abhishek 2025-01-16 13:49:15 INFO Login successful for user: abhishek 2025-01-16 13:49:16 INFO Rendering menu. 2025-01-16 13:49:16 INFO Rendering menu. 2025-01-16 13:49:16 INFO Rendering menu. 2025-01-16 13:49:21 INFO Rendering menu. 2025-01-16 13:49:21 INFO Rendering menu. 2025-01-16 13:49:21 INFO Rendering menu. 2025-01-16 13:49:21 INFO Database names fetched successfully. 2025-01-16 13:49:21 INFO Database names fetched successfully. 2025-01-16 13:49:21 INFO Database names fetched successfully. 2025-01-16 13:49:24 INFO Rendering menu. 2025-01-16 13:49:24 INFO Rendering menu. 2025-01-16 13:49:24 INFO Rendering menu. 2025-01-16 13:49:26 INFO Rendering menu. 2025-01-16 13:49:26 INFO Rendering menu. 2025-01-16 13:49:26 INFO Rendering menu. 2025-01-16 13:49:28 INFO Rendering menu. 2025-01-16 13:49:28 INFO Rendering menu. 2025-01-16 13:49:28 INFO Rendering menu. 2025-01-16 13:49:29 INFO Rendering menu. 2025-01-16 13:49:29 INFO Rendering menu. 2025-01-16 13:49:29 INFO Database names fetched successfully. 2025-01-16 13:49:29 INFO Database names fetched successfully. 2025-01-16 13:49:34 INFO Rendering menu. 2025-01-16 13:49:34 INFO Rendering menu. 2025-01-16 13:49:34 INFO Rendering menu. 2025-01-16 13:50:36 INFO Rendering menu. 2025-01-16 13:50:36 INFO Rendering menu. 2025-01-16 13:50:36 INFO Rendering menu. 2025-01-16 13:50:37 INFO Database names fetched successfully. 2025-01-16 13:50:37 INFO Database names fetched successfully. 2025-01-16 13:50:37 INFO Database names fetched successfully. 2025-01-16 13:50:38 INFO Rendering menu. 2025-01-16 13:50:38 INFO Rendering menu. 2025-01-16 13:50:38 INFO Rendering menu. 2025-01-16 13:54:11 INFO Date: 2025-01-16 ======================================== Time: 13:54:11 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:54:11 INFO Rendering menu. 2025-01-16 13:54:15 INFO Rendering unauthenticated menu. 2025-01-16 13:54:58 INFO Rendering menu. 2025-01-16 13:54:58 INFO Login button clicked. 2025-01-16 13:55:01 INFO Login successful for user: abhishek 2025-01-16 13:55:02 INFO Rendering menu. 2025-01-16 13:55:23 INFO Rendering menu. 2025-01-16 13:55:24 INFO Database names fetched successfully. 2025-01-16 13:55:40 INFO Rendering menu. 2025-01-16 13:55:58 INFO Rendering menu. 2025-01-16 13:55:58 INFO Database names fetched successfully. 2025-01-16 13:57:41 INFO Date: 2025-01-16 ======================================== Time: 13:57:41 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:57:41 INFO Date: 2025-01-16 ======================================== Time: 13:57:41 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 13:57:41 INFO Rendering menu. 2025-01-16 13:57:41 INFO Rendering menu. 2025-01-16 13:57:41 INFO Rendering unauthenticated menu. 2025-01-16 13:57:41 INFO Rendering unauthenticated menu. 2025-01-16 13:57:58 INFO Login button clicked. 2025-01-16 13:57:58 INFO Login button clicked. 2025-01-16 13:58:02 INFO Login successful for user: abhishek 2025-01-16 13:58:02 INFO Login successful for user: abhishek 2025-01-16 13:58:02 INFO Rendering menu. 2025-01-16 13:58:02 INFO Rendering menu. 2025-01-16 13:58:02 INFO Database names fetched successfully. 2025-01-16 13:58:02 INFO Database names fetched successfully. 2025-01-16 14:00:33 INFO Date: 2025-01-16 ======================================== Time: 14:00:33 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:00:33 INFO Rendering menu. 2025-01-16 14:00:38 INFO Rendering unauthenticated menu. 2025-01-16 14:00:57 INFO Rendering menu. 2025-01-16 14:00:57 INFO Login button clicked. 2025-01-16 14:01:01 INFO Login successful for user: abhishek 2025-01-16 14:01:01 INFO Rendering menu. 2025-01-16 14:01:19 INFO Rendering menu. 2025-01-16 14:01:19 INFO Database names fetched successfully. 2025-01-16 14:01:37 INFO Rendering menu. 2025-01-16 14:02:04 INFO Rendering menu. 2025-01-16 14:02:06 INFO Rendering menu. 2025-01-16 14:02:08 INFO Rendering menu. 2025-01-16 14:02:08 INFO Database names fetched successfully. 2025-01-16 14:02:10 INFO Rendering menu. 2025-01-16 14:02:10 INFO Database names fetched successfully. 2025-01-16 14:02:11 INFO Table details fetched successfully. 2025-01-16 14:02:38 INFO Rendering menu. 2025-01-16 14:02:38 INFO Database names fetched successfully. 2025-01-16 14:02:38 INFO Metadata fetched for table: Patient 2025-01-16 14:04:23 INFO Rendering menu. 2025-01-16 14:04:23 INFO Database names fetched successfully. 2025-01-16 14:04:23 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'patient_sdoh_scores': 'Insightlab - patient_sdoh_scores - Table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicates a score about one patient and one type of assessment.', 'EpisodeOfCare': 'Insightlab - EpisodeOfCare - Contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episode of care for a patient. One patient may have multiple episodes of care.', 'RiskScore': 'Insightlab - RiskScore - Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has a risk score of a unique patient.', 'Patient': 'Insightlab - Patient - The table stores the healthcare encounter information about patients. Each row has unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Insightlab - Encounter - Table that stores all encounters of each patient with the healthcare providers. Every row indicates a single encounter.'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Encounter': [{'name': 'id', 'type': 'nvarchar', 'description': 'Encounter id that identifies an encounter uniquely'}, {'name': 'status', 'type': 'nvarchar', 'description': "Encounter status, can be one of 'planned', 'completed', 'discharged', 'in-progress'"}, {'name': 'class', 'type': 'nvarchar', 'description': "Indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health"}, {'name': 'priority', 'type': 'nvarchar', 'description': "Indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine"}, {'name': 'subject_id', 'type': 'nvarchar', 'description': 'Indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table'}, {'name': 'subject_status', 'type': 'nvarchar', 'description': ''}, {'name': 'actual_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'actual_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'length', 'type': 'smallint', 'description': ''}, {'name': 'service_provider_id', 'type': 'nvarchar', 'description': 'Contains the id of the care delivery organization where the patient had the encounter'}, {'name': 'part_of_encounter_id', 'type': 'nvarchar', 'description': ''}, {'name': 'appointment_id', 'type': 'nvarchar', 'description': ''}, {'name': 'participant_actor_id', 'type': 'nvarchar', 'description': 'Contains the id of the provider associated with the care delivery organization who rendered the encounter'}, {'name': 'participant_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'participant_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'Contains list of diagnosis codes relevant to the patient of the encounter'}, {'name': 'diagnosis_condition_text', 'type': 'nvarchar', 'description': ''}, {'name': 'condition_class', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'location_id', 'type': 'nvarchar', 'description': 'Location where the encounter happened or is happening or will be happening'}, {'name': 'location_status', 'type': 'nvarchar', 'description': ''}, {'name': 'location_form', 'type': 'nvarchar', 'description': ''}, {'name': 'location_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'location_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'origin_id', 'type': 'nvarchar', 'description': ''}, {'name': 'admit_source', 'type': 'nvarchar', 'description': ''}, {'name': 're_admission', 'type': 'nvarchar', 'description': ''}, {'name': 'destination_id', 'type': 'nvarchar', 'description': ''}, {'name': 'discharge_disposition', 'type': 'nvarchar', 'description': 'How the patient was discharged at the end of the encounter'}], 'EpisodeOfCare': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Unique identifier of the episode'}, {'name': 'status', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'type', 'type': 'nvarchar', 'description': 'Type of episode, can be disease management, post acute care or specialist referral'}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'ICD-10 diagnosis code associated with the episode of care'}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'subject_id', 'type': 'nvarchar', 'description': "ID of the patient associated with the episode, should have a corresponding 'identifier_value' in the Patient table"}, {'name': 'managing_organization_id', 'type': 'nvarchar', 'description': 'Contains the ID of the organization managing the episode'}, {'name': 'period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'referral_request_id', 'type': 'nvarchar', 'description': ''}, {'name': 'care_manager_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care manager managing the episode'}, {'name': 'care_team_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care team managing the episode. Care manager is part of the care team'}, {'name': 'account_id', 'type': 'nvarchar', 'description': ''}], 'Patient': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Patient identifier that uniquely identifies patient and links a patient from this to other tables'}, {'name': 'identifier_use', 'type': 'nvarchar', 'description': 'If the identifier is used for any specific purpose'}, {'name': 'identifier_type', 'type': 'nvarchar', 'description': 'Type of identifier, usually means the source. MR stands for medical record'}, {'name': 'identifier_start_date', 'type': 'date', 'description': 'Date on since when the identifier was valid'}, {'name': 'identifier_assigner', 'type': 'nvarchar', 'description': 'Identification value assignment authority'}, {'name': 'active', 'type': 'nvarchar', 'description': 'If the patient is active or not'}, {'name': 'official_name_family', 'type': 'nvarchar', 'description': 'Family name of the patient'}, {'name': 'official_name_given', 'type': 'nvarchar', 'description': 'Given name of the patient'}, {'name': 'usual_name_given', 'type': 'nvarchar', 'description': 'Short form of the given name'}, {'name': 'gender', 'type': 'nvarchar', 'description': "Patient's gender, male or female"}, {'name': 'birth_date', 'type': 'date', 'description': 'Date of birth of the patient'}, {'name': 'Age', 'type': 'tinyint', 'description': 'Patient age'}, {'name': 'home_address_line', 'type': 'nvarchar', 'description': "Patient's home address street"}, {'name': 'home_address_city', 'type': 'nvarchar', 'description': "Patient's home address city"}, {'name': 'home_address_district', 'type': 'nvarchar', 'description': "Patient's home county"}, {'name': 'home_address_state', 'type': 'nvarchar', 'description': "Patient's home state"}, {'name': 'home_address_postalCode', 'type': 'smallint', 'description': "Patient's home address zip code"}, {'name': 'home_address_period_start', 'type': 'date', 'description': "Start date of the patient's home address"}], 'RiskScore': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Identifier that uniquely identifies a patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'risk_score', 'type': 'float', 'description': 'Decimal number between 0 and 1 indicating the risk score'}, {'name': 'risk_score_date', 'type': 'nvarchar', 'description': ''}], 'patient_sdoh_scores': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Unique identifier of the patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'assessment_id', 'type': 'nvarchar', 'description': 'Name of the assessment'}, {'name': 'answer', 'type': 'tinyint', 'description': 'The actual answer provided in the assessment'}, {'name': 'assessment_type', 'type': 'nvarchar', 'description': "Type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'"}, {'name': 'score', 'type': 'float', 'description': 'Derived standardized score based on the answer provided'}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patient```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 14:04:25 INFO Tokens consumed: 2525 2025-01-16 14:04:26 INFO No existing token_consumed found for month: 2025-01 2025-01-16 14:04:27 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-01-16 14:04:27 INFO Creating a new folder in the blob storage: 2025-01-16 14:04:29 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:04:30 INFO New token created: token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/2025-01.json 2025-01-16 14:04:30 INFO Connected to the database Insightlab. 2025-01-16 14:04:30 INFO Query executed successfully. 2025-01-16 14:04:31 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-01-16 14:04:32 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-01-16 14:04:34 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:04:36 INFO Rendering menu. 2025-01-16 14:04:36 INFO Database names fetched successfully. 2025-01-16 14:04:37 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-01-16 14:04:38 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:04:38 INFO Query saved in the library with id 1. 2025-01-16 14:05:34 INFO Rendering menu. 2025-01-16 14:05:34 INFO Database names fetched successfully. 2025-01-16 14:05:35 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:05:36 ERROR Exception while saving SQL query blob: The specified blob already exists. RequestId:8196ce51-501e-00ea-67f1-67bdaf000000 Time:2025-01-16T08:35:38.0876696Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:8196ce51-501e-00ea-67f1-67bdaf000000 Time:2025-01-16T08:35:38.0876696Z 2025-01-16 14:05:42 INFO Rendering menu. 2025-01-16 14:05:42 INFO Database names fetched successfully. 2025-01-16 14:05:42 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'patient_sdoh_scores': 'Insightlab - patient_sdoh_scores - Table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicates a score about one patient and one type of assessment.', 'EpisodeOfCare': 'Insightlab - EpisodeOfCare - Contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episode of care for a patient. One patient may have multiple episodes of care.', 'RiskScore': 'Insightlab - RiskScore - Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has a risk score of a unique patient.', 'Patient': 'Insightlab - Patient - The table stores the healthcare encounter information about patients. Each row has unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Insightlab - Encounter - Table that stores all encounters of each patient with the healthcare providers. Every row indicates a single encounter.'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Encounter': [{'name': 'id', 'type': 'nvarchar', 'description': 'Encounter id that identifies an encounter uniquely'}, {'name': 'status', 'type': 'nvarchar', 'description': "Encounter status, can be one of 'planned', 'completed', 'discharged', 'in-progress'"}, {'name': 'class', 'type': 'nvarchar', 'description': "Indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health"}, {'name': 'priority', 'type': 'nvarchar', 'description': "Indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine"}, {'name': 'subject_id', 'type': 'nvarchar', 'description': 'Indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table'}, {'name': 'subject_status', 'type': 'nvarchar', 'description': ''}, {'name': 'actual_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'actual_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'length', 'type': 'smallint', 'description': ''}, {'name': 'service_provider_id', 'type': 'nvarchar', 'description': 'Contains the id of the care delivery organization where the patient had the encounter'}, {'name': 'part_of_encounter_id', 'type': 'nvarchar', 'description': ''}, {'name': 'appointment_id', 'type': 'nvarchar', 'description': ''}, {'name': 'participant_actor_id', 'type': 'nvarchar', 'description': 'Contains the id of the provider associated with the care delivery organization who rendered the encounter'}, {'name': 'participant_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'participant_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'Contains list of diagnosis codes relevant to the patient of the encounter'}, {'name': 'diagnosis_condition_text', 'type': 'nvarchar', 'description': ''}, {'name': 'condition_class', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'location_id', 'type': 'nvarchar', 'description': 'Location where the encounter happened or is happening or will be happening'}, {'name': 'location_status', 'type': 'nvarchar', 'description': ''}, {'name': 'location_form', 'type': 'nvarchar', 'description': ''}, {'name': 'location_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'location_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'origin_id', 'type': 'nvarchar', 'description': ''}, {'name': 'admit_source', 'type': 'nvarchar', 'description': ''}, {'name': 're_admission', 'type': 'nvarchar', 'description': ''}, {'name': 'destination_id', 'type': 'nvarchar', 'description': ''}, {'name': 'discharge_disposition', 'type': 'nvarchar', 'description': 'How the patient was discharged at the end of the encounter'}], 'EpisodeOfCare': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Unique identifier of the episode'}, {'name': 'status', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'type', 'type': 'nvarchar', 'description': 'Type of episode, can be disease management, post acute care or specialist referral'}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'ICD-10 diagnosis code associated with the episode of care'}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'subject_id', 'type': 'nvarchar', 'description': "ID of the patient associated with the episode, should have a corresponding 'identifier_value' in the Patient table"}, {'name': 'managing_organization_id', 'type': 'nvarchar', 'description': 'Contains the ID of the organization managing the episode'}, {'name': 'period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'referral_request_id', 'type': 'nvarchar', 'description': ''}, {'name': 'care_manager_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care manager managing the episode'}, {'name': 'care_team_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care team managing the episode. Care manager is part of the care team'}, {'name': 'account_id', 'type': 'nvarchar', 'description': ''}], 'Patient': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Patient identifier that uniquely identifies patient and links a patient from this to other tables'}, {'name': 'identifier_use', 'type': 'nvarchar', 'description': 'If the identifier is used for any specific purpose'}, {'name': 'identifier_type', 'type': 'nvarchar', 'description': 'Type of identifier, usually means the source. MR stands for medical record'}, {'name': 'identifier_start_date', 'type': 'date', 'description': 'Date on since when the identifier was valid'}, {'name': 'identifier_assigner', 'type': 'nvarchar', 'description': 'Identification value assignment authority'}, {'name': 'active', 'type': 'nvarchar', 'description': 'If the patient is active or not'}, {'name': 'official_name_family', 'type': 'nvarchar', 'description': 'Family name of the patient'}, {'name': 'official_name_given', 'type': 'nvarchar', 'description': 'Given name of the patient'}, {'name': 'usual_name_given', 'type': 'nvarchar', 'description': 'Short form of the given name'}, {'name': 'gender', 'type': 'nvarchar', 'description': "Patient's gender, male or female"}, {'name': 'birth_date', 'type': 'date', 'description': 'Date of birth of the patient'}, {'name': 'Age', 'type': 'tinyint', 'description': 'Patient age'}, {'name': 'home_address_line', 'type': 'nvarchar', 'description': "Patient's home address street"}, {'name': 'home_address_city', 'type': 'nvarchar', 'description': "Patient's home address city"}, {'name': 'home_address_district', 'type': 'nvarchar', 'description': "Patient's home county"}, {'name': 'home_address_state', 'type': 'nvarchar', 'description': "Patient's home state"}, {'name': 'home_address_postalCode', 'type': 'smallint', 'description': "Patient's home address zip code"}, {'name': 'home_address_period_start', 'type': 'date', 'description': "Start date of the patient's home address"}], 'RiskScore': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Identifier that uniquely identifies a patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'risk_score', 'type': 'float', 'description': 'Decimal number between 0 and 1 indicating the risk score'}, {'name': 'risk_score_date', 'type': 'nvarchar', 'description': ''}], 'patient_sdoh_scores': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Unique identifier of the patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'assessment_id', 'type': 'nvarchar', 'description': 'Name of the assessment'}, {'name': 'answer', 'type': 'tinyint', 'description': 'The actual answer provided in the assessment'}, {'name': 'assessment_type', 'type': 'nvarchar', 'description': "Type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'"}, {'name': 'score', 'type': 'float', 'description': 'Derived standardized score based on the answer provided'}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````combine the patient and risk score where patient risk score is above 0.5```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 14:05:45 INFO Tokens consumed: 2574 2025-01-16 14:05:46 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:05:48 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:05:49 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:05:50 ERROR Error while creating new token: The specified blob already exists. RequestId:bc45e7b5-a01e-00d1-70f1-67f80b000000 Time:2025-01-16T08:35:51.6794228Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:bc45e7b5-a01e-00d1-70f1-67f80b000000 Time:2025-01-16T08:35:51.6794228Z 2025-01-16 14:05:50 INFO Connected to the database Insightlab. 2025-01-16 14:05:50 INFO Query executed successfully. 2025-01-16 14:05:51 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-01-16 14:05:52 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:05:53 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:06:01 INFO Rendering menu. 2025-01-16 14:06:01 INFO Database names fetched successfully. 2025-01-16 14:06:02 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:06:03 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:06:03 INFO Query saved in the library with id 2. 2025-01-16 14:06:30 INFO Rendering menu. 2025-01-16 14:06:40 INFO Rendering menu. 2025-01-16 14:06:40 INFO Rendering menu. 2025-01-16 14:06:40 INFO Rendering menu. 2025-01-16 14:06:43 INFO Rendering menu. 2025-01-16 14:06:44 INFO Rendering menu. 2025-01-16 14:06:46 INFO Rendering menu. 2025-01-16 14:06:55 INFO Date: 2025-01-16 ======================================== Time: 14:06:55 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:06:55 INFO Date: 2025-01-16 ======================================== Time: 14:06:55 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:06:55 INFO Rendering menu. 2025-01-16 14:06:55 INFO Rendering menu. 2025-01-16 14:06:56 INFO Rendering unauthenticated menu. 2025-01-16 14:06:56 INFO Rendering unauthenticated menu. 2025-01-16 14:09:31 INFO Rendering menu. 2025-01-16 14:09:31 INFO Rendering menu. 2025-01-16 14:09:31 INFO Login button clicked. 2025-01-16 14:09:31 INFO Login button clicked. 2025-01-16 14:09:35 INFO Login successful for user: abhishek 2025-01-16 14:09:35 INFO Login successful for user: abhishek 2025-01-16 14:09:35 INFO Rendering menu. 2025-01-16 14:09:35 INFO Rendering menu. 2025-01-16 14:09:40 INFO Rendering menu. 2025-01-16 14:09:40 INFO Rendering menu. 2025-01-16 14:09:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:09:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:09:51 INFO Rendering menu. 2025-01-16 14:09:51 INFO Rendering menu. 2025-01-16 14:09:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:09:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:09:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:09:55 INFO Connected to the database Insightlab. 2025-01-16 14:09:55 INFO Connected to the database Insightlab. 2025-01-16 14:09:55 INFO Query executed successfully. 2025-01-16 14:09:55 INFO Query executed successfully. 2025-01-16 14:09:55 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:09:55 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:11:00 INFO Rendering menu. 2025-01-16 14:11:00 INFO Rendering menu. 2025-01-16 14:11:00 INFO Rendering menu. 2025-01-16 14:11:00 INFO Rendering menu. 2025-01-16 14:11:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:11:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:11:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:11:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:11:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:11:04 INFO Connected to the database Insightlab. 2025-01-16 14:11:04 INFO Connected to the database Insightlab. 2025-01-16 14:11:04 INFO Query executed successfully. 2025-01-16 14:11:04 INFO Query executed successfully. 2025-01-16 14:11:04 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:11:04 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:11:04 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.7`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:11:04 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.7`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:11:07 INFO Tokens consumed: 971 2025-01-16 14:11:09 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:11:09 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:11:10 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:11:10 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:11:11 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:11:11 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:11:12 ERROR Error while creating new token: The specified blob already exists. RequestId:6c160917-c01e-007e-2cf2-670ac6000000 Time:2025-01-16T08:41:14.0720606Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:6c160917-c01e-007e-2cf2-670ac6000000 Time:2025-01-16T08:41:14.0720606Z 2025-01-16 14:11:12 ERROR Error while creating new token: The specified blob already exists. RequestId:6c160917-c01e-007e-2cf2-670ac6000000 Time:2025-01-16T08:41:14.0720606Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:6c160917-c01e-007e-2cf2-670ac6000000 Time:2025-01-16T08:41:14.0720606Z 2025-01-16 14:11:13 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-01-16 14:11:13 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-01-16 14:11:14 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-01-16 14:11:16 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/1.py 2025-01-16 14:11:16 INFO Code generated and written in generated_method//0.py 2025-01-16 14:11:16 INFO Code generated and written in generated_method//0.py 2025-01-16 14:11:16 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:11:16 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:12:01 INFO Rendering menu. 2025-01-16 14:12:01 INFO Rendering menu. 2025-01-16 14:12:01 INFO Rendering menu. 2025-01-16 14:12:01 INFO Rendering menu. 2025-01-16 14:12:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:05 INFO Connected to the database Insightlab. 2025-01-16 14:12:05 INFO Query executed successfully. 2025-01-16 14:12:05 INFO Query executed successfully. 2025-01-16 14:12:05 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:12:05 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:12:05 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph no of patient with risk score as 0.5 , 0.6 and 0.7 ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 14:12:05 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph no of patient with risk score as 0.5 , 0.6 and 0.7 ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 14:12:09 INFO Tokens consumed: 1788 2025-01-16 14:12:09 INFO Tokens consumed: 1788 2025-01-16 14:12:10 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:12:10 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:12:12 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:12:12 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:12:13 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:12:13 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:12:14 ERROR Error while creating new token: The specified blob already exists. RequestId:161c4fe7-801e-0112-19f2-671f5c000000 Time:2025-01-16T08:42:15.9997383Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:161c4fe7-801e-0112-19f2-671f5c000000 Time:2025-01-16T08:42:15.9997383Z 2025-01-16 14:12:14 ERROR Error while creating new token: The specified blob already exists. RequestId:161c4fe7-801e-0112-19f2-671f5c000000 Time:2025-01-16T08:42:15.9997383Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:161c4fe7-801e-0112-19f2-671f5c000000 Time:2025-01-16T08:42:15.9997383Z 2025-01-16 14:12:19 INFO Plotly chart object created successfully. 2025-01-16 14:12:19 INFO Plotly chart object created successfully. 2025-01-16 14:12:19 INFO Graph generated and displayed using Plotly. 2025-01-16 14:12:19 INFO Graph generated and displayed using Plotly. 2025-01-16 14:12:54 INFO Rendering menu. 2025-01-16 14:12:54 INFO Rendering menu. 2025-01-16 14:12:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:12:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:12:57 INFO Connected to the database Insightlab. 2025-01-16 14:12:57 INFO Connected to the database Insightlab. 2025-01-16 14:12:57 INFO Query executed successfully. 2025-01-16 14:12:57 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:12:57 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:07 INFO Rendering menu. 2025-01-16 14:13:07 INFO Rendering menu. 2025-01-16 14:13:07 INFO Rendering menu. 2025-01-16 14:13:07 INFO Rendering menu. 2025-01-16 14:13:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:11 INFO Connected to the database Insightlab. 2025-01-16 14:13:11 INFO Connected to the database Insightlab. 2025-01-16 14:13:11 INFO Query executed successfully. 2025-01-16 14:13:11 INFO Query executed successfully. 2025-01-16 14:13:11 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:11 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:12 INFO No existing insight found for base code: SELECT Patient.*, RiskScore.risk_score FROM Patient JOIN RiskScore ON Patient.identifier_value = RiskScore.patient_id WHERE RiskScore.risk_score > 0.5; 2025-01-16 14:13:12 INFO No existing insight found for base code: SELECT Patient.*, RiskScore.risk_score FROM Patient JOIN RiskScore ON Patient.identifier_value = RiskScore.patient_id WHERE RiskScore.risk_score > 0.5; 2025-01-16 14:13:13 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-01-16 14:13:13 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-01-16 14:13:13 INFO Creating a new folder in the blob storage: 2025-01-16 14:13:13 INFO Creating a new folder in the blob storage: 2025-01-16 14:13:15 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-01-16 14:13:15 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-01-16 14:13:15 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:15 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:17 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:17 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:26 INFO Rendering menu. 2025-01-16 14:13:26 INFO Rendering menu. 2025-01-16 14:13:26 INFO Rendering menu. 2025-01-16 14:13:26 INFO Rendering menu. 2025-01-16 14:13:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:29 INFO Connected to the database Insightlab. 2025-01-16 14:13:29 INFO Query executed successfully. 2025-01-16 14:13:29 INFO Query executed successfully. 2025-01-16 14:13:29 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:29 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:29 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.8`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:13:29 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.8`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:13:32 INFO Tokens consumed: 970 2025-01-16 14:13:32 INFO Tokens consumed: 970 2025-01-16 14:13:34 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:13:34 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:13:34 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:13:34 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:13:35 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:35 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:36 ERROR Error while creating new token: The specified blob already exists. RequestId:d24942e7-001e-010c-66f2-67f384000000 Time:2025-01-16T08:43:38.4339416Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:d24942e7-001e-010c-66f2-67f384000000 Time:2025-01-16T08:43:38.4339416Z 2025-01-16 14:13:36 ERROR Error while creating new token: The specified blob already exists. RequestId:d24942e7-001e-010c-66f2-67f384000000 Time:2025-01-16T08:43:38.4339416Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:d24942e7-001e-010c-66f2-67f384000000 Time:2025-01-16T08:43:38.4339416Z 2025-01-16 14:13:37 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-01-16 14:13:38 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:38 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:13:39 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/2.py 2025-01-16 14:13:39 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/2.py 2025-01-16 14:13:39 INFO Code generated and written in generated_method//1.py 2025-01-16 14:13:39 INFO Code generated and written in generated_method//1.py 2025-01-16 14:13:39 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:13:39 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:13:50 INFO Rendering menu. 2025-01-16 14:13:50 INFO Rendering menu. 2025-01-16 14:13:51 INFO Rendering menu. 2025-01-16 14:13:51 INFO Rendering menu. 2025-01-16 14:13:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:13:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:13:54 INFO Connected to the database Insightlab. 2025-01-16 14:13:54 INFO Connected to the database Insightlab. 2025-01-16 14:13:54 INFO Query executed successfully. 2025-01-16 14:13:54 INFO Query executed successfully. 2025-01-16 14:13:54 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:54 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:13:54 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a sccattered graph no of patient with risk score as 0.5 , 0.6 and 0.7 ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 14:13:54 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a sccattered graph no of patient with risk score as 0.5 , 0.6 and 0.7 ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-16 14:14:00 INFO Tokens consumed: 1803 2025-01-16 14:14:00 INFO Tokens consumed: 1803 2025-01-16 14:14:02 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:14:02 ERROR Error while retrieving token_consumed: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:14:03 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:14:03 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:14:03 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:14:03 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:14:04 ERROR Error while creating new token: The specified blob already exists. RequestId:f25fb097-401e-0002-30f2-672439000000 Time:2025-01-16T08:44:06.6253820Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:f25fb097-401e-0002-30f2-672439000000 Time:2025-01-16T08:44:06.6253820Z 2025-01-16 14:14:04 ERROR Error while creating new token: The specified blob already exists. RequestId:f25fb097-401e-0002-30f2-672439000000 Time:2025-01-16T08:44:06.6253820Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:f25fb097-401e-0002-30f2-672439000000 Time:2025-01-16T08:44:06.6253820Z 2025-01-16 14:14:05 INFO Plotly chart object created successfully. 2025-01-16 14:14:05 INFO Plotly chart object created successfully. 2025-01-16 14:14:05 INFO Graph generated and displayed using Plotly. 2025-01-16 14:14:05 INFO Graph generated and displayed using Plotly. 2025-01-16 14:14:29 INFO Rendering menu. 2025-01-16 14:14:29 INFO Rendering menu. 2025-01-16 14:14:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:14:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:14:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:14:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:14:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:14:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:14:32 INFO Connected to the database Insightlab. 2025-01-16 14:14:32 INFO Connected to the database Insightlab. 2025-01-16 14:14:32 INFO Query executed successfully. 2025-01-16 14:14:32 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:14:32 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:14:34 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:14:34 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-01-16 14:14:35 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:14:35 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-16 14:14:36 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-01-16 14:14:36 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-01-16 14:14:37 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:14:37 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:14:38 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:14:38 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:16:32 INFO Rendering menu. 2025-01-16 14:16:32 INFO Rendering menu. 2025-01-16 14:16:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:16:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:16:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:16:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:16:35 INFO Connected to the database Insightlab. 2025-01-16 14:16:35 INFO Connected to the database Insightlab. 2025-01-16 14:16:35 INFO Query executed successfully. 2025-01-16 14:16:35 INFO Query executed successfully. 2025-01-16 14:16:35 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:16:35 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:16:37 INFO Existing insight found for base code: SELECT Patient.*, RiskScore.risk_score FROM Patient JOIN RiskScore ON Patient.identifier_value = RiskScore.patient_id WHERE RiskScore.risk_score > 0.5; 2025-01-16 14:16:37 INFO Existing insight found for base code: SELECT Patient.*, RiskScore.risk_score FROM Patient JOIN RiskScore ON Patient.identifier_value = RiskScore.patient_id WHERE RiskScore.risk_score > 0.5; 2025-01-16 14:16:38 INFO Insight updated successfully: 1 2025-01-16 14:16:38 INFO Insight updated successfully: 1 2025-01-16 14:16:38 INFO Insight updated successfully. 2025-01-16 14:16:38 INFO Insight updated successfully. 2025-01-16 14:16:40 INFO Rendering menu. 2025-01-16 14:16:40 INFO Rendering menu. 2025-01-16 14:16:43 INFO Rendering menu. 2025-01-16 14:16:43 INFO Rendering menu. 2025-01-16 14:16:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:46 INFO Insight list generated successfully. 2025-01-16 14:16:46 INFO Insight list generated successfully. 2025-01-16 14:16:49 INFO Rendering menu. 2025-01-16 14:16:49 INFO Rendering menu. 2025-01-16 14:16:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:51 INFO Insight list generated successfully. 2025-01-16 14:16:51 INFO Insight list generated successfully. 2025-01-16 14:16:52 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:52 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:16:53 INFO Connected to the database Insightlab. 2025-01-16 14:16:53 INFO Connected to the database Insightlab. 2025-01-16 14:16:53 INFO Query executed successfully. 2025-01-16 14:16:53 INFO Query executed successfully. 2025-01-16 14:17:11 INFO Rendering menu. 2025-01-16 14:17:11 INFO Rendering menu. 2025-01-16 14:17:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:17:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:17:41 INFO Rendering menu. 2025-01-16 14:17:41 INFO Rendering menu. 2025-01-16 14:17:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:17:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:17:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:17:44 INFO Connected to the database Insightlab. 2025-01-16 14:17:44 INFO Connected to the database Insightlab. 2025-01-16 14:17:44 INFO Query executed successfully. 2025-01-16 14:17:44 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:17:44 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:19:55 INFO Rendering menu. 2025-01-16 14:19:55 INFO Rendering menu. 2025-01-16 14:19:56 INFO Rendering menu. 2025-01-16 14:19:56 INFO Rendering menu. 2025-01-16 14:19:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:19:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:19:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:19:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-16 14:19:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-16 14:19:59 INFO Connected to the database Insightlab. 2025-01-16 14:19:59 INFO Connected to the database Insightlab. 2025-01-16 14:19:59 INFO Query executed successfully. 2025-01-16 14:19:59 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:19:59 INFO Dataset columns displayed using AG Grid. 2025-01-16 14:19:59 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.9`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:19:59 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose risk score is above 0.9`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-16 14:20:03 INFO Tokens consumed: 970 2025-01-16 14:20:04 INFO Existing token_consumed found for month: 2025-01 2025-01-16 14:20:04 INFO Existing token_consumed found for month: 2025-01 2025-01-16 14:20:05 INFO token updated successfully: 2025-01 2025-01-16 14:20:05 INFO token updated successfully: 2025-01 2025-01-16 14:20:05 INFO token updated successfully. 2025-01-16 14:20:05 INFO token updated successfully. 2025-01-16 14:20:06 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 2 2025-01-16 14:20:08 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:20:08 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-16 14:20:09 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/3.py 2025-01-16 14:20:09 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/3.py 2025-01-16 14:20:09 INFO Code generated and written in generated_method//2.py 2025-01-16 14:20:09 INFO Code generated and written in generated_method//2.py 2025-01-16 14:20:09 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:20:09 INFO Insight generated and displayed using AG Grid. 2025-01-16 14:23:37 INFO Rendering menu. 2025-01-16 14:23:37 INFO Rendering menu. 2025-01-16 14:23:38 INFO User logged out. 2025-01-16 14:23:38 INFO User logged out. 2025-01-16 14:23:38 INFO Date: 2025-01-16 ======================================== Time: 14:23:38 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:23:38 INFO Date: 2025-01-16 ======================================== Time: 14:23:38 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:23:38 INFO Date: 2025-01-16 ======================================== Time: 14:23:38 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 14:23:38 INFO Rendering menu. 2025-01-16 14:23:38 INFO Rendering menu. 2025-01-16 14:23:38 INFO Rendering menu. 2025-01-16 14:23:38 INFO Rendering unauthenticated menu. 2025-01-16 14:23:38 INFO Rendering unauthenticated menu. 2025-01-16 14:23:38 INFO Rendering unauthenticated menu. 2025-01-16 14:23:43 INFO Rendering menu. 2025-01-16 14:23:43 INFO Rendering menu. 2025-01-16 14:23:43 INFO Rendering menu. 2025-01-16 14:23:43 INFO Login button clicked. 2025-01-16 14:23:43 INFO Login button clicked. 2025-01-16 14:23:43 INFO Login button clicked. 2025-01-16 14:23:47 INFO Login successful for user: nanthinisri.l 2025-01-16 14:23:47 INFO Login successful for user: nanthinisri.l 2025-01-16 14:23:47 INFO Login successful for user: nanthinisri.l 2025-01-16 14:23:47 INFO Rendering menu. 2025-01-16 14:23:47 INFO Rendering menu. 2025-01-16 14:23:47 INFO Rendering menu. 2025-01-16 14:24:29 INFO Rendering menu. 2025-01-16 14:24:29 INFO Rendering menu. 2025-01-16 14:24:29 INFO Rendering menu. 2025-01-16 14:25:33 INFO Rendering menu. 2025-01-16 14:25:33 INFO Rendering menu. 2025-01-16 14:25:33 INFO Rendering menu. 2025-01-16 14:25:34 INFO Rendering menu. 2025-01-16 14:25:34 INFO Rendering menu. 2025-01-16 14:25:34 INFO Rendering menu. 2025-01-16 14:25:36 INFO Rendering menu. 2025-01-16 14:25:36 INFO Rendering menu. 2025-01-16 14:25:36 INFO Rendering menu. 2025-01-16 14:25:36 INFO Database names fetched successfully. 2025-01-16 14:25:36 INFO Database names fetched successfully. 2025-01-16 14:25:36 INFO Database names fetched successfully. 2025-01-16 14:25:37 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 14:25:37 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 14:25:37 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-16 14:25:38 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 14:25:38 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 14:25:38 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-16 17:50:04 INFO Date: 2025-01-16 ======================================== Time: 17:50:04 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 17:50:04 INFO Rendering menu. 2025-01-16 17:50:11 INFO Rendering unauthenticated menu. 2025-01-16 17:51:39 INFO Rendering menu. 2025-01-16 17:51:39 INFO Login button clicked. 2025-01-16 17:51:43 INFO Login successful for user: abhishek 2025-01-16 17:51:43 INFO Rendering menu. 2025-01-16 17:52:57 INFO Rendering menu. 2025-01-16 17:53:01 INFO Database names fetched successfully. 2025-01-16 17:54:03 INFO Rendering menu. 2025-01-16 17:54:04 INFO Database names fetched successfully. 2025-01-16 17:54:04 INFO Table details fetched successfully. 2025-01-16 17:54:20 INFO Rendering menu. 2025-01-16 17:54:20 INFO Database names fetched successfully. 2025-01-16 17:54:20 INFO Table details fetched successfully. 2025-01-16 17:54:30 INFO Rendering menu. 2025-01-16 17:54:30 INFO Database names fetched successfully. 2025-01-16 17:54:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 17:54:32 INFO Tokens consumed: 2970 2025-01-16 17:54:34 INFO Existing token_consumed found for month: 2025-01 2025-01-16 17:54:35 INFO token updated successfully: 2025-01 2025-01-16 17:54:35 INFO token updated successfully. 2025-01-16 17:54:35 ERROR Error while executing generated query: %s 2025-01-16 17:54:35 ERROR Error processing request: 'NoneType' object has no attribute 'columns' 2025-01-16 17:57:10 INFO Rendering menu. 2025-01-16 17:57:11 INFO Database names fetched successfully. 2025-01-16 17:57:11 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 17:57:13 INFO Tokens consumed: 2970 2025-01-16 17:57:15 INFO Existing token_consumed found for month: 2025-01 2025-01-16 17:57:15 INFO token updated successfully: 2025-01 2025-01-16 17:57:15 INFO token updated successfully. 2025-01-16 17:57:15 ERROR Error while executing generated query: %s 2025-01-16 17:57:15 ERROR Error processing request: 'NoneType' object has no attribute 'columns' 2025-01-16 17:58:03 INFO Rendering menu. 2025-01-16 17:58:04 INFO Database names fetched successfully. 2025-01-16 17:58:04 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmentss```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 17:58:06 INFO Tokens consumed: 2970 2025-01-16 17:58:07 INFO Existing token_consumed found for month: 2025-01 2025-01-16 17:58:09 INFO token updated successfully: 2025-01 2025-01-16 17:58:09 INFO token updated successfully. 2025-01-16 17:58:09 ERROR Error while executing generated query: %s 2025-01-16 17:58:09 ERROR Error processing request: 'NoneType' object has no attribute 'columns' 2025-01-16 18:02:40 INFO Date: 2025-01-16 ======================================== Time: 18:02:40 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:02:40 INFO Rendering menu. 2025-01-16 18:02:44 INFO Rendering unauthenticated menu. 2025-01-16 18:03:22 INFO Rendering menu. 2025-01-16 18:03:22 INFO Login button clicked. 2025-01-16 18:03:26 INFO Login successful for user: abhishek 2025-01-16 18:03:26 INFO Rendering menu. 2025-01-16 18:03:49 INFO Rendering menu. 2025-01-16 18:03:49 INFO Database names fetched successfully. 2025-01-16 18:04:35 INFO Rendering menu. 2025-01-16 18:04:35 INFO Database names fetched successfully. 2025-01-16 18:04:36 INFO Table details fetched successfully. 2025-01-16 18:06:25 INFO Rendering menu. 2025-01-16 18:06:25 INFO Database names fetched successfully. 2025-01-16 18:06:25 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 18:06:28 INFO Tokens consumed: 2970 2025-01-16 18:06:29 INFO Existing token_consumed found for month: 2025-01 2025-01-16 18:06:30 INFO token updated successfully: 2025-01 2025-01-16 18:06:30 INFO token updated successfully. 2025-01-16 18:06:30 INFO Connected to the database MHealth_Dev. 2025-01-16 18:06:30 INFO Query executed successfully. 2025-01-16 18:06:30 INFO Connected to the database MHealth_Dev. 2025-01-16 18:06:30 INFO Query executed successfully. 2025-01-16 18:06:30 INFO Rendering menu. 2025-01-16 18:06:30 INFO Database names fetched successfully. 2025-01-16 18:13:58 INFO Date: 2025-01-16 ======================================== Time: 18:13:58 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:13:58 INFO Rendering menu. 2025-01-16 18:14:03 INFO Rendering unauthenticated menu. 2025-01-16 18:16:11 INFO Rendering menu. 2025-01-16 18:16:11 INFO Login button clicked. 2025-01-16 18:16:15 INFO Login successful for user: abhishek 2025-01-16 18:16:15 INFO Rendering menu. 2025-01-16 18:16:43 INFO Rendering menu. 2025-01-16 18:16:45 INFO Database names fetched successfully. 2025-01-16 18:17:19 INFO Rendering menu. 2025-01-16 18:17:19 INFO Database names fetched successfully. 2025-01-16 18:17:20 INFO Table details fetched successfully. 2025-01-16 18:17:35 INFO Rendering menu. 2025-01-16 18:17:35 INFO Database names fetched successfully. 2025-01-16 18:17:35 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 18:17:38 INFO Tokens consumed: 2970 2025-01-16 18:17:39 INFO Existing token_consumed found for month: 2025-01 2025-01-16 18:17:40 INFO token updated successfully: 2025-01 2025-01-16 18:17:40 INFO token updated successfully. 2025-01-16 18:17:40 INFO Connected to the database MHealth_Dev. 2025-01-16 18:17:40 INFO Query executed successfully. 2025-01-16 18:17:40 INFO Connected to the database MHealth_Dev. 2025-01-16 18:17:40 INFO Query executed successfully. 2025-01-16 18:17:40 INFO Connected to the database MHealth_Dev. 2025-01-16 18:17:40 INFO Query executed successfully. 2025-01-16 18:17:41 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 18:18:06 INFO Rendering menu. 2025-01-16 18:18:06 INFO Database names fetched successfully. 2025-01-16 18:41:59 INFO Rendering menu. 2025-01-16 18:42:01 INFO Database names fetched successfully. 2025-01-16 18:42:01 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 18:42:04 INFO Tokens consumed: 2970 2025-01-16 18:42:05 INFO Existing token_consumed found for month: 2025-01 2025-01-16 18:42:06 INFO token updated successfully: 2025-01 2025-01-16 18:42:06 INFO token updated successfully. 2025-01-16 18:42:06 INFO Connected to the database MHealth_Dev. 2025-01-16 18:42:06 INFO Query executed successfully. 2025-01-16 18:42:06 INFO Connected to the database MHealth_Dev. 2025-01-16 18:42:06 INFO Query executed successfully. 2025-01-16 18:42:06 INFO Connected to the database MHealth_Dev. 2025-01-16 18:42:06 INFO Query executed successfully. 2025-01-16 18:42:06 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 18:43:10 INFO Rendering menu. 2025-01-16 18:43:10 INFO Database names fetched successfully. 2025-01-16 18:50:15 INFO Rendering menu. 2025-01-16 18:50:15 INFO User logged out. 2025-01-16 18:50:15 INFO Date: 2025-01-16 ======================================== Time: 18:50:15 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:50:15 INFO Date: 2025-01-16 ======================================== Time: 18:50:15 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:50:15 INFO Rendering menu. 2025-01-16 18:50:15 INFO Rendering menu. 2025-01-16 18:50:15 INFO Rendering unauthenticated menu. 2025-01-16 18:50:15 INFO Rendering unauthenticated menu. 2025-01-16 18:50:21 INFO Date: 2025-01-16 ======================================== Time: 18:50:21 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:50:21 INFO Date: 2025-01-16 ======================================== Time: 18:50:21 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:50:21 INFO Date: 2025-01-16 ======================================== Time: 18:50:21 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 18:50:21 INFO Rendering menu. 2025-01-16 18:50:21 INFO Rendering menu. 2025-01-16 18:50:21 INFO Rendering menu. 2025-01-16 18:50:21 INFO Rendering unauthenticated menu. 2025-01-16 18:50:21 INFO Rendering unauthenticated menu. 2025-01-16 18:50:21 INFO Rendering unauthenticated menu. 2025-01-16 18:51:42 INFO Rendering menu. 2025-01-16 18:51:42 INFO Rendering menu. 2025-01-16 18:51:42 INFO Rendering menu. 2025-01-16 18:51:42 INFO Login button clicked. 2025-01-16 18:51:42 INFO Login button clicked. 2025-01-16 18:51:42 INFO Login button clicked. 2025-01-16 18:51:46 INFO Login successful for user: abhishek 2025-01-16 18:51:46 INFO Login successful for user: abhishek 2025-01-16 18:51:46 INFO Login successful for user: abhishek 2025-01-16 18:51:46 INFO Rendering menu. 2025-01-16 18:51:46 INFO Rendering menu. 2025-01-16 18:51:46 INFO Rendering menu. 2025-01-16 18:54:30 INFO Rendering menu. 2025-01-16 18:54:30 INFO Rendering menu. 2025-01-16 18:54:30 INFO Rendering menu. 2025-01-16 18:54:32 INFO Database names fetched successfully. 2025-01-16 18:54:32 INFO Database names fetched successfully. 2025-01-16 18:54:32 INFO Database names fetched successfully. 2025-01-16 19:28:54 INFO Rendering menu. 2025-01-16 19:28:54 INFO Rendering menu. 2025-01-16 19:28:54 INFO Rendering menu. 2025-01-16 19:28:55 INFO Database names fetched successfully. 2025-01-16 19:28:55 INFO Database names fetched successfully. 2025-01-16 19:28:55 INFO Database names fetched successfully. 2025-01-16 19:28:56 INFO Table details fetched successfully. 2025-01-16 19:28:56 INFO Table details fetched successfully. 2025-01-16 19:28:56 INFO Table details fetched successfully. 2025-01-16 19:29:25 INFO Rendering menu. 2025-01-16 19:29:25 INFO Rendering menu. 2025-01-16 19:29:25 INFO Rendering menu. 2025-01-16 19:29:26 INFO Database names fetched successfully. 2025-01-16 19:29:26 INFO Database names fetched successfully. 2025-01-16 19:29:26 INFO Database names fetched successfully. 2025-01-16 19:29:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 19:29:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 19:29:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 19:29:29 INFO Tokens consumed: 2970 2025-01-16 19:29:29 INFO Tokens consumed: 2970 2025-01-16 19:29:31 INFO Existing token_consumed found for month: 2025-01 2025-01-16 19:29:31 INFO Existing token_consumed found for month: 2025-01 2025-01-16 19:29:32 INFO token updated successfully: 2025-01 2025-01-16 19:29:32 INFO token updated successfully: 2025-01 2025-01-16 19:29:32 INFO token updated successfully: 2025-01 2025-01-16 19:29:32 INFO token updated successfully. 2025-01-16 19:29:32 INFO token updated successfully. 2025-01-16 19:29:32 INFO token updated successfully. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Connected to the database MHealth_Dev. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 INFO Query executed successfully. 2025-01-16 19:29:32 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 19:29:32 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 19:29:32 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 19:33:02 INFO Date: 2025-01-16 ======================================== Time: 19:33:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 19:33:02 INFO Rendering menu. 2025-01-16 19:33:08 INFO Rendering unauthenticated menu. 2025-01-16 19:37:06 INFO Rendering menu. 2025-01-16 19:37:06 INFO Login button clicked. 2025-01-16 19:37:11 INFO Login successful for user: abhishek 2025-01-16 19:37:11 INFO Rendering menu. 2025-01-16 19:41:30 INFO Rendering menu. 2025-01-16 19:41:33 INFO Database names fetched successfully. 2025-01-16 19:41:50 INFO Rendering menu. 2025-01-16 19:41:50 INFO Database names fetched successfully. 2025-01-16 19:41:51 INFO Table details fetched successfully. 2025-01-16 19:42:39 INFO Rendering menu. 2025-01-16 19:42:39 INFO Database names fetched successfully. 2025-01-16 19:42:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 19:42:41 INFO Tokens consumed: 2970 2025-01-16 19:42:43 INFO Existing token_consumed found for month: 2025-01 2025-01-16 19:42:44 INFO token updated successfully: 2025-01 2025-01-16 19:42:44 INFO token updated successfully. 2025-01-16 19:42:44 INFO Connected to the database MHealth_Dev. 2025-01-16 19:42:44 INFO Query executed successfully. 2025-01-16 19:42:45 INFO Connected to the database MHealth_Dev. 2025-01-16 19:42:45 INFO Query executed successfully. 2025-01-16 19:42:45 INFO Connected to the database MHealth_Dev. 2025-01-16 19:42:45 INFO Query executed successfully. 2025-01-16 19:42:45 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-16 19:56:10 INFO Date: 2025-01-16 ======================================== Time: 19:56:10 Logger Data: This is some log data. ---------------------------------------- 2025-01-16 19:56:10 INFO Rendering menu. 2025-01-16 19:56:14 INFO Rendering unauthenticated menu. 2025-01-16 19:58:50 INFO Rendering menu. 2025-01-16 19:58:50 INFO Login button clicked. 2025-01-16 19:58:54 INFO Login successful for user: abhishek 2025-01-16 19:58:54 INFO Rendering menu. 2025-01-16 19:59:08 INFO Rendering menu. 2025-01-16 19:59:26 INFO Rendering menu. 2025-01-16 19:59:28 INFO Database names fetched successfully. 2025-01-16 20:00:27 INFO Rendering menu. 2025-01-16 20:00:27 INFO Database names fetched successfully. 2025-01-16 20:00:27 INFO Table details fetched successfully. 2025-01-16 20:01:19 INFO Rendering menu. 2025-01-16 20:01:19 INFO Database names fetched successfully. 2025-01-16 20:01:19 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-16 20:01:22 INFO Tokens consumed: 2970 2025-01-16 20:01:24 INFO Existing token_consumed found for month: 2025-01 2025-01-16 20:01:25 INFO token updated successfully: 2025-01 2025-01-16 20:01:25 INFO token updated successfully. 2025-01-16 20:01:25 INFO Connected to the database MHealth_Dev. 2025-01-16 20:01:25 INFO Query executed successfully. 2025-01-16 20:01:25 INFO Connected to the database MHealth_Dev. 2025-01-16 20:01:25 INFO Query executed successfully. 2025-01-16 20:01:25 ERROR Error processing request: cannot access local variable 'grid_options' where it is not associated with a value 2025-01-17 10:49:15 INFO Date: 2025-01-17 ======================================== Time: 10:49:15 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 10:49:15 INFO Rendering menu. 2025-01-17 10:49:19 INFO Rendering unauthenticated menu. 2025-01-17 10:50:52 INFO Rendering menu. 2025-01-17 10:50:52 INFO Login button clicked. 2025-01-17 10:50:56 INFO Login successful for user: maheshsr 2025-01-17 10:50:56 INFO Rendering menu. 2025-01-17 10:51:10 INFO Rendering menu. 2025-01-17 10:51:12 INFO Database names fetched successfully. 2025-01-17 10:51:35 INFO Rendering menu. 2025-01-17 10:51:35 INFO Database names fetched successfully. 2025-01-17 10:51:36 INFO Table details fetched successfully. 2025-01-17 10:53:43 INFO Rendering menu. 2025-01-17 10:53:43 INFO Database names fetched successfully. 2025-01-17 10:53:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 10:53:47 INFO Tokens consumed: 2970 2025-01-17 10:53:49 INFO Existing token_consumed found for month: 2025-01 2025-01-17 10:53:51 INFO token updated successfully: 2025-01 2025-01-17 10:53:51 INFO token updated successfully. 2025-01-17 10:53:51 INFO Connected to the database MHealth_Dev. 2025-01-17 10:53:51 INFO Query executed successfully. 2025-01-17 10:53:51 INFO Connected to the database MHealth_Dev. 2025-01-17 10:53:51 INFO Query executed successfully. 2025-01-17 10:53:51 INFO Connected to the database MHealth_Dev. 2025-01-17 10:53:51 INFO Query executed successfully. 2025-01-17 10:53:51 INFO Connected to the database MHealth_Dev. 2025-01-17 10:53:51 INFO Query executed successfully. 2025-01-17 10:53:51 ERROR Error processing request: There are multiple `button` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `button` element. 2025-01-17 10:55:18 INFO Rendering menu. 2025-01-17 10:55:19 INFO Database names fetched successfully. 2025-01-17 11:15:46 INFO Date: 2025-01-17 ======================================== Time: 11:15:46 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 11:15:46 INFO Rendering menu. 2025-01-17 11:15:50 INFO Rendering unauthenticated menu. 2025-01-17 11:16:28 INFO Rendering menu. 2025-01-17 11:16:28 INFO Login button clicked. 2025-01-17 11:16:31 INFO Login successful for user: maheshsr 2025-01-17 11:16:31 INFO Rendering menu. 2025-01-17 11:17:41 INFO Rendering menu. 2025-01-17 11:17:43 INFO Database names fetched successfully. 2025-01-17 11:17:58 INFO Rendering menu. 2025-01-17 11:17:58 INFO Database names fetched successfully. 2025-01-17 11:17:59 INFO Table details fetched successfully. 2025-01-17 11:18:25 INFO Rendering menu. 2025-01-17 11:18:25 INFO Database names fetched successfully. 2025-01-17 11:18:25 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:18:27 INFO Tokens consumed: 2970 2025-01-17 11:18:28 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:18:29 INFO token updated successfully: 2025-01 2025-01-17 11:18:29 INFO token updated successfully. 2025-01-17 11:18:29 INFO Connected to the database MHealth_Dev. 2025-01-17 11:18:29 INFO Query executed successfully. 2025-01-17 11:18:29 INFO Connected to the database MHealth_Dev. 2025-01-17 11:18:29 INFO Query executed successfully. 2025-01-17 11:18:29 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-17 11:25:57 INFO Date: 2025-01-17 ======================================== Time: 11:25:57 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 11:25:57 INFO Rendering menu. 2025-01-17 11:26:02 INFO Rendering unauthenticated menu. 2025-01-17 11:26:31 INFO Rendering menu. 2025-01-17 11:26:31 INFO Login button clicked. 2025-01-17 11:26:35 INFO Login successful for user: maheshsr 2025-01-17 11:26:35 INFO Rendering menu. 2025-01-17 11:26:52 INFO Rendering menu. 2025-01-17 11:26:54 INFO Database names fetched successfully. 2025-01-17 11:27:11 INFO Rendering menu. 2025-01-17 11:27:11 INFO Database names fetched successfully. 2025-01-17 11:27:12 INFO Table details fetched successfully. 2025-01-17 11:27:48 INFO Rendering menu. 2025-01-17 11:27:48 INFO Database names fetched successfully. 2025-01-17 11:27:48 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:27:51 INFO Tokens consumed: 2970 2025-01-17 11:27:52 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:27:53 INFO token updated successfully: 2025-01 2025-01-17 11:27:53 INFO token updated successfully. 2025-01-17 11:27:53 INFO Connected to the database MHealth_Dev. 2025-01-17 11:27:53 INFO Query executed successfully. 2025-01-17 11:27:53 INFO Connected to the database MHealth_Dev. 2025-01-17 11:27:53 INFO Query executed successfully. 2025-01-17 11:27:53 ERROR Error processing request: module 'streamlit.components.v1' has no attribute 'components' 2025-01-17 11:34:48 INFO Date: 2025-01-17 ======================================== Time: 11:34:48 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 11:34:48 INFO Rendering menu. 2025-01-17 11:34:53 INFO Rendering unauthenticated menu. 2025-01-17 11:35:26 INFO Rendering menu. 2025-01-17 11:35:26 INFO Login button clicked. 2025-01-17 11:35:29 INFO Login successful for user: nanthinisri.l 2025-01-17 11:35:29 INFO Rendering menu. 2025-01-17 11:35:56 INFO Rendering menu. 2025-01-17 11:37:06 INFO Rendering menu. 2025-01-17 11:37:08 INFO Database names fetched successfully. 2025-01-17 11:37:14 INFO Rendering menu. 2025-01-17 11:37:14 INFO Database names fetched successfully. 2025-01-17 11:37:14 INFO Table details fetched successfully. 2025-01-17 11:37:41 INFO Rendering menu. 2025-01-17 11:37:42 INFO Database names fetched successfully. 2025-01-17 11:37:42 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:37:44 INFO Tokens consumed: 2970 2025-01-17 11:37:45 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:37:46 INFO token updated successfully: 2025-01 2025-01-17 11:37:46 INFO token updated successfully. 2025-01-17 11:37:46 INFO Connected to the database MHealth_Dev. 2025-01-17 11:37:46 INFO Query executed successfully. 2025-01-17 11:37:47 INFO Latest file number in generated_sql/c4686458-10a1-7096-10be-c5966f270129/: 6 2025-01-17 11:37:49 INFO Blob exists check for generated_sql/c4686458-10a1-7096-10be-c5966f270129/: True 2025-01-17 11:37:49 INFO SQL query blob saved successfully: generated_sql/c4686458-10a1-7096-10be-c5966f270129/7.json 2025-01-17 11:40:49 INFO Rendering menu. 2025-01-17 11:40:49 INFO Database names fetched successfully. 2025-01-17 11:40:49 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:40:51 INFO Tokens consumed: 2970 2025-01-17 11:40:53 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:40:54 INFO token updated successfully: 2025-01 2025-01-17 11:40:54 INFO token updated successfully. 2025-01-17 11:40:54 INFO Connected to the database MHealth_Dev. 2025-01-17 11:40:54 INFO Query executed successfully. 2025-01-17 11:40:55 INFO Latest file number in generated_sql/c4686458-10a1-7096-10be-c5966f270129/: 7 2025-01-17 11:40:56 INFO Blob exists check for generated_sql/c4686458-10a1-7096-10be-c5966f270129/: True 2025-01-17 11:40:57 INFO SQL query blob saved successfully: generated_sql/c4686458-10a1-7096-10be-c5966f270129/8.json 2025-01-17 11:41:02 INFO Date: 2025-01-17 ======================================== Time: 11:41:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 11:41:02 INFO Date: 2025-01-17 ======================================== Time: 11:41:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 11:41:02 INFO Rendering menu. 2025-01-17 11:41:02 INFO Rendering menu. 2025-01-17 11:41:02 INFO Rendering unauthenticated menu. 2025-01-17 11:41:02 INFO Rendering unauthenticated menu. 2025-01-17 11:41:26 INFO Rendering menu. 2025-01-17 11:41:26 INFO Rendering menu. 2025-01-17 11:41:26 INFO Login button clicked. 2025-01-17 11:41:26 INFO Login button clicked. 2025-01-17 11:41:30 INFO Login successful for user: maheshsr 2025-01-17 11:41:30 INFO Login successful for user: maheshsr 2025-01-17 11:41:30 INFO Rendering menu. 2025-01-17 11:41:30 INFO Rendering menu. 2025-01-17 11:41:34 INFO Rendering menu. 2025-01-17 11:41:34 INFO Rendering menu. 2025-01-17 11:41:35 INFO Database names fetched successfully. 2025-01-17 11:41:35 INFO Database names fetched successfully. 2025-01-17 11:41:37 INFO Rendering menu. 2025-01-17 11:41:37 INFO Rendering menu. 2025-01-17 11:41:38 INFO Database names fetched successfully. 2025-01-17 11:41:38 INFO Table details fetched successfully. 2025-01-17 11:41:38 INFO Table details fetched successfully. 2025-01-17 11:41:43 INFO Rendering menu. 2025-01-17 11:41:43 INFO Rendering menu. 2025-01-17 11:41:43 INFO Database names fetched successfully. 2025-01-17 11:41:43 INFO Database names fetched successfully. 2025-01-17 11:41:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:41:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 11:41:46 INFO Tokens consumed: 2970 2025-01-17 11:41:46 INFO Tokens consumed: 2970 2025-01-17 11:41:47 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:41:47 INFO Existing token_consumed found for month: 2025-01 2025-01-17 11:41:48 INFO token updated successfully: 2025-01 2025-01-17 11:41:48 INFO token updated successfully: 2025-01 2025-01-17 11:41:48 INFO token updated successfully. 2025-01-17 11:41:48 INFO Connected to the database MHealth_Dev. 2025-01-17 11:41:48 INFO Query executed successfully. 2025-01-17 11:41:48 INFO Query executed successfully. 2025-01-17 11:41:50 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 86 2025-01-17 11:41:50 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 86 2025-01-17 11:41:51 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-17 11:41:51 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-17 11:41:52 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/87.json 2025-01-17 11:41:52 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/87.json 2025-01-17 14:03:06 INFO Date: 2025-01-17 ======================================== Time: 14:03:06 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 14:03:06 INFO Rendering menu. 2025-01-17 14:03:11 INFO Rendering unauthenticated menu. 2025-01-17 14:09:52 INFO Rendering menu. 2025-01-17 14:09:52 INFO Login button clicked. 2025-01-17 14:09:56 INFO Login successful for user: maheshsr 2025-01-17 14:09:56 INFO Rendering menu. 2025-01-17 14:10:24 INFO Rendering menu. 2025-01-17 14:10:26 INFO Database names fetched successfully. 2025-01-17 14:10:42 INFO Rendering menu. 2025-01-17 14:10:42 INFO Database names fetched successfully. 2025-01-17 14:10:43 INFO Table details fetched successfully. 2025-01-17 14:16:43 INFO Rendering menu. 2025-01-17 14:16:43 INFO Database names fetched successfully. 2025-01-17 14:16:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 14:16:45 INFO Tokens consumed: 2970 2025-01-17 14:16:46 INFO Existing token_consumed found for month: 2025-01 2025-01-17 14:16:48 INFO token updated successfully: 2025-01 2025-01-17 14:16:48 INFO token updated successfully. 2025-01-17 14:16:48 INFO Connected to the database MHealth_Dev. 2025-01-17 14:16:48 INFO Query executed successfully. 2025-01-17 14:16:48 ERROR Error processing request: 'dict' object has no attribute 'columns' 2025-01-17 14:17:14 INFO Rendering menu. 2025-01-17 14:17:14 INFO Database names fetched successfully. 2025-01-17 14:44:42 INFO Date: 2025-01-17 ======================================== Time: 14:44:42 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 14:44:42 INFO Rendering menu. 2025-01-17 14:44:47 INFO Rendering unauthenticated menu. 2025-01-17 14:50:31 INFO Rendering menu. 2025-01-17 14:50:31 INFO Login button clicked. 2025-01-17 14:50:35 INFO Login successful for user: maheshsr 2025-01-17 14:50:35 INFO Rendering menu. 2025-01-17 14:52:00 INFO Rendering menu. 2025-01-17 14:52:02 INFO Database names fetched successfully. 2025-01-17 14:52:19 INFO Rendering menu. 2025-01-17 14:52:19 INFO Database names fetched successfully. 2025-01-17 14:52:20 INFO Table details fetched successfully. 2025-01-17 15:31:36 INFO Date: 2025-01-17 ======================================== Time: 15:31:36 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 15:31:36 INFO Date: 2025-01-17 ======================================== Time: 15:31:36 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 15:31:36 INFO Rendering menu. 2025-01-17 15:31:36 INFO Rendering menu. 2025-01-17 15:31:36 INFO Rendering unauthenticated menu. 2025-01-17 15:31:36 INFO Rendering unauthenticated menu. 2025-01-17 15:31:58 INFO Rendering menu. 2025-01-17 15:31:58 INFO Rendering menu. 2025-01-17 15:31:58 INFO Login button clicked. 2025-01-17 15:31:58 INFO Login button clicked. 2025-01-17 15:32:02 INFO Login successful for user: maheshsr 2025-01-17 15:32:02 INFO Login successful for user: maheshsr 2025-01-17 15:32:02 INFO Rendering menu. 2025-01-17 15:32:02 INFO Rendering menu. 2025-01-17 15:32:37 INFO Rendering menu. 2025-01-17 15:32:37 INFO Rendering menu. 2025-01-17 15:32:38 INFO Database names fetched successfully. 2025-01-17 15:32:38 INFO Database names fetched successfully. 2025-01-17 15:32:41 INFO Rendering menu. 2025-01-17 15:32:41 INFO Rendering menu. 2025-01-17 15:32:41 INFO Database names fetched successfully. 2025-01-17 15:32:41 INFO Database names fetched successfully. 2025-01-17 15:32:41 INFO Table details fetched successfully. 2025-01-17 15:32:41 INFO Table details fetched successfully. 2025-01-17 15:32:49 INFO Rendering menu. 2025-01-17 15:32:49 INFO Rendering menu. 2025-01-17 15:32:50 INFO Database names fetched successfully. 2025-01-17 15:32:50 INFO Database names fetched successfully. 2025-01-17 15:32:50 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 15:32:50 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 15:33:07 INFO Tokens consumed: 2970 2025-01-17 15:33:07 INFO Tokens consumed: 2970 2025-01-17 15:33:08 INFO Existing token_consumed found for month: 2025-01 2025-01-17 15:33:08 INFO Existing token_consumed found for month: 2025-01 2025-01-17 15:33:09 INFO token updated successfully: 2025-01 2025-01-17 15:33:09 INFO token updated successfully: 2025-01 2025-01-17 15:33:09 INFO token updated successfully. 2025-01-17 15:33:09 INFO token updated successfully. 2025-01-17 15:33:09 INFO Connected to the database MHealth_Dev. 2025-01-17 15:33:09 INFO Connected to the database MHealth_Dev. 2025-01-17 15:33:09 INFO Query executed successfully. 2025-01-17 15:33:09 INFO Query executed successfully. 2025-01-17 15:33:09 INFO Connected to the database MHealth_Dev. 2025-01-17 15:33:09 INFO Connected to the database MHealth_Dev. 2025-01-17 15:33:09 INFO Query executed successfully. 2025-01-17 15:33:09 ERROR Error processing request: Cannot set a DataFrame with multiple columns to the single column 0 2025-01-17 15:33:09 ERROR Error processing request: Cannot set a DataFrame with multiple columns to the single column 0 2025-01-17 15:38:22 INFO Date: 2025-01-17 ======================================== Time: 15:38:22 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 15:38:22 INFO Rendering menu. 2025-01-17 15:38:27 INFO Rendering unauthenticated menu. 2025-01-17 15:39:20 INFO Rendering menu. 2025-01-17 15:39:20 INFO Login button clicked. 2025-01-17 15:39:24 INFO Login successful for user: maheshsr 2025-01-17 15:39:24 INFO Rendering menu. 2025-01-17 15:41:15 INFO Rendering menu. 2025-01-17 15:41:17 INFO Database names fetched successfully. 2025-01-17 15:41:42 INFO Rendering menu. 2025-01-17 15:41:42 INFO Database names fetched successfully. 2025-01-17 15:41:43 INFO Table details fetched successfully. 2025-01-17 15:42:07 INFO Rendering menu. 2025-01-17 15:42:07 INFO Database names fetched successfully. 2025-01-17 15:42:08 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 15:42:10 INFO Tokens consumed: 2970 2025-01-17 15:42:12 INFO Existing token_consumed found for month: 2025-01 2025-01-17 15:42:13 INFO token updated successfully: 2025-01 2025-01-17 15:42:13 INFO token updated successfully. 2025-01-17 15:42:13 INFO Connected to the database MHealth_Dev. 2025-01-17 15:42:13 INFO Query executed successfully. 2025-01-17 15:42:15 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 87 2025-01-17 15:42:17 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-17 15:42:18 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/88.json 2025-01-17 15:44:19 INFO Rendering menu. 2025-01-17 15:44:19 INFO Database names fetched successfully. 2025-01-17 15:44:19 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-17 15:44:22 INFO Tokens consumed: 2969 2025-01-17 15:44:23 INFO Existing token_consumed found for month: 2025-01 2025-01-17 15:44:24 INFO token updated successfully: 2025-01 2025-01-17 15:44:24 INFO token updated successfully. 2025-01-17 15:44:24 INFO Connected to the database MHealth_Dev. 2025-01-17 15:44:24 INFO Query executed successfully. 2025-01-17 15:44:26 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 88 2025-01-17 15:44:28 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-17 15:44:29 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/89.json 2025-01-17 16:07:10 INFO Rendering menu. 2025-01-17 16:07:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-17 16:07:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-17 16:07:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 16:07:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-17 16:07:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-17 16:07:16 INFO Rendering menu. 2025-01-17 16:07:17 INFO Rendering menu. 2025-01-17 16:07:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-17 16:07:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-17 16:07:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 16:07:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-17 16:07:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-17 16:07:24 INFO Rendering menu. 2025-01-17 16:07:30 INFO Rendering menu. 2025-01-17 16:07:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 16:07:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-17 16:07:34 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-17 16:07:35 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-17 16:07:35 INFO Insight list generated successfully. 2025-01-17 16:07:39 INFO Rendering menu. 2025-01-17 16:07:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 16:07:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-17 16:07:43 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-17 16:07:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-17 16:07:44 INFO Insight list generated successfully. 2025-01-17 16:07:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 16:07:45 INFO Connected to the database Insightlab. 2025-01-17 16:07:45 INFO Query executed successfully. 2025-01-17 16:07:45 ERROR Error executing generated insight code: AttributeError("module 'streamlit.components.v1' has no attribute 'components'") 2025-01-17 16:07:49 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-17 16:59:13 INFO Date: 2025-01-17 ======================================== Time: 16:59:13 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 16:59:13 INFO Rendering menu. 2025-01-17 16:59:17 INFO Rendering unauthenticated menu. 2025-01-17 17:01:14 INFO Rendering menu. 2025-01-17 17:01:14 INFO Login button clicked. 2025-01-17 17:01:18 INFO Login successful for user: maheshsr 2025-01-17 17:01:18 INFO Rendering menu. 2025-01-17 17:01:33 INFO Rendering menu. 2025-01-17 17:01:33 INFO Database names fetched successfully. 2025-01-17 17:02:12 INFO Rendering menu. 2025-01-17 17:02:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-17 17:02:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-17 17:02:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-17 17:02:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-17 17:02:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-17 17:02:34 INFO Rendering menu. 2025-01-17 17:02:34 INFO User logged out. 2025-01-17 17:02:35 INFO Date: 2025-01-17 ======================================== Time: 17:02:35 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:02:35 INFO Rendering menu. 2025-01-17 17:02:35 INFO Rendering menu. 2025-01-17 17:02:35 INFO Rendering unauthenticated menu. 2025-01-17 17:02:35 INFO Rendering unauthenticated menu. 2025-01-17 17:02:39 INFO Rendering menu. 2025-01-17 17:02:39 INFO Login button clicked. 2025-01-17 17:02:39 INFO Login button clicked. 2025-01-17 17:02:43 INFO Login successful for user: nanthinisri.l 2025-01-17 17:02:43 INFO Login successful for user: nanthinisri.l 2025-01-17 17:02:43 INFO Rendering menu. 2025-01-17 17:02:51 INFO Rendering menu. 2025-01-17 17:02:51 INFO Rendering menu. 2025-01-17 17:03:27 INFO Rendering menu. 2025-01-17 17:03:27 INFO Rendering menu. 2025-01-17 17:03:29 INFO Rendering menu. 2025-01-17 17:03:32 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-17 17:03:32 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/6.json 2025-01-17 17:03:32 INFO Blob content retrieved successfully from: query_library/c4686458-10a1-7096-10be-c5966f270129/2.json 2025-01-17 17:03:33 INFO Rendering menu. 2025-01-17 17:03:33 INFO Rendering menu. 2025-01-17 17:03:33 INFO Database names fetched successfully. 2025-01-17 17:21:52 INFO Date: 2025-01-17 ======================================== Time: 17:21:52 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:21:52 INFO Date: 2025-01-17 ======================================== Time: 17:21:52 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:21:52 INFO Rendering menu. 2025-01-17 17:21:52 INFO Rendering menu. 2025-01-17 17:43:33 INFO Date: 2025-01-17 ======================================== Time: 17:43:33 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:43:33 INFO Date: 2025-01-17 ======================================== Time: 17:43:33 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:43:33 INFO Date: 2025-01-17 ======================================== Time: 17:43:33 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:43:33 INFO Date: 2025-01-17 ======================================== Time: 17:43:33 Logger Data: This is some log data. ---------------------------------------- 2025-01-17 17:43:33 INFO Rendering menu. 2025-01-17 17:43:33 INFO Rendering menu. 2025-01-17 17:43:33 INFO Rendering menu. 2025-01-17 17:43:33 INFO Rendering menu. 2025-01-17 17:43:33 INFO Rendering unauthenticated menu. 2025-01-17 17:43:33 INFO Rendering unauthenticated menu. 2025-01-17 17:43:33 INFO Rendering unauthenticated menu. 2025-01-17 17:43:33 INFO Rendering unauthenticated menu. 2025-01-17 17:43:53 INFO Rendering menu. 2025-01-17 17:43:53 INFO Rendering menu. 2025-01-17 17:43:53 INFO Rendering menu. 2025-01-17 17:43:53 INFO Rendering menu. 2025-01-17 17:43:53 INFO Login button clicked. 2025-01-17 17:43:53 INFO Login button clicked. 2025-01-17 17:43:53 INFO Login button clicked. 2025-01-17 17:43:53 INFO Login button clicked. 2025-01-17 17:43:56 INFO Login successful for user: maheshsr 2025-01-17 17:43:56 INFO Login successful for user: maheshsr 2025-01-17 17:43:56 INFO Login successful for user: maheshsr 2025-01-17 17:43:56 INFO Login successful for user: maheshsr 2025-01-17 17:43:57 INFO Rendering menu. 2025-01-17 17:43:57 INFO Rendering menu. 2025-01-17 17:43:57 INFO Rendering menu. 2025-01-17 17:43:57 INFO Rendering menu. 2025-01-20 10:25:38 INFO Date: 2025-01-20 ======================================== Time: 10:25:38 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:25:38 INFO Rendering menu. 2025-01-20 10:25:43 INFO Rendering unauthenticated menu. 2025-01-20 10:28:40 INFO Rendering menu. 2025-01-20 10:28:40 INFO Login button clicked. 2025-01-20 10:28:44 INFO Login successful for user: abhishek 2025-01-20 10:28:44 INFO Rendering menu. 2025-01-20 10:32:41 INFO Rendering menu. 2025-01-20 10:34:46 INFO Rendering menu. 2025-01-20 10:34:48 INFO Rendering menu. 2025-01-20 10:40:53 INFO Rendering menu. 2025-01-20 10:40:57 INFO Rendering menu. 2025-01-20 10:42:04 INFO Date: 2025-01-20 ======================================== Time: 10:42:04 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:42:04 INFO Date: 2025-01-20 ======================================== Time: 10:42:04 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:42:04 INFO Rendering menu. 2025-01-20 10:42:04 INFO Rendering menu. 2025-01-20 10:42:04 INFO Rendering unauthenticated menu. 2025-01-20 10:43:34 INFO Rendering menu. 2025-01-20 10:43:34 INFO Rendering menu. 2025-01-20 10:43:34 INFO Login button clicked. 2025-01-20 10:43:34 INFO Login button clicked. 2025-01-20 10:43:37 INFO Login successful for user: abhishek 2025-01-20 10:43:37 INFO Login successful for user: abhishek 2025-01-20 10:43:37 INFO Rendering menu. 2025-01-20 10:43:37 INFO Rendering menu. 2025-01-20 10:46:58 INFO Rendering menu. 2025-01-20 10:46:58 INFO Rendering menu. 2025-01-20 10:47:04 INFO Rendering menu. 2025-01-20 10:47:04 INFO Rendering menu. 2025-01-20 10:47:05 INFO Rendering menu. 2025-01-20 10:47:05 INFO Rendering menu. 2025-01-20 10:47:06 INFO Rendering menu. 2025-01-20 10:47:06 INFO Rendering menu. 2025-01-20 10:47:06 INFO Rendering menu. 2025-01-20 10:47:07 INFO Rendering menu. 2025-01-20 10:47:07 INFO Rendering menu. 2025-01-20 10:47:07 INFO Rendering menu. 2025-01-20 10:47:07 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:09 INFO Rendering menu. 2025-01-20 10:47:10 INFO Rendering menu. 2025-01-20 10:47:10 INFO Rendering menu. 2025-01-20 10:47:10 INFO Rendering menu. 2025-01-20 10:47:10 INFO Rendering menu. 2025-01-20 10:47:14 INFO Rendering menu. 2025-01-20 10:47:14 INFO Rendering menu. 2025-01-20 10:47:14 INFO User logged out. 2025-01-20 10:47:14 INFO User logged out. 2025-01-20 10:47:15 INFO Date: 2025-01-20 ======================================== Time: 10:47:15 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:15 INFO Date: 2025-01-20 ======================================== Time: 10:47:15 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:15 INFO Rendering menu. 2025-01-20 10:47:15 INFO Rendering menu. 2025-01-20 10:47:15 INFO Rendering unauthenticated menu. 2025-01-20 10:47:15 INFO Rendering unauthenticated menu. 2025-01-20 10:47:15 INFO Rendering unauthenticated menu. 2025-01-20 10:47:19 INFO Rendering menu. 2025-01-20 10:47:19 INFO Rendering menu. 2025-01-20 10:47:19 INFO Login button clicked. 2025-01-20 10:47:19 INFO Login button clicked. 2025-01-20 10:47:19 INFO Login button clicked. 2025-01-20 10:47:22 INFO Login successful for user: abhishek 2025-01-20 10:47:22 INFO Login successful for user: abhishek 2025-01-20 10:47:22 INFO Login successful for user: abhishek 2025-01-20 10:47:22 INFO Rendering menu. 2025-01-20 10:47:22 INFO Rendering menu. 2025-01-20 10:47:22 INFO Rendering menu. 2025-01-20 10:47:25 INFO Rendering menu. 2025-01-20 10:47:25 INFO Rendering menu. 2025-01-20 10:47:25 INFO Rendering menu. 2025-01-20 10:47:25 INFO Rendering menu. 2025-01-20 10:47:25 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:26 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO User logged out. 2025-01-20 10:47:29 INFO User logged out. 2025-01-20 10:47:29 INFO Date: 2025-01-20 ======================================== Time: 10:47:29 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:29 INFO Date: 2025-01-20 ======================================== Time: 10:47:29 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:29 INFO Date: 2025-01-20 ======================================== Time: 10:47:29 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:29 INFO Date: 2025-01-20 ======================================== Time: 10:47:29 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering menu. 2025-01-20 10:47:29 INFO Rendering unauthenticated menu. 2025-01-20 10:47:29 INFO Rendering unauthenticated menu. 2025-01-20 10:47:29 INFO Rendering unauthenticated menu. 2025-01-20 10:47:29 INFO Rendering unauthenticated menu. 2025-01-20 10:47:33 INFO Rendering menu. 2025-01-20 10:47:33 INFO Rendering menu. 2025-01-20 10:47:33 INFO Rendering menu. 2025-01-20 10:47:33 INFO Login button clicked. 2025-01-20 10:47:33 INFO Login button clicked. 2025-01-20 10:47:33 INFO Login button clicked. 2025-01-20 10:47:36 INFO Login successful for user: nanthinisri.l 2025-01-20 10:47:36 INFO Login successful for user: nanthinisri.l 2025-01-20 10:47:36 INFO Login successful for user: nanthinisri.l 2025-01-20 10:47:36 INFO Login successful for user: nanthinisri.l 2025-01-20 10:47:36 INFO Rendering menu. 2025-01-20 10:47:36 INFO Rendering menu. 2025-01-20 10:47:36 INFO Rendering menu. 2025-01-20 10:47:36 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:42 INFO Rendering menu. 2025-01-20 10:47:43 INFO Rendering menu. 2025-01-20 10:47:43 INFO Rendering menu. 2025-01-20 10:47:43 INFO Rendering menu. 2025-01-20 10:47:43 INFO Rendering menu. 2025-01-20 10:50:58 INFO Rendering menu. 2025-01-20 10:50:58 INFO Rendering menu. 2025-01-20 10:50:58 INFO Rendering menu. 2025-01-20 10:50:58 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:02 INFO Rendering menu. 2025-01-20 10:51:03 INFO Rendering menu. 2025-01-20 10:51:03 INFO Rendering menu. 2025-01-20 10:51:03 INFO Rendering menu. 2025-01-20 10:51:03 INFO Rendering menu. 2025-01-20 10:51:04 INFO Rendering menu. 2025-01-20 10:51:04 INFO Rendering menu. 2025-01-20 10:51:04 INFO Rendering menu. 2025-01-20 10:51:04 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:05 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:51:07 INFO Rendering menu. 2025-01-20 10:55:17 INFO Rendering menu. 2025-01-20 10:55:17 INFO Rendering menu. 2025-01-20 10:55:17 INFO Rendering menu. 2025-01-20 10:55:17 INFO Rendering menu. 2025-01-20 10:55:19 INFO Rendering menu. 2025-01-20 10:55:19 INFO Rendering menu. 2025-01-20 10:55:19 INFO Rendering menu. 2025-01-20 10:55:19 INFO Rendering menu. 2025-01-20 10:55:37 INFO Date: 2025-01-20 ======================================== Time: 10:55:37 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:55:37 INFO Date: 2025-01-20 ======================================== Time: 10:55:37 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:55:37 INFO Date: 2025-01-20 ======================================== Time: 10:55:37 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:55:37 INFO Date: 2025-01-20 ======================================== Time: 10:55:37 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:55:37 INFO Rendering menu. 2025-01-20 10:55:37 INFO Rendering menu. 2025-01-20 10:55:37 INFO Rendering menu. 2025-01-20 10:55:37 INFO Rendering menu. 2025-01-20 10:55:37 INFO Rendering menu. 2025-01-20 10:55:37 INFO Rendering unauthenticated menu. 2025-01-20 10:55:37 INFO Rendering unauthenticated menu. 2025-01-20 10:55:37 INFO Rendering unauthenticated menu. 2025-01-20 10:55:37 INFO Rendering unauthenticated menu. 2025-01-20 10:56:15 INFO Rendering menu. 2025-01-20 10:56:15 INFO Rendering menu. 2025-01-20 10:56:15 INFO Rendering menu. 2025-01-20 10:56:15 INFO Rendering menu. 2025-01-20 10:56:15 INFO Login button clicked. 2025-01-20 10:56:15 INFO Login button clicked. 2025-01-20 10:56:15 INFO Login button clicked. 2025-01-20 10:56:15 INFO Login button clicked. 2025-01-20 10:56:15 INFO Login button clicked. 2025-01-20 10:56:18 INFO Login successful for user: nanthinisri.l 2025-01-20 10:56:18 INFO Login successful for user: nanthinisri.l 2025-01-20 10:56:18 INFO Login successful for user: nanthinisri.l 2025-01-20 10:56:18 INFO Login successful for user: nanthinisri.l 2025-01-20 10:56:18 INFO Login successful for user: nanthinisri.l 2025-01-20 10:56:19 INFO Rendering menu. 2025-01-20 10:56:19 INFO Rendering menu. 2025-01-20 10:56:19 INFO Rendering menu. 2025-01-20 10:56:19 INFO Rendering menu. 2025-01-20 10:56:19 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:24 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:25 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:26 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:56:27 INFO Rendering menu. 2025-01-20 10:58:21 INFO Rendering menu. 2025-01-20 10:58:21 INFO Rendering menu. 2025-01-20 10:58:21 INFO Rendering menu. 2025-01-20 10:58:21 INFO Rendering menu. 2025-01-20 10:58:29 INFO Rendering menu. 2025-01-20 10:58:29 INFO Rendering menu. 2025-01-20 10:58:29 INFO Rendering menu. 2025-01-20 10:58:29 INFO Rendering menu. 2025-01-20 10:58:29 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:55 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:57 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:58:58 INFO Rendering menu. 2025-01-20 10:59:00 INFO Rendering menu. 2025-01-20 10:59:00 INFO Rendering menu. 2025-01-20 10:59:00 INFO Rendering menu. 2025-01-20 10:59:00 INFO Rendering menu. 2025-01-20 10:59:00 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:01 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO User logged out. 2025-01-20 10:59:02 INFO User logged out. 2025-01-20 10:59:02 INFO User logged out. 2025-01-20 10:59:02 INFO User logged out. 2025-01-20 10:59:02 INFO User logged out. 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Date: 2025-01-20 ======================================== Time: 10:59:02 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:02 INFO Rendering unauthenticated menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Rendering menu. 2025-01-20 10:59:16 INFO Login button clicked. 2025-01-20 10:59:16 INFO Login button clicked. 2025-01-20 10:59:16 INFO Login button clicked. 2025-01-20 10:59:16 INFO Login button clicked. 2025-01-20 10:59:16 INFO Login button clicked. 2025-01-20 10:59:20 INFO Login successful for user: nanthinisri.l 2025-01-20 10:59:20 INFO Login successful for user: nanthinisri.l 2025-01-20 10:59:20 INFO Login successful for user: nanthinisri.l 2025-01-20 10:59:20 INFO Login successful for user: nanthinisri.l 2025-01-20 10:59:20 INFO Login successful for user: nanthinisri.l 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 10:59:20 INFO Rendering menu. 2025-01-20 11:00:34 INFO Rendering menu. 2025-01-20 11:00:34 INFO Rendering menu. 2025-01-20 11:00:34 INFO Rendering menu. 2025-01-20 11:00:34 INFO Rendering menu. 2025-01-20 11:00:34 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:39 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:01:45 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:14 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:06:18 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:27 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:07:41 INFO Rendering menu. 2025-01-20 11:39:14 INFO Date: 2025-01-20 ======================================== Time: 11:39:14 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:39:14 INFO Rendering menu. 2025-01-20 11:39:14 INFO Rendering unauthenticated menu. 2025-01-20 11:39:34 INFO Rendering menu. 2025-01-20 11:39:34 INFO Login button clicked. 2025-01-20 11:39:36 INFO Rendering menu. 2025-01-20 11:39:36 INFO Login button clicked. 2025-01-20 11:39:38 INFO Login successful for user: nanthinisri.l 2025-01-20 11:39:38 INFO Rendering menu. 2025-01-20 11:40:00 INFO Rendering menu. 2025-01-20 11:40:02 INFO Database names fetched successfully. 2025-01-20 11:40:56 INFO Rendering menu. 2025-01-20 11:40:56 INFO Database names fetched successfully. 2025-01-20 11:41:04 INFO Date: 2025-01-20 ======================================== Time: 11:41:04 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:41:04 INFO Rendering menu. 2025-01-20 11:41:04 INFO Rendering menu. 2025-01-20 11:41:04 INFO Rendering unauthenticated menu. 2025-01-20 11:41:04 INFO Rendering unauthenticated menu. 2025-01-20 11:41:23 INFO Rendering menu. 2025-01-20 11:41:23 INFO Login button clicked. 2025-01-20 11:41:23 INFO Login button clicked. 2025-01-20 11:41:27 INFO Login successful for user: nanthinisri.l 2025-01-20 11:41:27 INFO Login successful for user: nanthinisri.l 2025-01-20 11:41:27 INFO Rendering menu. 2025-01-20 11:41:27 INFO Rendering menu. 2025-01-20 11:42:01 INFO Rendering menu. 2025-01-20 11:42:09 INFO Rendering menu. 2025-01-20 11:42:09 INFO Database names fetched successfully. 2025-01-20 11:42:09 INFO Database names fetched successfully. 2025-01-20 11:42:13 INFO Date: 2025-01-20 ======================================== Time: 11:42:13 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:42:13 INFO Date: 2025-01-20 ======================================== Time: 11:42:13 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:42:13 INFO Date: 2025-01-20 ======================================== Time: 11:42:13 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:42:13 INFO Rendering menu. 2025-01-20 11:42:13 INFO Rendering menu. 2025-01-20 11:42:13 INFO Rendering menu. 2025-01-20 11:42:13 INFO Rendering unauthenticated menu. 2025-01-20 11:42:13 INFO Rendering unauthenticated menu. 2025-01-20 11:42:32 INFO Rendering menu. 2025-01-20 11:42:32 INFO Rendering menu. 2025-01-20 11:42:32 INFO Login button clicked. 2025-01-20 11:42:32 INFO Login button clicked. 2025-01-20 11:42:32 INFO Login button clicked. 2025-01-20 11:42:36 INFO Login successful for user: abhishek 2025-01-20 11:42:36 INFO Login successful for user: abhishek 2025-01-20 11:42:36 INFO Login successful for user: abhishek 2025-01-20 11:42:36 INFO Rendering menu. 2025-01-20 11:42:36 INFO Rendering menu. 2025-01-20 11:42:36 INFO Rendering menu. 2025-01-20 11:45:04 INFO Rendering menu. 2025-01-20 11:45:04 INFO Rendering menu. 2025-01-20 11:45:04 INFO Rendering menu. 2025-01-20 11:45:11 INFO Date: 2025-01-20 ======================================== Time: 11:45:11 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:45:11 INFO Date: 2025-01-20 ======================================== Time: 11:45:11 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:45:11 INFO Date: 2025-01-20 ======================================== Time: 11:45:11 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:45:11 INFO Date: 2025-01-20 ======================================== Time: 11:45:11 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:45:11 INFO Rendering menu. 2025-01-20 11:45:11 INFO Rendering menu. 2025-01-20 11:45:11 INFO Rendering menu. 2025-01-20 11:45:11 INFO Rendering unauthenticated menu. 2025-01-20 11:45:11 INFO Rendering unauthenticated menu. 2025-01-20 11:45:11 INFO Rendering unauthenticated menu. 2025-01-20 11:45:30 INFO Rendering menu. 2025-01-20 11:45:30 INFO Rendering menu. 2025-01-20 11:45:30 INFO Rendering menu. 2025-01-20 11:45:30 INFO Rendering menu. 2025-01-20 11:45:30 INFO Login button clicked. 2025-01-20 11:45:30 INFO Login button clicked. 2025-01-20 11:45:30 INFO Login button clicked. 2025-01-20 11:45:30 INFO Login button clicked. 2025-01-20 11:45:33 INFO Login successful for user: abhishek 2025-01-20 11:45:33 INFO Login successful for user: abhishek 2025-01-20 11:45:33 INFO Login successful for user: abhishek 2025-01-20 11:45:33 INFO Login successful for user: abhishek 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Rendering menu. 2025-01-20 11:45:33 INFO Database names fetched successfully. 2025-01-20 11:45:33 INFO Database names fetched successfully. 2025-01-20 11:45:33 INFO Database names fetched successfully. 2025-01-20 11:45:33 INFO Database names fetched successfully. 2025-01-20 11:45:47 INFO Rendering menu. 2025-01-20 11:45:47 INFO Rendering menu. 2025-01-20 11:45:47 INFO Rendering menu. 2025-01-20 11:45:47 INFO Rendering menu. 2025-01-20 11:45:47 INFO Database names fetched successfully. 2025-01-20 11:45:47 INFO Database names fetched successfully. 2025-01-20 11:45:47 INFO Database names fetched successfully. 2025-01-20 11:45:47 INFO Database names fetched successfully. 2025-01-20 11:45:48 INFO Rendering menu. 2025-01-20 11:45:48 INFO Rendering menu. 2025-01-20 11:45:48 INFO Rendering menu. 2025-01-20 11:45:48 INFO Rendering menu. 2025-01-20 11:45:50 INFO Rendering menu. 2025-01-20 11:45:50 INFO Rendering menu. 2025-01-20 11:45:50 INFO Rendering menu. 2025-01-20 11:45:50 INFO Rendering menu. 2025-01-20 11:45:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:45:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:45:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:45:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:45:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:45:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:45:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:45:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:45:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:45:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:46:05 INFO Rendering menu. 2025-01-20 11:46:05 INFO Rendering menu. 2025-01-20 11:46:05 INFO Rendering menu. 2025-01-20 11:46:05 INFO Rendering menu. 2025-01-20 11:46:07 INFO Date: 2025-01-20 ======================================== Time: 11:46:07 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:07 INFO Date: 2025-01-20 ======================================== Time: 11:46:07 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:07 INFO Date: 2025-01-20 ======================================== Time: 11:46:07 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:07 INFO Date: 2025-01-20 ======================================== Time: 11:46:07 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:07 INFO Date: 2025-01-20 ======================================== Time: 11:46:07 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:07 INFO Rendering menu. 2025-01-20 11:46:07 INFO Rendering menu. 2025-01-20 11:46:07 INFO Rendering menu. 2025-01-20 11:46:07 INFO Rendering menu. 2025-01-20 11:46:07 INFO Rendering menu. 2025-01-20 11:46:07 INFO Rendering unauthenticated menu. 2025-01-20 11:46:07 INFO Rendering unauthenticated menu. 2025-01-20 11:46:07 INFO Rendering unauthenticated menu. 2025-01-20 11:46:07 INFO Rendering unauthenticated menu. 2025-01-20 11:46:07 INFO Rendering unauthenticated menu. 2025-01-20 11:46:27 INFO Rendering menu. 2025-01-20 11:46:27 INFO Rendering menu. 2025-01-20 11:46:27 INFO Rendering menu. 2025-01-20 11:46:27 INFO Rendering menu. 2025-01-20 11:46:27 INFO Rendering menu. 2025-01-20 11:46:27 INFO Login button clicked. 2025-01-20 11:46:27 INFO Login button clicked. 2025-01-20 11:46:27 INFO Login button clicked. 2025-01-20 11:46:27 INFO Login button clicked. 2025-01-20 11:46:27 INFO Login button clicked. 2025-01-20 11:46:30 INFO Login successful for user: abhishek 2025-01-20 11:46:30 INFO Login successful for user: abhishek 2025-01-20 11:46:30 INFO Login successful for user: abhishek 2025-01-20 11:46:30 INFO Login successful for user: abhishek 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Rendering menu. 2025-01-20 11:46:30 INFO Database names fetched successfully. 2025-01-20 11:46:30 INFO Database names fetched successfully. 2025-01-20 11:46:30 INFO Database names fetched successfully. 2025-01-20 11:46:30 INFO Database names fetched successfully. 2025-01-20 11:46:44 INFO Rendering menu. 2025-01-20 11:46:44 INFO Rendering menu. 2025-01-20 11:46:44 INFO Rendering menu. 2025-01-20 11:46:44 INFO Rendering menu. 2025-01-20 11:46:44 INFO Rendering menu. 2025-01-20 11:46:44 INFO Database names fetched successfully. 2025-01-20 11:46:44 INFO Database names fetched successfully. 2025-01-20 11:46:44 INFO Database names fetched successfully. 2025-01-20 11:46:44 INFO Database names fetched successfully. 2025-01-20 11:46:48 INFO Rendering menu. 2025-01-20 11:46:48 INFO Rendering menu. 2025-01-20 11:46:48 INFO Rendering menu. 2025-01-20 11:46:48 INFO Rendering menu. 2025-01-20 11:46:48 INFO Rendering menu. 2025-01-20 11:46:49 INFO Rendering menu. 2025-01-20 11:46:49 INFO Rendering menu. 2025-01-20 11:46:49 INFO Rendering menu. 2025-01-20 11:46:49 INFO Rendering menu. 2025-01-20 11:46:49 INFO Rendering menu. 2025-01-20 11:46:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:46:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:46:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:46:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:46:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:46:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:46:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:46:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:46:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:46:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:46:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:46:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Date: 2025-01-20 ======================================== Time: 11:46:54 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering menu. 2025-01-20 11:46:54 INFO Rendering unauthenticated menu. 2025-01-20 11:46:54 INFO Rendering unauthenticated menu. 2025-01-20 11:46:54 INFO Rendering unauthenticated menu. 2025-01-20 11:46:54 INFO Rendering unauthenticated menu. 2025-01-20 11:46:54 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:10 INFO Rendering unauthenticated menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Rendering menu. 2025-01-20 11:48:21 INFO Login button clicked. 2025-01-20 11:48:21 INFO Login button clicked. 2025-01-20 11:48:21 INFO Login button clicked. 2025-01-20 11:48:21 INFO Login button clicked. 2025-01-20 11:48:21 INFO Login button clicked. 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Login successful for user: abhishek 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Rendering menu. 2025-01-20 11:48:24 INFO Database names fetched successfully. 2025-01-20 11:48:24 INFO Database names fetched successfully. 2025-01-20 11:48:24 INFO Database names fetched successfully. 2025-01-20 11:48:24 INFO Database names fetched successfully. 2025-01-20 11:48:24 INFO Database names fetched successfully. 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Date: 2025-01-20 ======================================== Time: 11:49:32 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:49:32 INFO Rendering unauthenticated menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Rendering menu. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:37 INFO Login button clicked. 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Login successful for user: abhishek 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Rendering menu. 2025-01-20 11:50:40 INFO Database names fetched successfully. 2025-01-20 11:50:40 INFO Database names fetched successfully. 2025-01-20 11:50:40 INFO Database names fetched successfully. 2025-01-20 11:50:40 INFO Database names fetched successfully. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:56 INFO Rendering menu. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:50:57 INFO Database names fetched successfully. 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Date: 2025-01-20 ======================================== Time: 11:53:30 Logger Data: This is some log data. ---------------------------------------- 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:30 INFO Rendering unauthenticated menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Rendering menu. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:48 INFO Login button clicked. 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Login successful for user: abhishek 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:53:51 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:54:31 INFO Rendering unauthenticated menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Rendering menu. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:03 INFO Login button clicked. 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Login successful for user: abhishek 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Rendering menu. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:07 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Rendering menu. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:11 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Rendering menu. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:16 INFO Database names fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:17 INFO Table details fetched successfully. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:55:33 INFO Rendering unauthenticated menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Rendering menu. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:16 INFO Login button clicked. 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Login successful for user: abhishek 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Rendering menu. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:19 INFO Database names fetched successfully. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:33 INFO Rendering menu. 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:56:57 INFO Rendering unauthenticated menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Rendering menu. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:18 INFO Login button clicked. 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:21 INFO Login successful for user: abhishek 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Rendering menu. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-20 11:57:22 INFO Database names fetched successfully. 2025-01-22 11:33:36 INFO Date: 2025-01-22 ======================================== Time: 11:33:36 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:33:40 INFO not logined 2025-01-22 11:33:40 INFO Rendering unauthenticated menu. 2025-01-22 11:34:22 INFO Login button clicked. 2025-01-22 11:34:26 INFO Login successful for user: maheshsr 2025-01-22 11:34:35 INFO Database names fetched successfully. 2025-01-22 11:34:50 INFO Database names fetched successfully. 2025-01-22 11:34:51 INFO Table details fetched successfully. 2025-01-22 11:35:13 INFO Database names fetched successfully. 2025-01-22 11:35:13 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:35:15 INFO Tokens consumed: 2970 2025-01-22 11:35:16 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:35:17 INFO token updated successfully: 2025-01 2025-01-22 11:35:17 INFO token updated successfully. 2025-01-22 11:35:17 INFO Connected to the database MHealth_Dev. 2025-01-22 11:35:17 INFO Query executed successfully. 2025-01-22 11:35:17 INFO Connected to the database MHealth_Dev. 2025-01-22 11:35:17 INFO Query executed successfully. 2025-01-22 11:35:17 INFO st.session_state['offset'], 2025-01-22 11:35:17 INFO hi buttoon............................................................ 2025-01-22 11:35:19 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 128 2025-01-22 11:35:21 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-22 11:35:22 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/129.json 2025-01-22 11:39:28 INFO Database names fetched successfully. 2025-01-22 11:39:39 INFO Date: 2025-01-22 ======================================== Time: 11:39:39 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:39:39 INFO Date: 2025-01-22 ======================================== Time: 11:39:39 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:39:39 INFO not logined 2025-01-22 11:39:39 INFO not logined 2025-01-22 11:39:39 INFO Rendering unauthenticated menu. 2025-01-22 11:39:39 INFO Rendering unauthenticated menu. 2025-01-22 11:40:01 INFO Login button clicked. 2025-01-22 11:40:01 INFO Login button clicked. 2025-01-22 11:40:04 INFO Login successful for user: maheshsr 2025-01-22 11:40:04 INFO Login successful for user: maheshsr 2025-01-22 11:40:04 INFO Database names fetched successfully. 2025-01-22 11:40:04 INFO Database names fetched successfully. 2025-01-22 11:40:10 INFO Database names fetched successfully. 2025-01-22 11:40:10 INFO Table details fetched successfully. 2025-01-22 11:40:24 INFO Database names fetched successfully. 2025-01-22 11:40:24 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:40:24 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:40:26 INFO Tokens consumed: 2970 2025-01-22 11:40:27 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:40:27 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:40:28 INFO token updated successfully: 2025-01 2025-01-22 11:40:28 INFO token updated successfully: 2025-01 2025-01-22 11:40:28 INFO token updated successfully. 2025-01-22 11:40:28 INFO token updated successfully. 2025-01-22 11:40:28 INFO Connected to the database MHealth_Dev. 2025-01-22 11:40:28 INFO Connected to the database MHealth_Dev. 2025-01-22 11:40:28 INFO Query executed successfully. 2025-01-22 11:40:28 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:40:28 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:43:33 INFO Database names fetched successfully. 2025-01-22 11:43:33 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:43:33 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:43:35 INFO Tokens consumed: 2970 2025-01-22 11:43:35 INFO Tokens consumed: 2970 2025-01-22 11:43:37 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:43:37 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:43:38 INFO token updated successfully: 2025-01 2025-01-22 11:43:38 INFO token updated successfully: 2025-01 2025-01-22 11:43:38 INFO token updated successfully. 2025-01-22 11:43:38 INFO token updated successfully. 2025-01-22 11:43:38 INFO Connected to the database MHealth_Dev. 2025-01-22 11:43:38 INFO Query executed successfully. 2025-01-22 11:43:38 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:43:38 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:44:51 INFO Database names fetched successfully. 2025-01-22 11:44:51 INFO Database names fetched successfully. 2025-01-22 11:44:51 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:44:51 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:44:53 INFO Tokens consumed: 2970 2025-01-22 11:44:53 INFO Tokens consumed: 2970 2025-01-22 11:44:55 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:44:55 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:44:56 INFO token updated successfully: 2025-01 2025-01-22 11:44:56 INFO token updated successfully: 2025-01 2025-01-22 11:44:56 INFO token updated successfully. 2025-01-22 11:44:56 INFO Connected to the database MHealth_Dev. 2025-01-22 11:44:56 INFO Query executed successfully. 2025-01-22 11:44:56 INFO Query executed successfully. 2025-01-22 11:44:56 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:44:56 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:49:01 INFO Date: 2025-01-22 ======================================== Time: 11:49:01 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:49:05 INFO not logined 2025-01-22 11:49:05 INFO Rendering unauthenticated menu. 2025-01-22 11:49:18 INFO Login button clicked. 2025-01-22 11:49:22 INFO Login successful for user: maheshsr 2025-01-22 11:49:30 INFO Database names fetched successfully. 2025-01-22 11:49:45 INFO Database names fetched successfully. 2025-01-22 11:49:45 INFO Table details fetched successfully. 2025-01-22 11:50:12 INFO Database names fetched successfully. 2025-01-22 11:50:12 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:50:14 INFO Tokens consumed: 2970 2025-01-22 11:50:16 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:50:17 INFO token updated successfully: 2025-01 2025-01-22 11:50:17 INFO token updated successfully. 2025-01-22 11:50:17 INFO Connected to the database MHealth_Dev. 2025-01-22 11:50:17 INFO Query executed successfully. 2025-01-22 11:50:17 INFO Connected to the database MHealth_Dev. 2025-01-22 11:50:17 INFO Query executed successfully. 2025-01-22 11:50:17 INFO 0 2025-01-22 11:50:17 INFO hi buttoon............................................................ 2025-01-22 11:50:20 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 129 2025-01-22 11:50:22 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-22 11:50:23 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-22 11:50:41 INFO Database names fetched successfully. 2025-01-22 11:50:43 INFO Blob exists check for query_library/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-22 11:50:44 INFO SQL query blob saved successfully: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-22 11:50:44 INFO Query saved in the library with id 130. 2025-01-22 11:55:15 INFO Date: 2025-01-22 ======================================== Time: 11:55:15 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:55:15 INFO not logined 2025-01-22 11:55:15 INFO not logined 2025-01-22 11:55:15 INFO Rendering unauthenticated menu. 2025-01-22 11:55:15 INFO Rendering unauthenticated menu. 2025-01-22 11:55:33 INFO Login button clicked. 2025-01-22 11:55:33 INFO Login button clicked. 2025-01-22 11:55:37 INFO Login successful for user: maheshsr 2025-01-22 11:55:37 INFO Login successful for user: maheshsr 2025-01-22 11:55:37 INFO Database names fetched successfully. 2025-01-22 11:55:37 INFO Database names fetched successfully. 2025-01-22 11:55:43 INFO Database names fetched successfully. 2025-01-22 11:55:43 INFO Database names fetched successfully. 2025-01-22 11:55:43 INFO Table details fetched successfully. 2025-01-22 11:55:43 INFO Table details fetched successfully. 2025-01-22 11:55:52 INFO Database names fetched successfully. 2025-01-22 11:55:52 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:55:52 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:55:54 INFO Tokens consumed: 2970 2025-01-22 11:55:54 INFO Tokens consumed: 2970 2025-01-22 11:55:56 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:55:56 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:55:57 INFO token updated successfully: 2025-01 2025-01-22 11:55:57 INFO token updated successfully. 2025-01-22 11:55:57 INFO token updated successfully: 2025-01 2025-01-22 11:55:57 INFO token updated successfully. 2025-01-22 11:55:57 INFO Connected to the database MHealth_Dev. 2025-01-22 11:55:57 INFO Query executed successfully. 2025-01-22 11:55:57 INFO Query executed successfully. 2025-01-22 11:55:57 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:55:57 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:58:07 INFO Date: 2025-01-22 ======================================== Time: 11:58:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:58:07 INFO Date: 2025-01-22 ======================================== Time: 11:58:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:58:07 INFO Date: 2025-01-22 ======================================== Time: 11:58:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 11:58:07 INFO not logined 2025-01-22 11:58:07 INFO not logined 2025-01-22 11:58:07 INFO not logined 2025-01-22 11:58:07 INFO Rendering unauthenticated menu. 2025-01-22 11:58:07 INFO Rendering unauthenticated menu. 2025-01-22 11:58:07 INFO Rendering unauthenticated menu. 2025-01-22 11:58:28 INFO Login button clicked. 2025-01-22 11:58:28 INFO Login button clicked. 2025-01-22 11:58:31 INFO Login successful for user: maheshsr 2025-01-22 11:58:31 INFO Login successful for user: maheshsr 2025-01-22 11:58:31 INFO Login successful for user: maheshsr 2025-01-22 11:58:32 INFO Database names fetched successfully. 2025-01-22 11:58:32 INFO Database names fetched successfully. 2025-01-22 11:58:36 INFO Database names fetched successfully. 2025-01-22 11:58:36 INFO Database names fetched successfully. 2025-01-22 11:58:36 INFO Table details fetched successfully. 2025-01-22 11:58:36 INFO Table details fetched successfully. 2025-01-22 11:58:44 INFO Database names fetched successfully. 2025-01-22 11:58:44 INFO Database names fetched successfully. 2025-01-22 11:58:44 INFO Database names fetched successfully. 2025-01-22 11:58:44 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:58:44 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:58:44 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 11:58:46 INFO Tokens consumed: 2970 2025-01-22 11:58:46 INFO Tokens consumed: 2970 2025-01-22 11:58:46 INFO Tokens consumed: 2970 2025-01-22 11:58:48 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:58:48 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:58:48 INFO Existing token_consumed found for month: 2025-01 2025-01-22 11:58:49 INFO token updated successfully: 2025-01 2025-01-22 11:58:49 INFO token updated successfully: 2025-01 2025-01-22 11:58:49 INFO token updated successfully: 2025-01 2025-01-22 11:58:49 INFO token updated successfully. 2025-01-22 11:58:49 INFO token updated successfully. 2025-01-22 11:58:49 INFO token updated successfully. 2025-01-22 11:58:49 INFO Connected to the database MHealth_Dev. 2025-01-22 11:58:49 INFO Connected to the database MHealth_Dev. 2025-01-22 11:58:49 INFO Connected to the database MHealth_Dev. 2025-01-22 11:58:49 INFO Query executed successfully. 2025-01-22 11:58:49 INFO Query executed successfully. 2025-01-22 11:58:49 INFO Query executed successfully. 2025-01-22 11:58:49 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:58:49 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 11:58:49 ERROR Error processing request: 'st.session_state has no key "offset". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-22 12:00:39 INFO Date: 2025-01-22 ======================================== Time: 12:00:39 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 12:00:43 INFO not logined 2025-01-22 12:00:43 INFO Rendering unauthenticated menu. 2025-01-22 12:01:02 INFO Login button clicked. 2025-01-22 12:01:06 INFO Login successful for user: maheshsr 2025-01-22 12:01:14 INFO Database names fetched successfully. 2025-01-22 12:01:30 INFO Database names fetched successfully. 2025-01-22 12:01:30 INFO Table details fetched successfully. 2025-01-22 12:01:55 INFO Database names fetched successfully. 2025-01-22 12:01:55 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 12:01:57 INFO Tokens consumed: 2970 2025-01-22 12:01:59 INFO Existing token_consumed found for month: 2025-01 2025-01-22 12:02:00 INFO token updated successfully: 2025-01 2025-01-22 12:02:00 INFO token updated successfully. 2025-01-22 12:02:00 INFO Connected to the database MHealth_Dev. 2025-01-22 12:02:00 INFO Query executed successfully. 2025-01-22 12:02:00 INFO Connected to the database MHealth_Dev. 2025-01-22 12:02:00 INFO Query executed successfully. 2025-01-22 12:02:00 INFO 0 2025-01-22 12:02:00 INFO hi buttoon............................................................ 2025-01-22 12:02:29 INFO Database names fetched successfully. 2025-01-22 13:11:47 INFO Date: 2025-01-22 ======================================== Time: 13:11:47 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 13:11:47 INFO Date: 2025-01-22 ======================================== Time: 13:11:47 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 13:11:47 INFO not logined 2025-01-22 13:11:47 INFO not logined 2025-01-22 13:11:47 INFO Rendering unauthenticated menu. 2025-01-22 13:11:47 INFO Rendering unauthenticated menu. 2025-01-22 13:12:31 INFO Login button clicked. 2025-01-22 13:12:31 INFO Login button clicked. 2025-01-22 13:12:34 INFO Login successful for user: maheshsr 2025-01-22 13:12:34 INFO Login successful for user: maheshsr 2025-01-22 13:12:36 INFO Database names fetched successfully. 2025-01-22 13:12:36 INFO Database names fetched successfully. 2025-01-22 13:12:40 INFO Database names fetched successfully. 2025-01-22 13:12:40 INFO Database names fetched successfully. 2025-01-22 13:12:40 INFO Table details fetched successfully. 2025-01-22 13:12:40 INFO Table details fetched successfully. 2025-01-22 13:12:48 INFO Database names fetched successfully. 2025-01-22 13:12:48 INFO Database names fetched successfully. 2025-01-22 13:12:48 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 13:12:48 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 13:12:50 INFO Tokens consumed: 2970 2025-01-22 13:12:50 INFO Tokens consumed: 2970 2025-01-22 13:12:52 INFO Existing token_consumed found for month: 2025-01 2025-01-22 13:12:52 INFO Existing token_consumed found for month: 2025-01 2025-01-22 13:12:53 INFO token updated successfully: 2025-01 2025-01-22 13:12:53 INFO token updated successfully: 2025-01 2025-01-22 13:12:53 INFO token updated successfully. 2025-01-22 13:12:53 INFO token updated successfully. 2025-01-22 13:12:53 INFO Connected to the database MHealth_Dev. 2025-01-22 13:12:53 INFO Connected to the database MHealth_Dev. 2025-01-22 13:12:53 INFO Query executed successfully. 2025-01-22 13:12:53 INFO Query executed successfully. 2025-01-22 13:12:53 INFO Connected to the database MHealth_Dev. 2025-01-22 13:12:53 INFO Query executed successfully. 2025-01-22 13:12:53 INFO Query executed successfully. 2025-01-22 13:12:53 INFO 0 2025-01-22 13:12:53 INFO 0 2025-01-22 13:12:53 INFO hi buttoon............................................................ 2025-01-22 13:12:53 INFO hi buttoon............................................................ 2025-01-22 13:14:39 INFO Date: 2025-01-22 ======================================== Time: 13:14:39 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 13:14:43 INFO not logined 2025-01-22 13:14:43 INFO Rendering unauthenticated menu. 2025-01-22 13:14:57 INFO Login button clicked. 2025-01-22 13:15:00 INFO Login successful for user: maheshsr 2025-01-22 13:15:09 INFO Database names fetched successfully. 2025-01-22 13:15:24 INFO Database names fetched successfully. 2025-01-22 13:15:24 INFO Table details fetched successfully. 2025-01-22 13:15:51 INFO Database names fetched successfully. 2025-01-22 13:15:51 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 13:15:53 INFO Tokens consumed: 2970 2025-01-22 13:15:55 INFO Existing token_consumed found for month: 2025-01 2025-01-22 13:15:57 INFO token updated successfully: 2025-01 2025-01-22 13:15:57 INFO token updated successfully. 2025-01-22 13:15:57 INFO Connected to the database MHealth_Dev. 2025-01-22 13:15:57 INFO Query executed successfully. 2025-01-22 13:15:57 INFO Connected to the database MHealth_Dev. 2025-01-22 13:15:57 INFO Query executed successfully. 2025-01-22 13:15:58 ERROR Error processing request: There are multiple `button` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `button` element. 2025-01-22 13:41:07 INFO Database names fetched successfully. 2025-01-22 15:22:40 INFO Date: 2025-01-22 ======================================== Time: 15:22:40 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 15:22:45 INFO not logined 2025-01-22 15:22:45 INFO Rendering unauthenticated menu. 2025-01-22 15:25:45 INFO Login button clicked. 2025-01-22 15:26:18 INFO Login successful for user: maheshsr 2025-01-22 15:26:29 INFO Database names fetched successfully. 2025-01-22 15:27:47 INFO Database names fetched successfully. 2025-01-22 15:27:48 INFO Table details fetched successfully. 2025-01-22 15:38:59 INFO Database names fetched successfully. 2025-01-22 15:38:59 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 15:39:15 ERROR Exception while running prompt: Request timed out. 2025-01-22 15:39:16 INFO Connected to the database MHealth_Dev. 2025-01-22 15:39:16 ERROR Query execution failed: (pyodbc.ProgrammingError) ('42000', "[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Incorrect syntax near ')'. (102) (SQLExecDirectW)") [SQL: WITH CTE AS ( SELECT *, ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS RowNum FROM () AS subquery ) SELECT * FROM CTE WHERE RowNum BETWEEN 1 AND 100; ] (Background on this error at: https://sqlalche.me/e/20/f405) 2025-01-22 15:39:16 INFO Connected to the database MHealth_Dev. 2025-01-22 15:39:16 ERROR Query execution failed: (pyodbc.ProgrammingError) ('42000', "[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Incorrect syntax near ')'. (102) (SQLExecDirectW)") [SQL: WITH CTE AS ( SELECT *, ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS RowNum FROM () AS subquery ) SELECT * FROM CTE WHERE RowNum BETWEEN 1 AND 100; ] (Background on this error at: https://sqlalche.me/e/20/f405) 2025-01-22 15:40:39 INFO Database names fetched successfully. 2025-01-22 15:41:07 INFO Database names fetched successfully. 2025-01-22 15:41:13 INFO Database names fetched successfully. 2025-01-22 15:41:13 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 15:41:29 ERROR Exception while running prompt: Request timed out. 2025-01-22 15:41:29 INFO Connected to the database MHealth_Dev. 2025-01-22 15:41:29 ERROR Query execution failed: (pyodbc.ProgrammingError) ('42000', "[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Incorrect syntax near ')'. (102) (SQLExecDirectW)") [SQL: WITH CTE AS ( SELECT *, ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS RowNum FROM () AS subquery ) SELECT * FROM CTE WHERE RowNum BETWEEN 1 AND 100; ] (Background on this error at: https://sqlalche.me/e/20/f405) 2025-01-22 15:41:29 INFO Connected to the database MHealth_Dev. 2025-01-22 15:41:29 ERROR Query execution failed: (pyodbc.ProgrammingError) ('42000', "[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]Incorrect syntax near ')'. (102) (SQLExecDirectW)") [SQL: WITH CTE AS ( SELECT *, ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS RowNum FROM () AS subquery ) SELECT * FROM CTE WHERE RowNum BETWEEN 1 AND 100; ] (Background on this error at: https://sqlalche.me/e/20/f405) 2025-01-22 15:41:53 ERROR Exception while getting max blob number: (, 'Connection to phsstorageacc.blob.core.windows.net timed out. (connect timeout=20)') 2025-01-22 15:44:11 ERROR Exception while getting max blob number: (, 'Connection to phsstorageacc.blob.core.windows.net timed out. (connect timeout=20)') 2025-01-22 15:46:57 ERROR Exception while checking if blob exists: (, 'Connection to phsstorageacc.blob.core.windows.net timed out. (connect timeout=20)') 2025-01-22 15:49:38 ERROR Exception while saving SQL query blob: (, 'Connection to phsstorageacc.blob.core.windows.net timed out. (connect timeout=20)') 2025-01-22 16:52:44 INFO Date: 2025-01-22 ======================================== Time: 16:52:44 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 16:52:49 INFO not logined 2025-01-22 16:52:49 INFO Rendering unauthenticated menu. 2025-01-22 16:53:42 INFO Login button clicked. 2025-01-22 16:53:45 INFO Login successful for user: maheshsr 2025-01-22 16:53:56 INFO Database names fetched successfully. 2025-01-22 16:54:10 INFO Database names fetched successfully. 2025-01-22 16:54:11 INFO Table details fetched successfully. 2025-01-22 16:54:41 INFO Database names fetched successfully. 2025-01-22 16:54:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 16:54:43 INFO Tokens consumed: 2970 2025-01-22 16:54:45 INFO Existing token_consumed found for month: 2025-01 2025-01-22 16:54:46 INFO token updated successfully: 2025-01 2025-01-22 16:54:46 INFO token updated successfully. 2025-01-22 16:54:46 INFO Connected to the database MHealth_Dev. 2025-01-22 16:54:46 INFO Query executed successfully. 2025-01-22 16:54:46 INFO Connected to the database MHealth_Dev. 2025-01-22 16:54:46 INFO Query executed successfully. 2025-01-22 16:54:47 ERROR Error processing request: There are multiple `button` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `button` element. 2025-01-22 16:57:27 INFO Database names fetched successfully. 2025-01-22 16:57:53 INFO Database names fetched successfully. 2025-01-22 16:57:53 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 16:57:55 INFO Tokens consumed: 2970 2025-01-22 16:57:57 INFO Existing token_consumed found for month: 2025-01 2025-01-22 16:57:58 INFO token updated successfully: 2025-01 2025-01-22 16:57:58 INFO token updated successfully. 2025-01-22 16:57:58 INFO Connected to the database MHealth_Dev. 2025-01-22 16:57:58 INFO Query executed successfully. 2025-01-22 16:57:58 INFO Connected to the database MHealth_Dev. 2025-01-22 16:57:58 INFO Query executed successfully. 2025-01-22 16:57:59 ERROR Error processing request: There are multiple `button` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `button` element. 2025-01-22 16:58:43 INFO Database names fetched successfully. 2025-01-22 17:00:12 INFO Database names fetched successfully. 2025-01-22 17:01:23 INFO Database names fetched successfully. 2025-01-22 17:01:23 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:01:25 INFO Tokens consumed: 2970 2025-01-22 17:01:27 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:01:28 INFO token updated successfully: 2025-01 2025-01-22 17:01:28 INFO token updated successfully. 2025-01-22 17:01:28 INFO Connected to the database MHealth_Dev. 2025-01-22 17:01:28 INFO Query executed successfully. 2025-01-22 17:01:28 INFO Connected to the database MHealth_Dev. 2025-01-22 17:01:28 INFO Query executed successfully. 2025-01-22 17:01:29 ERROR Error processing request: There are multiple `button` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `button` element. 2025-01-22 17:04:26 INFO Date: 2025-01-22 ======================================== Time: 17:04:26 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 17:04:30 INFO not logined 2025-01-22 17:04:30 INFO Rendering unauthenticated menu. 2025-01-22 17:04:52 INFO Login button clicked. 2025-01-22 17:04:55 INFO Login successful for user: maheshsr 2025-01-22 17:05:04 INFO Database names fetched successfully. 2025-01-22 17:05:19 INFO Database names fetched successfully. 2025-01-22 17:05:19 INFO Table details fetched successfully. 2025-01-22 17:05:39 INFO Database names fetched successfully. 2025-01-22 17:05:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:05:41 INFO Tokens consumed: 2970 2025-01-22 17:05:43 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:05:44 INFO token updated successfully: 2025-01 2025-01-22 17:05:44 INFO token updated successfully. 2025-01-22 17:05:44 INFO Connected to the database MHealth_Dev. 2025-01-22 17:05:44 INFO Query executed successfully. 2025-01-22 17:05:44 INFO Connected to the database MHealth_Dev. 2025-01-22 17:05:44 INFO Query executed successfully. 2025-01-22 17:05:45 ERROR Error processing request: There are multiple elements with the same `key='1'`. To fix this, please make sure that the `key` argument is unique for each element you create. 2025-01-22 17:09:26 INFO Database names fetched successfully. 2025-01-22 17:10:14 INFO Database names fetched successfully. 2025-01-22 17:10:14 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:10:16 INFO Tokens consumed: 2970 2025-01-22 17:10:18 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:10:19 INFO token updated successfully: 2025-01 2025-01-22 17:10:19 INFO token updated successfully. 2025-01-22 17:10:19 INFO Connected to the database MHealth_Dev. 2025-01-22 17:10:19 INFO Query executed successfully. 2025-01-22 17:10:19 INFO Connected to the database MHealth_Dev. 2025-01-22 17:10:19 INFO Query executed successfully. 2025-01-22 17:10:20 ERROR Error processing request: There are multiple elements with the same `key='1'`. To fix this, please make sure that the `key` argument is unique for each element you create. 2025-01-22 17:12:15 INFO Date: 2025-01-22 ======================================== Time: 17:12:15 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 17:12:20 INFO not logined 2025-01-22 17:12:20 INFO Rendering unauthenticated menu. 2025-01-22 17:15:04 INFO Login button clicked. 2025-01-22 17:15:08 INFO Login successful for user: maheshsr 2025-01-22 17:15:18 INFO Database names fetched successfully. 2025-01-22 17:16:01 INFO Database names fetched successfully. 2025-01-22 17:16:02 INFO Table details fetched successfully. 2025-01-22 17:16:34 INFO Database names fetched successfully. 2025-01-22 17:16:34 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:16:36 INFO Tokens consumed: 2970 2025-01-22 17:16:38 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:16:39 INFO token updated successfully: 2025-01 2025-01-22 17:16:39 INFO token updated successfully. 2025-01-22 17:16:39 INFO Connected to the database MHealth_Dev. 2025-01-22 17:16:39 INFO Query executed successfully. 2025-01-22 17:16:39 INFO Connected to the database MHealth_Dev. 2025-01-22 17:16:39 INFO Query executed successfully. 2025-01-22 17:17:06 INFO Database names fetched successfully. 2025-01-22 17:19:17 INFO Database names fetched successfully. 2025-01-22 17:19:17 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:19:19 INFO Tokens consumed: 2970 2025-01-22 17:19:21 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:19:22 INFO token updated successfully: 2025-01 2025-01-22 17:19:22 INFO token updated successfully. 2025-01-22 17:19:22 INFO Connected to the database MHealth_Dev. 2025-01-22 17:19:22 INFO Query executed successfully. 2025-01-22 17:19:22 INFO Connected to the database MHealth_Dev. 2025-01-22 17:19:22 INFO Query executed successfully. 2025-01-22 17:19:29 INFO Database names fetched successfully. 2025-01-22 17:19:42 INFO Database names fetched successfully. 2025-01-22 17:19:42 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 17:19:44 INFO Tokens consumed: 2970 2025-01-22 17:19:46 INFO Existing token_consumed found for month: 2025-01 2025-01-22 17:19:47 INFO token updated successfully: 2025-01 2025-01-22 17:19:47 INFO token updated successfully. 2025-01-22 17:19:47 INFO Connected to the database MHealth_Dev. 2025-01-22 17:19:47 INFO Query executed successfully. 2025-01-22 17:19:47 INFO Connected to the database MHealth_Dev. 2025-01-22 17:19:47 INFO Query executed successfully. 2025-01-22 17:20:34 INFO Database names fetched successfully. 2025-01-22 20:26:19 INFO Date: 2025-01-22 ======================================== Time: 20:26:19 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-22 20:26:24 INFO not logined 2025-01-22 20:26:24 INFO Rendering unauthenticated menu. 2025-01-22 20:27:08 INFO Login button clicked. 2025-01-22 20:27:12 INFO Login successful for user: abhishek 2025-01-22 20:27:26 INFO Database names fetched successfully. 2025-01-22 20:27:54 INFO Database names fetched successfully. 2025-01-22 20:27:54 INFO Table details fetched successfully. 2025-01-22 20:28:29 INFO Database names fetched successfully. 2025-01-22 20:28:29 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 20:28:32 INFO Tokens consumed: 2970 2025-01-22 20:28:34 INFO Existing token_consumed found for month: 2025-01 2025-01-22 20:28:35 INFO token updated successfully: 2025-01 2025-01-22 20:28:35 INFO token updated successfully. 2025-01-22 20:28:35 INFO Connected to the database MHealth_Dev. 2025-01-22 20:28:35 INFO Query executed successfully. 2025-01-22 20:28:37 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 29 2025-01-22 20:28:39 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-22 20:28:40 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/30.json 2025-01-22 20:29:05 INFO Database names fetched successfully. 2025-01-22 20:29:28 INFO Database names fetched successfully. 2025-01-22 20:29:28 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 20:29:31 INFO Tokens consumed: 2970 2025-01-22 20:29:33 INFO Existing token_consumed found for month: 2025-01 2025-01-22 20:29:34 INFO token updated successfully: 2025-01 2025-01-22 20:29:34 INFO token updated successfully. 2025-01-22 20:29:34 INFO Connected to the database MHealth_Dev. 2025-01-22 20:29:34 INFO Query executed successfully. 2025-01-22 20:29:36 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 30 2025-01-22 20:29:38 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-22 20:29:39 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/31.json 2025-01-22 20:30:07 INFO Database names fetched successfully. 2025-01-22 20:33:00 INFO Database names fetched successfully. 2025-01-22 20:33:00 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-22 20:33:16 INFO Tokens consumed: 2970 2025-01-22 20:33:18 INFO Existing token_consumed found for month: 2025-01 2025-01-22 20:33:19 INFO token updated successfully: 2025-01 2025-01-22 20:33:19 INFO token updated successfully. 2025-01-22 20:33:19 INFO Connected to the database MHealth_Dev. 2025-01-22 20:33:19 INFO Query executed successfully. 2025-01-22 20:33:20 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 31 2025-01-22 20:33:22 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-22 20:33:23 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/32.json 2025-01-22 22:29:15 INFO Database names fetched successfully. 2025-01-22 22:31:16 INFO Database names fetched successfully. 2025-01-22 22:31:16 INFO Table details fetched successfully. 2025-01-22 22:31:19 INFO Database names fetched successfully. 2025-01-22 22:31:19 INFO Table details fetched successfully. 2025-01-23 09:49:45 INFO Date: 2025-01-23 ======================================== Time: 09:49:45 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 09:49:45 INFO Date: 2025-01-23 ======================================== Time: 09:49:45 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 09:49:45 INFO not logined 2025-01-23 09:49:45 INFO not logined 2025-01-23 09:49:45 INFO Rendering unauthenticated menu. 2025-01-23 09:49:45 INFO Rendering unauthenticated menu. 2025-01-23 09:50:13 INFO Login button clicked. 2025-01-23 09:50:13 INFO Login button clicked. 2025-01-23 09:50:17 INFO Login successful for user: abhishek 2025-01-23 09:50:17 INFO Login successful for user: abhishek 2025-01-23 09:50:20 INFO Database names fetched successfully. 2025-01-23 09:50:20 INFO Database names fetched successfully. 2025-01-23 09:50:58 INFO Database names fetched successfully. 2025-01-23 09:50:58 INFO Database names fetched successfully. 2025-01-23 09:50:58 INFO Table details fetched successfully. 2025-01-23 09:50:58 INFO Table details fetched successfully. 2025-01-23 09:54:41 INFO Database names fetched successfully. 2025-01-23 09:54:41 INFO Database names fetched successfully. 2025-01-23 09:54:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 09:54:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 09:54:43 INFO Tokens consumed: 2970 2025-01-23 09:54:43 INFO Tokens consumed: 2970 2025-01-23 09:54:44 INFO Existing token_consumed found for month: 2025-01 2025-01-23 09:54:44 INFO Existing token_consumed found for month: 2025-01 2025-01-23 09:54:45 INFO token updated successfully: 2025-01 2025-01-23 09:54:45 INFO token updated successfully: 2025-01 2025-01-23 09:54:45 INFO token updated successfully. 2025-01-23 09:54:45 INFO token updated successfully. 2025-01-23 09:54:45 INFO Connected to the database MHealth_Dev. 2025-01-23 09:54:45 INFO Connected to the database MHealth_Dev. 2025-01-23 09:54:45 INFO Query executed successfully. 2025-01-23 09:54:45 INFO Query executed successfully. 2025-01-23 09:54:47 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 32 2025-01-23 09:54:47 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 32 2025-01-23 09:54:48 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 09:54:48 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 09:54:49 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/33.json 2025-01-23 09:54:49 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/33.json 2025-01-23 10:03:40 INFO Database names fetched successfully. 2025-01-23 10:03:40 INFO Database names fetched successfully. 2025-01-23 10:29:14 INFO Database names fetched successfully. 2025-01-23 10:29:14 INFO Database names fetched successfully. 2025-01-23 10:29:22 INFO Database names fetched successfully. 2025-01-23 10:29:22 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 10:29:22 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 10:29:24 INFO Tokens consumed: 2970 2025-01-23 10:29:26 INFO Existing token_consumed found for month: 2025-01 2025-01-23 10:29:26 INFO Existing token_consumed found for month: 2025-01 2025-01-23 10:29:27 INFO token updated successfully: 2025-01 2025-01-23 10:29:27 INFO token updated successfully: 2025-01 2025-01-23 10:29:27 INFO token updated successfully. 2025-01-23 10:29:27 INFO token updated successfully. 2025-01-23 10:29:27 INFO Connected to the database MHealth_Dev. 2025-01-23 10:29:27 INFO Connected to the database MHealth_Dev. 2025-01-23 10:29:27 INFO Query executed successfully. 2025-01-23 10:29:27 INFO Query executed successfully. 2025-01-23 10:29:29 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 33 2025-01-23 10:29:29 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 33 2025-01-23 10:29:30 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 10:29:30 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 10:29:32 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/34.json 2025-01-23 10:29:32 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/34.json 2025-01-23 10:29:37 INFO Database names fetched successfully. 2025-01-23 10:29:37 INFO Database names fetched successfully. 2025-01-23 10:32:15 INFO Date: 2025-01-23 ======================================== Time: 10:32:15 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 10:32:19 INFO not logined 2025-01-23 10:32:19 INFO Rendering unauthenticated menu. 2025-01-23 10:33:14 INFO Login button clicked. 2025-01-23 10:33:17 INFO Login successful for user: abhishek 2025-01-23 10:33:26 INFO Database names fetched successfully. 2025-01-23 10:36:29 INFO Database names fetched successfully. 2025-01-23 10:36:30 INFO Table details fetched successfully. 2025-01-23 10:37:02 INFO Database names fetched successfully. 2025-01-23 10:37:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 10:37:05 INFO Tokens consumed: 2970 2025-01-23 10:37:06 INFO Existing token_consumed found for month: 2025-01 2025-01-23 10:37:07 INFO token updated successfully: 2025-01 2025-01-23 10:37:07 INFO token updated successfully. 2025-01-23 10:37:08 INFO Connected to the database MHealth_Dev. 2025-01-23 10:37:08 INFO Query executed successfully. 2025-01-23 10:37:09 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 34 2025-01-23 10:37:11 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 10:37:12 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/35.json 2025-01-23 10:37:35 INFO Database names fetched successfully. 2025-01-23 10:43:38 INFO Database names fetched successfully. 2025-01-23 10:43:44 INFO Database names fetched successfully. 2025-01-23 10:43:48 INFO Database names fetched successfully. 2025-01-23 10:43:48 INFO Table details fetched successfully. 2025-01-23 10:44:53 INFO Database names fetched successfully. 2025-01-23 10:44:53 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'patient_sdoh_scores': 'Insightlab - patient_sdoh_scores - Table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicates a score about one patient and one type of assessment.', 'EpisodeOfCare': 'Insightlab - EpisodeOfCare - Contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episode of care for a patient. One patient may have multiple episodes of care.', 'RiskScore': 'Insightlab - RiskScore - Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has a risk score of a unique patient.', 'Patient': 'Insightlab - Patient - The table stores the healthcare encounter information about patients. Each row has unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Insightlab - Encounter - Table that stores all encounters of each patient with the healthcare providers. Every row indicates a single encounter.'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Encounter': [{'name': 'id', 'type': 'nvarchar', 'description': 'Encounter id that identifies an encounter uniquely'}, {'name': 'status', 'type': 'nvarchar', 'description': "Encounter status, can be one of 'planned', 'completed', 'discharged', 'in-progress'"}, {'name': 'class', 'type': 'nvarchar', 'description': "Indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health"}, {'name': 'priority', 'type': 'nvarchar', 'description': "Indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine"}, {'name': 'subject_id', 'type': 'nvarchar', 'description': 'Indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table'}, {'name': 'subject_status', 'type': 'nvarchar', 'description': ''}, {'name': 'actual_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'actual_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_start_date', 'type': 'datetime2', 'description': ''}, {'name': 'planned_end_date', 'type': 'datetime2', 'description': ''}, {'name': 'length', 'type': 'smallint', 'description': ''}, {'name': 'service_provider_id', 'type': 'nvarchar', 'description': 'Contains the id of the care delivery organization where the patient had the encounter'}, {'name': 'part_of_encounter_id', 'type': 'nvarchar', 'description': ''}, {'name': 'appointment_id', 'type': 'nvarchar', 'description': ''}, {'name': 'participant_actor_id', 'type': 'nvarchar', 'description': 'Contains the id of the provider associated with the care delivery organization who rendered the encounter'}, {'name': 'participant_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'participant_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'Contains list of diagnosis codes relevant to the patient of the encounter'}, {'name': 'diagnosis_condition_text', 'type': 'nvarchar', 'description': ''}, {'name': 'condition_class', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'location_id', 'type': 'nvarchar', 'description': 'Location where the encounter happened or is happening or will be happening'}, {'name': 'location_status', 'type': 'nvarchar', 'description': ''}, {'name': 'location_form', 'type': 'nvarchar', 'description': ''}, {'name': 'location_period_start', 'type': 'datetime2', 'description': ''}, {'name': 'location_period_end', 'type': 'datetime2', 'description': ''}, {'name': 'origin_id', 'type': 'nvarchar', 'description': ''}, {'name': 'admit_source', 'type': 'nvarchar', 'description': ''}, {'name': 're_admission', 'type': 'nvarchar', 'description': ''}, {'name': 'destination_id', 'type': 'nvarchar', 'description': ''}, {'name': 'discharge_disposition', 'type': 'nvarchar', 'description': 'How the patient was discharged at the end of the encounter'}], 'EpisodeOfCare': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Unique identifier of the episode'}, {'name': 'status', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'status_history_period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'type', 'type': 'nvarchar', 'description': 'Type of episode, can be disease management, post acute care or specialist referral'}, {'name': 'reason_use', 'type': 'nvarchar', 'description': ''}, {'name': 'reason_value_reference', 'type': 'nvarchar', 'description': ''}, {'name': 'diagnosis_condition_id', 'type': 'nvarchar', 'description': 'ICD-10 diagnosis code associated with the episode of care'}, {'name': 'diagnosis_use', 'type': 'nvarchar', 'description': ''}, {'name': 'subject_id', 'type': 'nvarchar', 'description': "ID of the patient associated with the episode, should have a corresponding 'identifier_value' in the Patient table"}, {'name': 'managing_organization_id', 'type': 'nvarchar', 'description': 'Contains the ID of the organization managing the episode'}, {'name': 'period_start', 'type': 'nvarchar', 'description': ''}, {'name': 'period_end', 'type': 'nvarchar', 'description': ''}, {'name': 'referral_request_id', 'type': 'nvarchar', 'description': ''}, {'name': 'care_manager_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care manager managing the episode'}, {'name': 'care_team_id', 'type': 'nvarchar', 'description': 'Contains the ID of the care team managing the episode. Care manager is part of the care team'}, {'name': 'account_id', 'type': 'nvarchar', 'description': ''}], 'Patient': [{'name': 'id', 'type': 'nvarchar', 'description': ''}, {'name': 'identifier_value', 'type': 'nvarchar', 'description': 'Patient identifier that uniquely identifies patient and links a patient from this to other tables'}, {'name': 'identifier_use', 'type': 'nvarchar', 'description': 'If the identifier is used for any specific purpose'}, {'name': 'identifier_type', 'type': 'nvarchar', 'description': 'Type of identifier, usually means the source. MR stands for medical record'}, {'name': 'identifier_start_date', 'type': 'date', 'description': 'Date on since when the identifier was valid'}, {'name': 'identifier_assigner', 'type': 'nvarchar', 'description': 'Identification value assignment authority'}, {'name': 'active', 'type': 'nvarchar', 'description': 'If the patient is active or not'}, {'name': 'official_name_family', 'type': 'nvarchar', 'description': 'Family name of the patient'}, {'name': 'official_name_given', 'type': 'nvarchar', 'description': 'Given name of the patient'}, {'name': 'usual_name_given', 'type': 'nvarchar', 'description': 'Short form of the given name'}, {'name': 'gender', 'type': 'nvarchar', 'description': "Patient's gender, male or female"}, {'name': 'birth_date', 'type': 'date', 'description': 'Date of birth of the patient'}, {'name': 'Age', 'type': 'tinyint', 'description': 'Patient age'}, {'name': 'home_address_line', 'type': 'nvarchar', 'description': "Patient's home address street"}, {'name': 'home_address_city', 'type': 'nvarchar', 'description': "Patient's home address city"}, {'name': 'home_address_district', 'type': 'nvarchar', 'description': "Patient's home county"}, {'name': 'home_address_state', 'type': 'nvarchar', 'description': "Patient's home state"}, {'name': 'home_address_postalCode', 'type': 'smallint', 'description': "Patient's home address zip code"}, {'name': 'home_address_period_start', 'type': 'date', 'description': "Start date of the patient's home address"}], 'RiskScore': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Identifier that uniquely identifies a patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'risk_score', 'type': 'float', 'description': 'Decimal number between 0 and 1 indicating the risk score'}, {'name': 'risk_score_date', 'type': 'nvarchar', 'description': ''}], 'patient_sdoh_scores': [{'name': 'patient_id', 'type': 'nvarchar', 'description': 'Unique identifier of the patient. Matches with at least one identifier_value of the Patient table.'}, {'name': 'assessment_id', 'type': 'nvarchar', 'description': 'Name of the assessment'}, {'name': 'answer', 'type': 'tinyint', 'description': 'The actual answer provided in the assessment'}, {'name': 'assessment_type', 'type': 'nvarchar', 'description': "Type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'"}, {'name': 'score', 'type': 'float', 'description': 'Derived standardized score based on the answer provided'}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the patient```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 10:44:55 INFO Tokens consumed: 2525 2025-01-23 10:44:56 INFO Existing token_consumed found for month: 2025-01 2025-01-23 10:44:57 INFO token updated successfully: 2025-01 2025-01-23 10:44:57 INFO token updated successfully. 2025-01-23 10:44:57 INFO Connected to the database Insightlab. 2025-01-23 10:44:57 INFO Query executed successfully. 2025-01-23 10:44:59 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 35 2025-01-23 10:45:00 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 10:45:01 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/36.json 2025-01-23 10:45:10 INFO Database names fetched successfully. 2025-01-23 10:45:10 INFO Table details fetched successfully. 2025-01-23 10:45:15 INFO Database names fetched successfully. 2025-01-23 10:45:15 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 10:45:17 INFO Tokens consumed: 2970 2025-01-23 10:45:18 INFO Existing token_consumed found for month: 2025-01 2025-01-23 10:45:19 INFO token updated successfully: 2025-01 2025-01-23 10:45:19 INFO token updated successfully. 2025-01-23 10:45:19 INFO Connected to the database MHealth_Dev. 2025-01-23 10:45:19 INFO Query executed successfully. 2025-01-23 10:45:21 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 36 2025-01-23 10:45:22 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 10:45:23 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/37.json 2025-01-23 10:45:28 INFO Database names fetched successfully. 2025-01-23 10:47:18 INFO Date: 2025-01-23 ======================================== Time: 10:47:18 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 10:47:22 INFO not logined 2025-01-23 10:47:22 INFO Rendering unauthenticated menu. 2025-01-23 10:47:37 INFO Login button clicked. 2025-01-23 10:47:40 INFO Login successful for user: abhishek 2025-01-23 10:47:47 INFO Database names fetched successfully. 2025-01-23 10:48:03 INFO Database names fetched successfully. 2025-01-23 10:48:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-23 10:48:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-23 10:48:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-23 10:48:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-23 10:48:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-23 10:48:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-23 10:48:48 INFO Database names fetched successfully. 2025-01-23 10:49:50 INFO Date: 2025-01-23 ======================================== Time: 10:49:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 10:49:50 INFO Date: 2025-01-23 ======================================== Time: 10:49:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 10:49:50 INFO not logined 2025-01-23 10:49:50 INFO not logined 2025-01-23 10:49:50 INFO Rendering unauthenticated menu. 2025-01-23 10:49:50 INFO Rendering unauthenticated menu. 2025-01-23 10:50:15 INFO Login button clicked. 2025-01-23 10:50:15 INFO Login button clicked. 2025-01-23 10:50:18 INFO Login successful for user: abhishek 2025-01-23 10:50:18 INFO Login successful for user: abhishek 2025-01-23 10:50:19 INFO Database names fetched successfully. 2025-01-23 10:52:38 INFO Date: 2025-01-23 ======================================== Time: 10:52:38 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 10:52:43 INFO not logined 2025-01-23 10:52:43 INFO Rendering unauthenticated menu. 2025-01-23 10:53:04 INFO Login button clicked. 2025-01-23 10:53:08 INFO Login successful for user: abhishek 2025-01-23 10:53:17 INFO Database names fetched successfully. 2025-01-23 11:07:50 INFO Date: 2025-01-23 ======================================== Time: 11:07:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:07:55 INFO not logined 2025-01-23 11:07:55 INFO Rendering unauthenticated menu. 2025-01-23 11:08:14 INFO Login button clicked. 2025-01-23 11:08:17 INFO Login successful for user: abhishek 2025-01-23 11:08:27 INFO Database names fetched successfully. 2025-01-23 11:09:14 INFO Database names fetched successfully. 2025-01-23 11:09:15 INFO Table details fetched successfully. 2025-01-23 11:09:38 INFO Database names fetched successfully. 2025-01-23 11:09:38 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:09:40 INFO Tokens consumed: 2970 2025-01-23 11:09:41 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:09:42 INFO token updated successfully: 2025-01 2025-01-23 11:09:42 INFO token updated successfully. 2025-01-23 11:09:42 INFO Connected to the database MHealth_Dev. 2025-01-23 11:09:42 INFO Query executed successfully. 2025-01-23 11:09:58 INFO Database names fetched successfully. 2025-01-23 11:10:07 INFO Database names fetched successfully. 2025-01-23 11:10:11 INFO Database names fetched successfully. 2025-01-23 11:10:18 INFO Database names fetched successfully. 2025-01-23 11:10:20 INFO Database names fetched successfully. 2025-01-23 11:10:35 INFO Database names fetched successfully. 2025-01-23 11:10:36 INFO Database names fetched successfully. 2025-01-23 11:10:42 INFO Database names fetched successfully. 2025-01-23 11:10:44 INFO Database names fetched successfully. 2025-01-23 11:10:46 INFO Database names fetched successfully. 2025-01-23 11:10:50 INFO Database names fetched successfully. 2025-01-23 11:10:54 INFO Database names fetched successfully. 2025-01-23 11:10:58 INFO Database names fetched successfully. 2025-01-23 11:11:10 INFO Database names fetched successfully. 2025-01-23 11:23:00 INFO Date: 2025-01-23 ======================================== Time: 11:23:00 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:23:05 INFO not logined 2025-01-23 11:23:05 INFO Rendering unauthenticated menu. 2025-01-23 11:25:45 INFO Login button clicked. 2025-01-23 11:25:48 INFO Login successful for user: abhishek 2025-01-23 11:25:58 INFO Database names fetched successfully. 2025-01-23 11:26:48 INFO Database names fetched successfully. 2025-01-23 11:26:48 INFO Table details fetched successfully. 2025-01-23 11:27:09 INFO Database names fetched successfully. 2025-01-23 11:27:09 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:27:11 INFO Tokens consumed: 2970 2025-01-23 11:27:12 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:27:13 INFO token updated successfully: 2025-01 2025-01-23 11:27:13 INFO token updated successfully. 2025-01-23 11:27:13 INFO Connected to the database MHealth_Dev. 2025-01-23 11:27:13 INFO Query executed successfully. 2025-01-23 11:28:27 INFO Database names fetched successfully. 2025-01-23 11:28:27 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:28:33 INFO Tokens consumed: 2970 2025-01-23 11:28:35 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:28:36 INFO token updated successfully: 2025-01 2025-01-23 11:28:36 INFO token updated successfully. 2025-01-23 11:28:36 INFO Connected to the database MHealth_Dev. 2025-01-23 11:28:36 INFO Query executed successfully. 2025-01-23 11:28:50 INFO Database names fetched successfully. 2025-01-23 11:29:03 INFO Database names fetched successfully. 2025-01-23 11:29:06 INFO Database names fetched successfully. 2025-01-23 11:29:24 INFO Date: 2025-01-23 ======================================== Time: 11:29:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:29:24 INFO Date: 2025-01-23 ======================================== Time: 11:29:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:29:24 INFO not logined 2025-01-23 11:29:24 INFO not logined 2025-01-23 11:29:24 INFO Rendering unauthenticated menu. 2025-01-23 11:29:24 INFO Rendering unauthenticated menu. 2025-01-23 11:29:44 INFO Login button clicked. 2025-01-23 11:29:47 INFO Login successful for user: abhishek 2025-01-23 11:29:47 INFO Login successful for user: abhishek 2025-01-23 11:29:48 INFO Database names fetched successfully. 2025-01-23 11:29:48 INFO Database names fetched successfully. 2025-01-23 11:29:53 INFO Database names fetched successfully. 2025-01-23 11:29:53 INFO Database names fetched successfully. 2025-01-23 11:29:53 INFO Table details fetched successfully. 2025-01-23 11:30:02 INFO Database names fetched successfully. 2025-01-23 11:30:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:30:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:30:04 INFO Tokens consumed: 2970 2025-01-23 11:30:04 INFO Tokens consumed: 2970 2025-01-23 11:30:05 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:30:05 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:30:06 INFO token updated successfully: 2025-01 2025-01-23 11:30:06 INFO token updated successfully: 2025-01 2025-01-23 11:30:06 INFO token updated successfully. 2025-01-23 11:30:06 INFO token updated successfully. 2025-01-23 11:30:06 INFO Connected to the database MHealth_Dev. 2025-01-23 11:30:06 INFO Query executed successfully. 2025-01-23 11:38:53 INFO Date: 2025-01-23 ======================================== Time: 11:38:53 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:38:53 INFO Date: 2025-01-23 ======================================== Time: 11:38:53 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:38:53 INFO not logined 2025-01-23 11:38:53 INFO not logined 2025-01-23 11:38:53 INFO Rendering unauthenticated menu. 2025-01-23 11:38:53 INFO Rendering unauthenticated menu. 2025-01-23 11:39:14 INFO Login button clicked. 2025-01-23 11:39:14 INFO Login button clicked. 2025-01-23 11:39:14 INFO Login button clicked. 2025-01-23 11:39:17 INFO Login successful for user: abhishek 2025-01-23 11:39:17 INFO Login successful for user: abhishek 2025-01-23 11:39:17 INFO Login successful for user: abhishek 2025-01-23 11:39:19 INFO Database names fetched successfully. 2025-01-23 11:39:19 INFO Database names fetched successfully. 2025-01-23 11:39:19 INFO Database names fetched successfully. 2025-01-23 11:39:23 INFO Database names fetched successfully. 2025-01-23 11:39:23 INFO Database names fetched successfully. 2025-01-23 11:39:23 INFO Table details fetched successfully. 2025-01-23 11:39:23 INFO Table details fetched successfully. 2025-01-23 11:39:30 INFO Database names fetched successfully. 2025-01-23 11:39:30 INFO Database names fetched successfully. 2025-01-23 11:39:30 INFO Database names fetched successfully. 2025-01-23 11:39:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appoinments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:39:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appoinments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:39:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appoinments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:39:32 INFO Tokens consumed: 2972 2025-01-23 11:39:32 INFO Tokens consumed: 2972 2025-01-23 11:39:33 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:39:33 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:39:33 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:39:35 INFO token updated successfully: 2025-01 2025-01-23 11:39:35 INFO token updated successfully: 2025-01 2025-01-23 11:39:35 INFO token updated successfully: 2025-01 2025-01-23 11:39:35 INFO token updated successfully. 2025-01-23 11:39:35 INFO token updated successfully. 2025-01-23 11:39:35 INFO token updated successfully. 2025-01-23 11:39:35 INFO Connected to the database MHealth_Dev. 2025-01-23 11:39:35 INFO Connected to the database MHealth_Dev. 2025-01-23 11:39:35 INFO Connected to the database MHealth_Dev. 2025-01-23 11:39:35 INFO Query executed successfully. 2025-01-23 11:39:35 INFO Query executed successfully. 2025-01-23 11:39:35 INFO Query executed successfully. 2025-01-23 11:41:08 INFO Database names fetched successfully. 2025-01-23 11:41:08 INFO Database names fetched successfully. 2025-01-23 11:41:08 INFO Database names fetched successfully. 2025-01-23 11:41:21 INFO Database names fetched successfully. 2025-01-23 11:41:21 INFO Database names fetched successfully. 2025-01-23 11:41:21 INFO Table details fetched successfully. 2025-01-23 11:41:21 INFO Table details fetched successfully. 2025-01-23 11:41:21 INFO Table details fetched successfully. 2025-01-23 11:41:29 INFO Database names fetched successfully. 2025-01-23 11:41:29 INFO Database names fetched successfully. 2025-01-23 11:41:29 INFO Table details fetched successfully. 2025-01-23 11:41:29 INFO Table details fetched successfully. 2025-01-23 11:41:34 INFO Database names fetched successfully. 2025-01-23 11:41:34 INFO Database names fetched successfully. 2025-01-23 11:41:34 INFO Database names fetched successfully. 2025-01-23 11:41:34 INFO Metadata fetched for table: NewAppointment 2025-01-23 11:41:34 INFO Metadata fetched for table: NewAppointment 2025-01-23 11:41:46 INFO Database names fetched successfully. 2025-01-23 11:41:46 INFO Database names fetched successfully. 2025-01-23 11:41:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:41:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:41:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:41:48 INFO Tokens consumed: 2970 2025-01-23 11:41:48 INFO Tokens consumed: 2970 2025-01-23 11:41:48 INFO Tokens consumed: 2970 2025-01-23 11:41:49 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:41:49 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:41:49 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:41:50 INFO token updated successfully: 2025-01 2025-01-23 11:41:50 INFO token updated successfully: 2025-01 2025-01-23 11:41:50 INFO token updated successfully: 2025-01 2025-01-23 11:41:50 INFO token updated successfully. 2025-01-23 11:41:50 INFO token updated successfully. 2025-01-23 11:41:50 INFO token updated successfully. 2025-01-23 11:41:50 INFO Connected to the database MHealth_Dev. 2025-01-23 11:41:50 INFO Connected to the database MHealth_Dev. 2025-01-23 11:41:50 INFO Connected to the database MHealth_Dev. 2025-01-23 11:41:50 INFO Query executed successfully. 2025-01-23 11:41:50 INFO Query executed successfully. 2025-01-23 11:43:56 INFO Date: 2025-01-23 ======================================== Time: 11:43:56 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:44:01 INFO not logined 2025-01-23 11:44:01 INFO Rendering unauthenticated menu. 2025-01-23 11:44:16 INFO Login button clicked. 2025-01-23 11:44:19 INFO Login successful for user: abhishek 2025-01-23 11:44:28 INFO Database names fetched successfully. 2025-01-23 11:44:56 INFO Database names fetched successfully. 2025-01-23 11:44:56 INFO Table details fetched successfully. 2025-01-23 11:45:26 INFO Database names fetched successfully. 2025-01-23 11:45:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:45:28 INFO Tokens consumed: 2970 2025-01-23 11:45:30 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:45:31 INFO token updated successfully: 2025-01 2025-01-23 11:45:31 INFO token updated successfully. 2025-01-23 11:45:31 INFO Connected to the database MHealth_Dev. 2025-01-23 11:45:31 INFO Query executed successfully. 2025-01-23 11:46:13 INFO Database names fetched successfully. 2025-01-23 11:48:19 INFO Database names fetched successfully. 2025-01-23 11:48:25 INFO Date: 2025-01-23 ======================================== Time: 11:48:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:48:25 INFO Date: 2025-01-23 ======================================== Time: 11:48:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:48:25 INFO not logined 2025-01-23 11:48:25 INFO not logined 2025-01-23 11:48:25 INFO Rendering unauthenticated menu. 2025-01-23 11:48:25 INFO Rendering unauthenticated menu. 2025-01-23 11:48:45 INFO Login button clicked. 2025-01-23 11:48:45 INFO Login button clicked. 2025-01-23 11:48:48 INFO Login successful for user: abhishek 2025-01-23 11:48:48 INFO Login successful for user: abhishek 2025-01-23 11:48:49 INFO Database names fetched successfully. 2025-01-23 11:48:54 INFO Database names fetched successfully. 2025-01-23 11:48:54 INFO Database names fetched successfully. 2025-01-23 11:48:54 INFO Table details fetched successfully. 2025-01-23 11:48:54 INFO Table details fetched successfully. 2025-01-23 11:49:02 INFO Database names fetched successfully. 2025-01-23 11:49:02 INFO Database names fetched successfully. 2025-01-23 11:49:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:49:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:49:04 INFO Tokens consumed: 2970 2025-01-23 11:49:04 INFO Tokens consumed: 2970 2025-01-23 11:49:06 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:49:06 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:49:07 INFO token updated successfully: 2025-01 2025-01-23 11:49:07 INFO token updated successfully: 2025-01 2025-01-23 11:49:07 INFO token updated successfully. 2025-01-23 11:49:07 INFO token updated successfully. 2025-01-23 11:49:07 INFO Connected to the database MHealth_Dev. 2025-01-23 11:49:07 INFO Query executed successfully. 2025-01-23 11:49:07 INFO Query executed successfully. 2025-01-23 11:52:15 INFO Database names fetched successfully. 2025-01-23 11:52:15 INFO Database names fetched successfully. 2025-01-23 11:54:08 INFO Date: 2025-01-23 ======================================== Time: 11:54:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 11:54:11 INFO not logined 2025-01-23 11:54:11 INFO Rendering unauthenticated menu. 2025-01-23 11:54:31 INFO Login button clicked. 2025-01-23 11:54:34 INFO Login successful for user: abhishek 2025-01-23 11:54:42 INFO Database names fetched successfully. 2025-01-23 11:55:01 INFO Database names fetched successfully. 2025-01-23 11:55:01 INFO Table details fetched successfully. 2025-01-23 11:56:53 INFO Database names fetched successfully. 2025-01-23 11:56:53 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 11:56:54 INFO Tokens consumed: 2970 2025-01-23 11:56:56 INFO Existing token_consumed found for month: 2025-01 2025-01-23 11:56:57 INFO token updated successfully: 2025-01 2025-01-23 11:56:57 INFO token updated successfully. 2025-01-23 11:56:57 INFO Connected to the database MHealth_Dev. 2025-01-23 11:56:57 INFO Query executed successfully. 2025-01-23 11:57:18 INFO Database names fetched successfully. 2025-01-23 12:05:54 INFO Database names fetched successfully. 2025-01-23 12:05:59 INFO Database names fetched successfully. 2025-01-23 12:06:05 INFO Date: 2025-01-23 ======================================== Time: 12:06:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:06:05 INFO Date: 2025-01-23 ======================================== Time: 12:06:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:06:05 INFO not logined 2025-01-23 12:06:05 INFO Rendering unauthenticated menu. 2025-01-23 12:06:05 INFO Rendering unauthenticated menu. 2025-01-23 12:07:23 INFO Login button clicked. 2025-01-23 12:07:23 INFO Login button clicked. 2025-01-23 12:07:26 INFO Login successful for user: abhishek 2025-01-23 12:07:26 INFO Login successful for user: abhishek 2025-01-23 12:07:27 INFO Database names fetched successfully. 2025-01-23 12:07:45 INFO Database names fetched successfully. 2025-01-23 12:07:45 INFO Database names fetched successfully. 2025-01-23 12:07:45 INFO Table details fetched successfully. 2025-01-23 12:07:52 INFO Database names fetched successfully. 2025-01-23 12:07:52 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:07:52 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:07:54 INFO Tokens consumed: 2970 2025-01-23 12:07:55 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:07:56 INFO token updated successfully: 2025-01 2025-01-23 12:07:56 INFO token updated successfully: 2025-01 2025-01-23 12:07:56 INFO token updated successfully. 2025-01-23 12:07:56 INFO token updated successfully. 2025-01-23 12:07:56 INFO Connected to the database MHealth_Dev. 2025-01-23 12:07:56 INFO Connected to the database MHealth_Dev. 2025-01-23 12:07:56 INFO Query executed successfully. 2025-01-23 12:08:02 INFO Database names fetched successfully. 2025-01-23 12:08:02 INFO Database names fetched successfully. 2025-01-23 12:08:06 INFO Database names fetched successfully. 2025-01-23 12:08:06 INFO Database names fetched successfully. 2025-01-23 12:08:09 INFO Database names fetched successfully. 2025-01-23 12:08:20 INFO Database names fetched successfully. 2025-01-23 12:08:20 INFO Database names fetched successfully. 2025-01-23 12:08:23 INFO Database names fetched successfully. 2025-01-23 12:08:23 INFO Database names fetched successfully. 2025-01-23 12:10:43 INFO Date: 2025-01-23 ======================================== Time: 12:10:43 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:10:48 INFO not logined 2025-01-23 12:10:48 INFO Rendering unauthenticated menu. 2025-01-23 12:11:09 INFO Login button clicked. 2025-01-23 12:11:12 INFO Login successful for user: abhishek 2025-01-23 12:11:21 INFO Database names fetched successfully. 2025-01-23 12:11:37 INFO Database names fetched successfully. 2025-01-23 12:11:37 INFO Table details fetched successfully. 2025-01-23 12:12:04 INFO Database names fetched successfully. 2025-01-23 12:12:04 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:12:06 INFO Tokens consumed: 2970 2025-01-23 12:12:07 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:12:08 INFO token updated successfully: 2025-01 2025-01-23 12:12:08 INFO token updated successfully. 2025-01-23 12:12:08 INFO Connected to the database MHealth_Dev. 2025-01-23 12:12:08 INFO Query executed successfully. 2025-01-23 12:12:31 INFO Database names fetched successfully. 2025-01-23 12:12:38 INFO Database names fetched successfully. 2025-01-23 12:13:02 INFO Database names fetched successfully. 2025-01-23 12:13:04 INFO Database names fetched successfully. 2025-01-23 12:13:11 INFO Database names fetched successfully. 2025-01-23 12:13:13 INFO Database names fetched successfully. 2025-01-23 12:13:16 INFO Database names fetched successfully. 2025-01-23 12:13:18 INFO Database names fetched successfully. 2025-01-23 12:13:24 INFO Database names fetched successfully. 2025-01-23 12:13:27 INFO Database names fetched successfully. 2025-01-23 12:13:29 INFO Database names fetched successfully. 2025-01-23 12:13:36 INFO Database names fetched successfully. 2025-01-23 12:13:42 INFO Database names fetched successfully. 2025-01-23 12:15:36 INFO Database names fetched successfully. 2025-01-23 12:15:41 INFO Date: 2025-01-23 ======================================== Time: 12:15:41 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:15:41 INFO Date: 2025-01-23 ======================================== Time: 12:15:41 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:15:41 INFO not logined 2025-01-23 12:15:41 INFO not logined 2025-01-23 12:15:41 INFO Rendering unauthenticated menu. 2025-01-23 12:15:41 INFO Rendering unauthenticated menu. 2025-01-23 12:16:06 INFO Login button clicked. 2025-01-23 12:16:06 INFO Login button clicked. 2025-01-23 12:16:09 INFO Login successful for user: abhishek 2025-01-23 12:16:09 INFO Login successful for user: abhishek 2025-01-23 12:16:10 INFO Database names fetched successfully. 2025-01-23 12:16:13 INFO Database names fetched successfully. 2025-01-23 12:16:13 INFO Database names fetched successfully. 2025-01-23 12:16:14 INFO Table details fetched successfully. 2025-01-23 12:16:14 INFO Table details fetched successfully. 2025-01-23 12:16:20 INFO Database names fetched successfully. 2025-01-23 12:16:20 INFO Database names fetched successfully. 2025-01-23 12:16:20 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:16:20 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:16:22 INFO Tokens consumed: 2970 2025-01-23 12:16:22 INFO Tokens consumed: 2970 2025-01-23 12:16:23 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:16:23 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:16:24 INFO token updated successfully: 2025-01 2025-01-23 12:16:24 INFO token updated successfully: 2025-01 2025-01-23 12:16:24 INFO token updated successfully. 2025-01-23 12:16:24 INFO token updated successfully. 2025-01-23 12:16:24 INFO Connected to the database MHealth_Dev. 2025-01-23 12:16:24 INFO Query executed successfully. 2025-01-23 12:16:30 INFO Database names fetched successfully. 2025-01-23 12:16:30 INFO Database names fetched successfully. 2025-01-23 12:16:39 INFO Database names fetched successfully. 2025-01-23 12:16:39 INFO Database names fetched successfully. 2025-01-23 12:16:45 INFO Database names fetched successfully. 2025-01-23 12:16:47 INFO Database names fetched successfully. 2025-01-23 12:16:49 INFO Database names fetched successfully. 2025-01-23 12:16:50 INFO Database names fetched successfully. 2025-01-23 12:16:59 INFO Database names fetched successfully. 2025-01-23 12:16:59 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmetns```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:16:59 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmetns```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:17:00 INFO Tokens consumed: 2972 2025-01-23 12:17:00 INFO Tokens consumed: 2972 2025-01-23 12:17:02 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:17:02 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:17:03 INFO token updated successfully: 2025-01 2025-01-23 12:17:03 INFO token updated successfully: 2025-01 2025-01-23 12:17:03 INFO token updated successfully. 2025-01-23 12:17:03 INFO token updated successfully. 2025-01-23 12:17:03 INFO Connected to the database MHealth_Dev. 2025-01-23 12:17:03 INFO Query executed successfully. 2025-01-23 12:17:07 INFO Database names fetched successfully. 2025-01-23 12:17:07 INFO Database names fetched successfully. 2025-01-23 12:18:06 INFO Date: 2025-01-23 ======================================== Time: 12:18:06 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:18:06 INFO Date: 2025-01-23 ======================================== Time: 12:18:06 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:18:06 INFO not logined 2025-01-23 12:18:06 INFO not logined 2025-01-23 12:18:06 INFO not logined 2025-01-23 12:18:06 INFO Rendering unauthenticated menu. 2025-01-23 12:18:06 INFO Rendering unauthenticated menu. 2025-01-23 12:18:06 INFO Rendering unauthenticated menu. 2025-01-23 12:18:22 INFO Login button clicked. 2025-01-23 12:18:22 INFO Login button clicked. 2025-01-23 12:18:25 INFO Login successful for user: abhishek 2025-01-23 12:18:25 INFO Login successful for user: abhishek 2025-01-23 12:18:25 INFO Login successful for user: abhishek 2025-01-23 12:18:26 INFO Database names fetched successfully. 2025-01-23 12:18:26 INFO Database names fetched successfully. 2025-01-23 12:18:26 INFO Database names fetched successfully. 2025-01-23 12:50:13 INFO Database names fetched successfully. 2025-01-23 12:50:13 INFO Database names fetched successfully. 2025-01-23 12:50:13 INFO Database names fetched successfully. 2025-01-23 12:50:13 INFO Table details fetched successfully. 2025-01-23 12:50:13 INFO Table details fetched successfully. 2025-01-23 12:50:13 INFO Table details fetched successfully. 2025-01-23 12:50:43 INFO Database names fetched successfully. 2025-01-23 12:50:43 INFO Database names fetched successfully. 2025-01-23 12:50:43 INFO Database names fetched successfully. 2025-01-23 12:50:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:50:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:50:43 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:50:45 INFO Tokens consumed: 2970 2025-01-23 12:50:45 INFO Tokens consumed: 2970 2025-01-23 12:50:45 INFO Tokens consumed: 2970 2025-01-23 12:50:47 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:50:47 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:50:48 INFO token updated successfully: 2025-01 2025-01-23 12:50:48 INFO token updated successfully: 2025-01 2025-01-23 12:50:48 INFO token updated successfully: 2025-01 2025-01-23 12:50:48 INFO token updated successfully. 2025-01-23 12:50:48 INFO token updated successfully. 2025-01-23 12:50:48 INFO token updated successfully. 2025-01-23 12:50:48 INFO Connected to the database MHealth_Dev. 2025-01-23 12:50:48 INFO Connected to the database MHealth_Dev. 2025-01-23 12:50:48 INFO Connected to the database MHealth_Dev. 2025-01-23 12:50:48 INFO Query executed successfully. 2025-01-23 12:50:48 INFO Query executed successfully. 2025-01-23 12:50:48 INFO Query executed successfully. 2025-01-23 12:50:59 INFO Database names fetched successfully. 2025-01-23 12:50:59 INFO Database names fetched successfully. 2025-01-23 12:50:59 INFO Database names fetched successfully. 2025-01-23 12:55:51 INFO Date: 2025-01-23 ======================================== Time: 12:55:51 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 12:55:56 INFO not logined 2025-01-23 12:55:56 INFO Rendering unauthenticated menu. 2025-01-23 12:57:14 INFO Login button clicked. 2025-01-23 12:57:17 INFO Login successful for user: abhishek 2025-01-23 12:57:28 INFO Database names fetched successfully. 2025-01-23 12:58:02 INFO Database names fetched successfully. 2025-01-23 12:58:03 INFO Table details fetched successfully. 2025-01-23 12:58:29 INFO Database names fetched successfully. 2025-01-23 12:58:29 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 12:58:31 INFO Tokens consumed: 2970 2025-01-23 12:58:34 INFO Existing token_consumed found for month: 2025-01 2025-01-23 12:58:35 INFO token updated successfully: 2025-01 2025-01-23 12:58:35 INFO token updated successfully. 2025-01-23 12:58:35 INFO Connected to the database MHealth_Dev. 2025-01-23 12:58:35 INFO Query executed successfully. 2025-01-23 12:58:57 INFO Database names fetched successfully. 2025-01-23 12:59:08 INFO Database names fetched successfully. 2025-01-23 12:59:15 INFO Database names fetched successfully. 2025-01-23 12:59:17 INFO Database names fetched successfully. 2025-01-23 12:59:23 INFO Database names fetched successfully. 2025-01-23 12:59:26 INFO Database names fetched successfully. 2025-01-23 12:59:34 INFO Database names fetched successfully. 2025-01-23 12:59:36 INFO Database names fetched successfully. 2025-01-23 12:59:38 INFO Database names fetched successfully. 2025-01-23 12:59:41 INFO Database names fetched successfully. 2025-01-23 12:59:45 INFO Database names fetched successfully. 2025-01-23 12:59:45 INFO Database names fetched successfully. 2025-01-23 12:59:49 INFO Database names fetched successfully. 2025-01-23 12:59:51 INFO Database names fetched successfully. 2025-01-23 13:00:36 INFO Database names fetched successfully. 2025-01-23 13:00:36 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 13:00:38 INFO Tokens consumed: 2968 2025-01-23 13:00:39 INFO Existing token_consumed found for month: 2025-01 2025-01-23 13:00:40 INFO token updated successfully: 2025-01 2025-01-23 13:00:40 INFO token updated successfully. 2025-01-23 13:00:40 INFO Connected to the database MHealth_Dev. 2025-01-23 13:00:40 INFO Query executed successfully. 2025-01-23 13:00:59 INFO Database names fetched successfully. 2025-01-23 13:04:42 INFO Database names fetched successfully. 2025-01-23 13:04:50 INFO Date: 2025-01-23 ======================================== Time: 13:04:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:04:50 INFO Date: 2025-01-23 ======================================== Time: 13:04:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:04:50 INFO not logined 2025-01-23 13:04:50 INFO not logined 2025-01-23 13:04:50 INFO Rendering unauthenticated menu. 2025-01-23 13:04:50 INFO Rendering unauthenticated menu. 2025-01-23 13:05:09 INFO Login button clicked. 2025-01-23 13:05:09 INFO Login button clicked. 2025-01-23 13:05:12 INFO Login successful for user: abhishek 2025-01-23 13:05:12 INFO Login successful for user: abhishek 2025-01-23 13:05:12 INFO Database names fetched successfully. 2025-01-23 13:05:12 INFO Database names fetched successfully. 2025-01-23 13:05:17 INFO Database names fetched successfully. 2025-01-23 13:05:17 INFO Database names fetched successfully. 2025-01-23 13:05:17 INFO Table details fetched successfully. 2025-01-23 13:05:17 INFO Table details fetched successfully. 2025-01-23 13:05:26 INFO Database names fetched successfully. 2025-01-23 13:05:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 13:05:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 13:05:28 INFO Tokens consumed: 2970 2025-01-23 13:05:28 INFO Tokens consumed: 2970 2025-01-23 13:05:30 INFO Existing token_consumed found for month: 2025-01 2025-01-23 13:05:30 INFO Existing token_consumed found for month: 2025-01 2025-01-23 13:05:31 INFO token updated successfully: 2025-01 2025-01-23 13:05:31 INFO token updated successfully: 2025-01 2025-01-23 13:05:31 INFO token updated successfully. 2025-01-23 13:05:31 INFO token updated successfully. 2025-01-23 13:05:31 INFO Connected to the database MHealth_Dev. 2025-01-23 13:05:31 INFO Connected to the database MHealth_Dev. 2025-01-23 13:05:31 INFO Query executed successfully. 2025-01-23 13:05:33 INFO Database names fetched successfully. 2025-01-23 13:05:33 INFO Database names fetched successfully. 2025-01-23 13:05:38 INFO Database names fetched successfully. 2025-01-23 13:07:37 INFO Date: 2025-01-23 ======================================== Time: 13:07:37 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:07:42 INFO not logined 2025-01-23 13:07:42 INFO Rendering unauthenticated menu. 2025-01-23 13:08:00 INFO Login button clicked. 2025-01-23 13:08:03 INFO Login successful for user: abhishek 2025-01-23 13:08:11 INFO Database names fetched successfully. 2025-01-23 13:08:29 INFO Date: 2025-01-23 ======================================== Time: 13:08:29 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:08:29 INFO Date: 2025-01-23 ======================================== Time: 13:08:29 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:08:29 INFO not logined 2025-01-23 13:08:29 INFO not logined 2025-01-23 13:08:29 INFO Rendering unauthenticated menu. 2025-01-23 13:08:29 INFO Rendering unauthenticated menu. 2025-01-23 13:08:33 INFO Login button clicked. 2025-01-23 13:08:35 INFO Login successful for user: abhishek 2025-01-23 13:08:35 INFO Login successful for user: abhishek 2025-01-23 13:08:35 INFO Database names fetched successfully. 2025-01-23 13:08:40 INFO Database names fetched successfully. 2025-01-23 13:08:40 INFO Database names fetched successfully. 2025-01-23 13:08:41 INFO Table details fetched successfully. 2025-01-23 13:08:41 INFO Table details fetched successfully. 2025-01-23 13:10:05 INFO Database names fetched successfully. 2025-01-23 13:10:05 INFO Database names fetched successfully. 2025-01-23 13:10:11 INFO Database names fetched successfully. 2025-01-23 13:10:13 INFO Database names fetched successfully. 2025-01-23 13:10:50 INFO Database names fetched successfully. 2025-01-23 13:12:36 INFO Date: 2025-01-23 ======================================== Time: 13:12:36 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 13:12:41 INFO not logined 2025-01-23 13:12:41 INFO Rendering unauthenticated menu. 2025-01-23 13:13:05 INFO Login button clicked. 2025-01-23 13:13:08 INFO Login successful for user: abhishek 2025-01-23 13:13:17 INFO Database names fetched successfully. 2025-01-23 13:13:34 INFO Database names fetched successfully. 2025-01-23 13:13:35 INFO Table details fetched successfully. 2025-01-23 13:13:55 INFO Database names fetched successfully. 2025-01-23 13:13:55 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 13:13:57 INFO Tokens consumed: 2970 2025-01-23 13:13:58 INFO Existing token_consumed found for month: 2025-01 2025-01-23 13:13:59 INFO token updated successfully: 2025-01 2025-01-23 13:13:59 INFO token updated successfully. 2025-01-23 13:13:59 INFO Connected to the database MHealth_Dev. 2025-01-23 13:13:59 INFO Query executed successfully. 2025-01-23 13:14:23 INFO Database names fetched successfully. 2025-01-23 13:14:30 INFO Database names fetched successfully. 2025-01-23 13:14:32 INFO Database names fetched successfully. 2025-01-23 13:14:36 INFO Database names fetched successfully. 2025-01-23 13:14:37 INFO Database names fetched successfully. 2025-01-23 13:14:40 INFO Database names fetched successfully. 2025-01-23 13:14:43 INFO Database names fetched successfully. 2025-01-23 13:14:47 INFO Database names fetched successfully. 2025-01-23 13:14:49 INFO Database names fetched successfully. 2025-01-23 13:14:57 INFO Database names fetched successfully. 2025-01-23 13:15:17 INFO Database names fetched successfully. 2025-01-23 13:15:26 INFO Database names fetched successfully. 2025-01-23 13:15:26 INFO Database names fetched successfully. 2025-01-23 13:15:27 INFO Database names fetched successfully. 2025-01-23 13:15:27 INFO Database names fetched successfully. 2025-01-23 13:15:27 INFO Database names fetched successfully. 2025-01-23 13:15:27 INFO Database names fetched successfully. 2025-01-23 13:15:28 INFO Database names fetched successfully. 2025-01-23 13:15:28 INFO Database names fetched successfully. 2025-01-23 13:15:28 INFO Database names fetched successfully. 2025-01-23 13:15:29 INFO Database names fetched successfully. 2025-01-23 13:15:29 INFO Database names fetched successfully. 2025-01-23 13:15:29 INFO Database names fetched successfully. 2025-01-23 13:15:29 INFO Database names fetched successfully. 2025-01-23 13:15:30 INFO Database names fetched successfully. 2025-01-23 13:15:30 INFO Database names fetched successfully. 2025-01-23 13:15:39 INFO Database names fetched successfully. 2025-01-23 13:15:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 13:15:41 INFO Tokens consumed: 2969 2025-01-23 13:15:42 INFO Existing token_consumed found for month: 2025-01 2025-01-23 13:15:43 INFO token updated successfully: 2025-01 2025-01-23 13:15:43 INFO token updated successfully. 2025-01-23 13:15:43 INFO Connected to the database MHealth_Dev. 2025-01-23 13:15:43 INFO Query executed successfully. 2025-01-23 14:33:58 INFO Date: 2025-01-23 ======================================== Time: 14:33:58 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 14:33:58 INFO not logined 2025-01-23 14:33:58 INFO Rendering unauthenticated menu. 2025-01-23 14:33:58 INFO Rendering unauthenticated menu. 2025-01-23 14:34:24 INFO Login button clicked. 2025-01-23 14:34:24 INFO Login button clicked. 2025-01-23 14:34:28 INFO Login successful for user: abhishek 2025-01-23 14:34:28 INFO Login successful for user: abhishek 2025-01-23 14:34:30 INFO Database names fetched successfully. 2025-01-23 14:34:33 INFO Database names fetched successfully. 2025-01-23 14:34:33 INFO Table details fetched successfully. 2025-01-23 14:34:40 INFO Database names fetched successfully. 2025-01-23 14:34:40 INFO Database names fetched successfully. 2025-01-23 14:34:40 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 14:34:40 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 14:34:42 INFO Tokens consumed: 2970 2025-01-23 14:34:42 INFO Tokens consumed: 2970 2025-01-23 14:34:43 INFO Existing token_consumed found for month: 2025-01 2025-01-23 14:34:45 INFO token updated successfully: 2025-01 2025-01-23 14:34:45 INFO token updated successfully: 2025-01 2025-01-23 14:34:45 INFO token updated successfully. 2025-01-23 14:34:45 INFO token updated successfully. 2025-01-23 14:34:45 INFO Connected to the database MHealth_Dev. 2025-01-23 14:34:45 INFO Query executed successfully. 2025-01-23 14:35:10 INFO Database names fetched successfully. 2025-01-23 14:35:10 INFO Database names fetched successfully. 2025-01-23 14:35:36 INFO Database names fetched successfully. 2025-01-23 14:35:36 INFO Database names fetched successfully. 2025-01-23 14:38:45 INFO Database names fetched successfully. 2025-01-23 14:38:45 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 14:38:45 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 14:38:47 INFO Tokens consumed: 2968 2025-01-23 14:38:47 INFO Tokens consumed: 2968 2025-01-23 14:38:49 INFO Existing token_consumed found for month: 2025-01 2025-01-23 14:38:49 INFO Existing token_consumed found for month: 2025-01 2025-01-23 14:38:50 INFO token updated successfully: 2025-01 2025-01-23 14:38:50 INFO token updated successfully: 2025-01 2025-01-23 14:38:50 INFO token updated successfully. 2025-01-23 14:38:50 INFO token updated successfully. 2025-01-23 14:38:50 INFO Connected to the database MHealth_Dev. 2025-01-23 14:38:50 INFO Connected to the database MHealth_Dev. 2025-01-23 14:38:50 INFO Query executed successfully. 2025-01-23 14:44:50 INFO Database names fetched successfully. 2025-01-23 14:44:50 INFO Database names fetched successfully. 2025-01-23 14:52:25 INFO Date: 2025-01-23 ======================================== Time: 14:52:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 14:52:25 INFO Date: 2025-01-23 ======================================== Time: 14:52:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 14:52:25 INFO not logined 2025-01-23 14:52:25 INFO not logined 2025-01-23 14:52:25 INFO not logined 2025-01-23 14:52:25 INFO Rendering unauthenticated menu. 2025-01-23 14:52:25 INFO Rendering unauthenticated menu. 2025-01-23 14:52:25 INFO Rendering unauthenticated menu. 2025-01-23 14:53:00 INFO Login button clicked. 2025-01-23 14:53:00 INFO Login button clicked. 2025-01-23 14:53:00 INFO Login button clicked. 2025-01-23 14:53:03 INFO Login successful for user: abhishek 2025-01-23 14:53:03 INFO Login successful for user: abhishek 2025-01-23 14:53:03 INFO Login successful for user: abhishek 2025-01-23 14:53:05 INFO Database names fetched successfully. 2025-01-23 14:53:05 INFO Database names fetched successfully. 2025-01-23 14:53:05 INFO Database names fetched successfully. 2025-01-23 14:53:17 INFO Database names fetched successfully. 2025-01-23 14:53:17 INFO Database names fetched successfully. 2025-01-23 14:53:17 INFO Database names fetched successfully. 2025-01-23 14:53:17 INFO Table details fetched successfully. 2025-01-23 14:53:17 INFO Table details fetched successfully. 2025-01-23 14:53:17 INFO Table details fetched successfully. 2025-01-23 16:19:51 INFO Date: 2025-01-23 ======================================== Time: 16:19:51 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:19:55 INFO not logined 2025-01-23 16:19:55 INFO Rendering unauthenticated menu. 2025-01-23 16:22:51 INFO Login button clicked. 2025-01-23 16:22:54 INFO Login successful for user: abhishek 2025-01-23 16:23:05 INFO Database names fetched successfully. 2025-01-23 16:23:20 INFO Database names fetched successfully. 2025-01-23 16:23:21 INFO Table details fetched successfully. 2025-01-23 16:23:50 INFO Database names fetched successfully. 2025-01-23 16:23:50 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:23:52 INFO Tokens consumed: 2970 2025-01-23 16:23:53 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:23:54 INFO token updated successfully: 2025-01 2025-01-23 16:23:54 INFO token updated successfully. 2025-01-23 16:23:54 INFO Connected to the database MHealth_Dev. 2025-01-23 16:23:54 INFO Query executed successfully. 2025-01-23 16:24:15 INFO Database names fetched successfully. 2025-01-23 16:24:15 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:24:17 INFO Tokens consumed: 2969 2025-01-23 16:24:18 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:24:20 INFO token updated successfully: 2025-01 2025-01-23 16:24:20 INFO token updated successfully. 2025-01-23 16:24:20 INFO Connected to the database MHealth_Dev. 2025-01-23 16:24:20 INFO Query executed successfully. 2025-01-23 16:25:13 INFO Database names fetched successfully. 2025-01-23 16:25:13 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:25:15 INFO Tokens consumed: 2970 2025-01-23 16:25:17 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:25:19 INFO token updated successfully: 2025-01 2025-01-23 16:25:19 INFO token updated successfully. 2025-01-23 16:25:19 INFO Connected to the database MHealth_Dev. 2025-01-23 16:25:19 INFO Query executed successfully. 2025-01-23 16:32:02 INFO Date: 2025-01-23 ======================================== Time: 16:32:02 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:32:07 INFO not logined 2025-01-23 16:32:07 INFO Rendering unauthenticated menu. 2025-01-23 16:32:36 INFO Login button clicked. 2025-01-23 16:32:39 INFO Login successful for user: abhishek 2025-01-23 16:32:48 INFO Database names fetched successfully. 2025-01-23 16:33:06 INFO Database names fetched successfully. 2025-01-23 16:33:06 INFO Table details fetched successfully. 2025-01-23 16:33:33 INFO Database names fetched successfully. 2025-01-23 16:33:33 INFO Metadata fetched for table: NewAppointment 2025-01-23 16:33:57 INFO Database names fetched successfully. 2025-01-23 16:33:57 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:33:59 INFO Tokens consumed: 2970 2025-01-23 16:34:01 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:34:02 INFO token updated successfully: 2025-01 2025-01-23 16:34:02 INFO token updated successfully. 2025-01-23 16:34:02 INFO Connected to the database MHealth_Dev. 2025-01-23 16:34:02 INFO Query executed successfully. 2025-01-23 16:34:28 INFO Database names fetched successfully. 2025-01-23 16:34:28 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:34:30 INFO Tokens consumed: 2968 2025-01-23 16:34:31 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:34:32 INFO token updated successfully: 2025-01 2025-01-23 16:34:32 INFO token updated successfully. 2025-01-23 16:34:32 INFO Connected to the database MHealth_Dev. 2025-01-23 16:34:32 INFO Query executed successfully. 2025-01-23 16:34:42 INFO Database names fetched successfully. 2025-01-23 16:35:00 INFO Database names fetched successfully. 2025-01-23 16:35:01 INFO Database names fetched successfully. 2025-01-23 16:35:05 INFO Database names fetched successfully. 2025-01-23 16:35:07 INFO Database names fetched successfully. 2025-01-23 16:35:10 INFO Database names fetched successfully. 2025-01-23 16:35:12 INFO Database names fetched successfully. 2025-01-23 16:35:17 INFO Database names fetched successfully. 2025-01-23 16:35:20 INFO Database names fetched successfully. 2025-01-23 16:35:22 INFO Database names fetched successfully. 2025-01-23 16:35:22 INFO Database names fetched successfully. 2025-01-23 16:35:24 INFO Database names fetched successfully. 2025-01-23 16:35:24 INFO Database names fetched successfully. 2025-01-23 16:35:34 INFO Database names fetched successfully. 2025-01-23 16:35:37 INFO Database names fetched successfully. 2025-01-23 16:36:37 INFO Database names fetched successfully. 2025-01-23 16:36:40 INFO Database names fetched successfully. 2025-01-23 16:38:34 INFO Database names fetched successfully. 2025-01-23 16:38:35 INFO Database names fetched successfully. 2025-01-23 16:38:36 INFO Database names fetched successfully. 2025-01-23 16:38:38 INFO Database names fetched successfully. 2025-01-23 16:38:39 INFO Database names fetched successfully. 2025-01-23 16:38:43 INFO Database names fetched successfully. 2025-01-23 16:38:45 INFO Database names fetched successfully. 2025-01-23 16:38:48 INFO Database names fetched successfully. 2025-01-23 16:38:54 INFO Database names fetched successfully. 2025-01-23 16:40:30 INFO Database names fetched successfully. 2025-01-23 16:40:33 INFO Database names fetched successfully. 2025-01-23 16:41:13 INFO Date: 2025-01-23 ======================================== Time: 16:41:13 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:41:13 INFO Date: 2025-01-23 ======================================== Time: 16:41:13 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:41:13 INFO not logined 2025-01-23 16:41:13 INFO not logined 2025-01-23 16:41:13 INFO Rendering unauthenticated menu. 2025-01-23 16:41:13 INFO Rendering unauthenticated menu. 2025-01-23 16:41:37 INFO Login button clicked. 2025-01-23 16:41:40 INFO Login successful for user: abhishek 2025-01-23 16:41:40 INFO Login successful for user: abhishek 2025-01-23 16:41:40 INFO Database names fetched successfully. 2025-01-23 16:41:40 INFO Database names fetched successfully. 2025-01-23 16:42:34 INFO Database names fetched successfully. 2025-01-23 16:42:34 INFO Database names fetched successfully. 2025-01-23 16:42:34 INFO Table details fetched successfully. 2025-01-23 16:42:34 INFO Table details fetched successfully. 2025-01-23 16:42:41 INFO Database names fetched successfully. 2025-01-23 16:42:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:42:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:42:44 INFO Tokens consumed: 2970 2025-01-23 16:42:44 INFO Tokens consumed: 2970 2025-01-23 16:42:45 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:42:45 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:42:46 INFO token updated successfully: 2025-01 2025-01-23 16:42:46 INFO token updated successfully: 2025-01 2025-01-23 16:42:46 INFO token updated successfully. 2025-01-23 16:42:46 INFO token updated successfully. 2025-01-23 16:42:46 INFO Connected to the database MHealth_Dev. 2025-01-23 16:42:46 INFO Connected to the database MHealth_Dev. 2025-01-23 16:42:46 INFO Query executed successfully. 2025-01-23 16:42:46 INFO Query executed successfully. 2025-01-23 16:54:10 INFO Date: 2025-01-23 ======================================== Time: 16:54:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:54:14 INFO not logined 2025-01-23 16:54:14 INFO Rendering unauthenticated menu. 2025-01-23 16:54:45 INFO Login button clicked. 2025-01-23 16:54:48 INFO Login successful for user: abhishek 2025-01-23 16:54:58 INFO Database names fetched successfully. 2025-01-23 16:55:17 INFO Database names fetched successfully. 2025-01-23 16:55:17 INFO Table details fetched successfully. 2025-01-23 16:55:41 INFO Database names fetched successfully. 2025-01-23 16:55:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmetns```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:55:43 INFO Tokens consumed: 2972 2025-01-23 16:55:44 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:55:45 INFO token updated successfully: 2025-01 2025-01-23 16:55:45 INFO token updated successfully. 2025-01-23 16:55:45 INFO Connected to the database MHealth_Dev. 2025-01-23 16:55:45 INFO Query executed successfully. 2025-01-23 16:56:18 INFO Database names fetched successfully. 2025-01-23 16:56:18 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:56:20 INFO Tokens consumed: 2969 2025-01-23 16:56:21 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:56:23 INFO token updated successfully: 2025-01 2025-01-23 16:56:23 INFO token updated successfully. 2025-01-23 16:56:23 INFO Connected to the database MHealth_Dev. 2025-01-23 16:56:23 INFO Query executed successfully. 2025-01-23 16:57:02 INFO Database names fetched successfully. 2025-01-23 16:57:07 INFO Database names fetched successfully. 2025-01-23 16:57:08 INFO Database names fetched successfully. 2025-01-23 16:57:12 INFO Database names fetched successfully. 2025-01-23 16:57:14 INFO Database names fetched successfully. 2025-01-23 16:57:16 INFO Database names fetched successfully. 2025-01-23 16:57:17 INFO Database names fetched successfully. 2025-01-23 16:57:20 INFO Database names fetched successfully. 2025-01-23 16:57:23 INFO Database names fetched successfully. 2025-01-23 16:57:23 INFO Database names fetched successfully. 2025-01-23 16:57:27 INFO Database names fetched successfully. 2025-01-23 16:57:30 INFO Database names fetched successfully. 2025-01-23 16:57:31 INFO Database names fetched successfully. 2025-01-23 16:57:41 INFO Database names fetched successfully. 2025-01-23 16:57:42 INFO Database names fetched successfully. 2025-01-23 16:57:43 INFO Database names fetched successfully. 2025-01-23 16:57:44 INFO Database names fetched successfully. 2025-01-23 16:57:47 INFO Database names fetched successfully. 2025-01-23 16:57:48 INFO Database names fetched successfully. 2025-01-23 16:57:50 INFO Database names fetched successfully. 2025-01-23 16:57:52 INFO Database names fetched successfully. 2025-01-23 16:57:54 INFO Database names fetched successfully. 2025-01-23 16:57:55 INFO Database names fetched successfully. 2025-01-23 16:57:57 INFO Database names fetched successfully. 2025-01-23 16:57:58 INFO Database names fetched successfully. 2025-01-23 16:58:01 INFO Database names fetched successfully. 2025-01-23 16:58:02 INFO Database names fetched successfully. 2025-01-23 16:58:12 INFO Database names fetched successfully. 2025-01-23 16:58:12 INFO Metadata fetched for table: NewAppointment 2025-01-23 16:58:34 INFO Database names fetched successfully. 2025-01-23 16:58:34 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the text chat```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 16:58:36 INFO Tokens consumed: 2971 2025-01-23 16:58:37 INFO Existing token_consumed found for month: 2025-01 2025-01-23 16:58:38 INFO token updated successfully: 2025-01 2025-01-23 16:58:38 INFO token updated successfully. 2025-01-23 16:58:38 INFO Connected to the database MHealth_Dev. 2025-01-23 16:58:38 INFO Query executed successfully. 2025-01-23 16:59:36 INFO Date: 2025-01-23 ======================================== Time: 16:59:36 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 16:59:36 INFO not logined 2025-01-23 16:59:36 INFO not logined 2025-01-23 16:59:36 INFO Rendering unauthenticated menu. 2025-01-23 16:59:36 INFO Rendering unauthenticated menu. 2025-01-23 17:04:49 INFO Login button clicked. 2025-01-23 17:04:49 INFO Login button clicked. 2025-01-23 17:04:53 INFO Login successful for user: abhishek 2025-01-23 17:04:53 INFO Login successful for user: abhishek 2025-01-23 17:04:54 INFO Database names fetched successfully. 2025-01-23 17:04:54 INFO Database names fetched successfully. 2025-01-23 17:06:01 INFO Database names fetched successfully. 2025-01-23 17:06:01 INFO Table details fetched successfully. 2025-01-23 17:06:09 INFO Database names fetched successfully. 2025-01-23 17:06:09 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 17:06:09 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 17:06:11 INFO Tokens consumed: 2970 2025-01-23 17:06:11 INFO Tokens consumed: 2970 2025-01-23 17:06:12 INFO Existing token_consumed found for month: 2025-01 2025-01-23 17:06:12 INFO Existing token_consumed found for month: 2025-01 2025-01-23 17:06:13 INFO token updated successfully: 2025-01 2025-01-23 17:06:13 INFO token updated successfully: 2025-01 2025-01-23 17:06:13 INFO token updated successfully. 2025-01-23 17:06:13 INFO token updated successfully. 2025-01-23 17:06:13 INFO Connected to the database MHealth_Dev. 2025-01-23 17:06:13 INFO Connected to the database MHealth_Dev. 2025-01-23 17:06:13 INFO Query executed successfully. 2025-01-23 17:06:21 INFO Database names fetched successfully. 2025-01-23 17:06:21 INFO Database names fetched successfully. 2025-01-23 17:06:21 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 17:06:21 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 17:06:23 INFO Tokens consumed: 2969 2025-01-23 17:06:23 INFO Tokens consumed: 2969 2025-01-23 17:06:24 INFO Existing token_consumed found for month: 2025-01 2025-01-23 17:06:24 INFO Existing token_consumed found for month: 2025-01 2025-01-23 17:06:25 INFO token updated successfully: 2025-01 2025-01-23 17:06:25 INFO token updated successfully: 2025-01 2025-01-23 17:06:25 INFO token updated successfully. 2025-01-23 17:06:25 INFO token updated successfully. 2025-01-23 17:06:25 INFO Connected to the database MHealth_Dev. 2025-01-23 17:06:25 INFO Connected to the database MHealth_Dev. 2025-01-23 17:06:25 INFO Query executed successfully. 2025-01-23 17:06:41 INFO Database names fetched successfully. 2025-01-23 17:06:41 INFO Database names fetched successfully. 2025-01-23 17:06:48 INFO Database names fetched successfully. 2025-01-23 17:06:48 INFO Database names fetched successfully. 2025-01-23 17:06:50 INFO Database names fetched successfully. 2025-01-23 17:06:50 INFO Database names fetched successfully. 2025-01-23 17:53:59 INFO Date: 2025-01-23 ======================================== Time: 17:53:59 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 17:54:04 INFO not logined 2025-01-23 17:54:04 INFO Rendering unauthenticated menu. 2025-01-23 17:54:51 INFO Login button clicked. 2025-01-23 17:54:54 INFO Login successful for user: abhishek 2025-01-23 17:55:04 INFO Database names fetched successfully. 2025-01-23 17:56:41 INFO Database names fetched successfully. 2025-01-23 17:56:42 INFO Table details fetched successfully. 2025-01-23 17:56:58 INFO Database names fetched successfully. 2025-01-23 17:56:58 INFO Metadata fetched for table: NewAppointment 2025-01-23 17:58:06 INFO Database names fetched successfully. 2025-01-23 17:58:06 INFO Metadata fetched for table: Registration 2025-01-23 17:58:22 INFO Database names fetched successfully. 2025-01-23 17:58:22 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 17:58:24 INFO Tokens consumed: 2970 2025-01-23 17:58:25 INFO Existing token_consumed found for month: 2025-01 2025-01-23 17:58:26 INFO token updated successfully: 2025-01 2025-01-23 17:58:26 INFO token updated successfully. 2025-01-23 17:58:26 INFO Connected to the database MHealth_Dev. 2025-01-23 17:58:26 INFO Query executed successfully. 2025-01-23 17:58:44 INFO Database names fetched successfully. 2025-01-23 17:59:08 INFO Database names fetched successfully. 2025-01-23 17:59:14 INFO Database names fetched successfully. 2025-01-23 17:59:16 INFO Database names fetched successfully. 2025-01-23 17:59:23 INFO Database names fetched successfully. 2025-01-23 17:59:24 INFO Database names fetched successfully. 2025-01-23 17:59:27 INFO Database names fetched successfully. 2025-01-23 17:59:28 INFO Database names fetched successfully. 2025-01-23 17:59:30 INFO Database names fetched successfully. 2025-01-23 17:59:31 INFO Database names fetched successfully. 2025-01-23 17:59:34 INFO Database names fetched successfully. 2025-01-23 17:59:34 INFO Database names fetched successfully. 2025-01-23 17:59:35 INFO Database names fetched successfully. 2025-01-23 17:59:39 INFO Database names fetched successfully. 2025-01-23 17:59:39 INFO Database names fetched successfully. 2025-01-23 18:00:01 INFO Database names fetched successfully. 2025-01-23 18:00:01 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 18:00:03 INFO Tokens consumed: 2968 2025-01-23 18:00:04 INFO Existing token_consumed found for month: 2025-01 2025-01-23 18:00:06 INFO token updated successfully: 2025-01 2025-01-23 18:00:06 INFO token updated successfully. 2025-01-23 18:00:06 INFO Connected to the database MHealth_Dev. 2025-01-23 18:00:06 INFO Query executed successfully. 2025-01-23 18:00:32 INFO Database names fetched successfully. 2025-01-23 18:00:32 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 18:00:34 INFO Tokens consumed: 2970 2025-01-23 18:00:35 INFO Existing token_consumed found for month: 2025-01 2025-01-23 18:00:36 INFO token updated successfully: 2025-01 2025-01-23 18:00:36 INFO token updated successfully. 2025-01-23 18:00:36 INFO Connected to the database MHealth_Dev. 2025-01-23 18:00:36 INFO Query executed successfully. 2025-01-23 18:00:42 INFO Database names fetched successfully. 2025-01-23 19:39:47 INFO Date: 2025-01-23 ======================================== Time: 19:39:47 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 19:39:52 INFO not logined 2025-01-23 19:39:52 INFO Rendering unauthenticated menu. 2025-01-23 19:40:18 INFO Login button clicked. 2025-01-23 19:40:28 INFO Login successful for user: abhishek 2025-01-23 19:40:41 INFO Database names fetched successfully. 2025-01-23 19:41:28 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-23 19:41:28 INFO Insight list generated successfully. 2025-01-23 19:43:26 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-23 19:43:26 INFO Insight list generated successfully. 2025-01-23 19:43:27 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-23 19:43:28 INFO Connected to the database Insightlab. 2025-01-23 19:43:28 INFO Query executed successfully. 2025-01-23 19:45:01 INFO Database names fetched successfully. 2025-01-23 19:45:06 INFO Database names fetched successfully. 2025-01-23 19:45:06 INFO Table details fetched successfully. 2025-01-23 19:46:35 INFO Database names fetched successfully. 2025-01-23 19:46:35 INFO Metadata fetched for table: NewAppointment 2025-01-23 19:46:47 INFO Database names fetched successfully. 2025-01-23 19:46:47 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 19:46:50 INFO Tokens consumed: 2970 2025-01-23 19:49:17 INFO Existing token_consumed found for month: 2025-01 2025-01-23 19:49:31 INFO token updated successfully: 2025-01 2025-01-23 19:49:31 INFO token updated successfully. 2025-01-23 19:49:31 INFO Connected to the database MHealth_Dev. 2025-01-23 19:49:31 INFO Query executed successfully. 2025-01-23 19:49:31 ERROR Error processing request: st.session_state has no attribute "query_result". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization 2025-01-23 20:23:45 INFO Database names fetched successfully. 2025-01-23 20:23:45 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 20:23:48 INFO Tokens consumed: 2970 2025-01-23 20:23:56 INFO Existing token_consumed found for month: 2025-01 2025-01-23 20:23:57 INFO token updated successfully: 2025-01 2025-01-23 20:23:57 INFO token updated successfully. 2025-01-23 20:23:57 INFO Connected to the database MHealth_Dev. 2025-01-23 20:23:57 INFO Query executed successfully. 2025-01-23 20:23:57 ERROR Error processing request: st.session_state has no attribute "query_result". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization 2025-01-23 20:26:35 INFO Date: 2025-01-23 ======================================== Time: 20:26:35 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-23 20:26:41 INFO not logined 2025-01-23 20:26:41 INFO Rendering unauthenticated menu. 2025-01-23 20:27:36 INFO Login button clicked. 2025-01-23 20:27:40 INFO Login successful for user: abhishek 2025-01-23 20:27:51 INFO Database names fetched successfully. 2025-01-23 20:28:17 INFO Database names fetched successfully. 2025-01-23 20:28:18 INFO Table details fetched successfully. 2025-01-23 20:30:30 INFO Database names fetched successfully. 2025-01-23 20:30:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 20:30:37 INFO Tokens consumed: 2970 2025-01-23 20:30:39 INFO Existing token_consumed found for month: 2025-01 2025-01-23 20:31:57 INFO token updated successfully: 2025-01 2025-01-23 20:31:57 INFO token updated successfully. 2025-01-23 20:31:57 INFO Connected to the database MHealth_Dev. 2025-01-23 20:31:57 INFO Query executed successfully. 2025-01-23 20:32:05 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 37 2025-01-23 20:33:47 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 20:33:48 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/38.json 2025-01-23 20:34:43 INFO Database names fetched successfully. 2025-01-23 20:35:08 INFO Database names fetched successfully. 2025-01-23 20:35:08 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 20:35:12 INFO Tokens consumed: 2970 2025-01-23 20:38:54 INFO Existing token_consumed found for month: 2025-01 2025-01-23 20:38:56 INFO token updated successfully: 2025-01 2025-01-23 20:38:56 INFO token updated successfully. 2025-01-23 20:38:56 INFO Connected to the database MHealth_Dev. 2025-01-23 20:38:56 INFO Query executed successfully. 2025-01-23 20:38:57 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 38 2025-01-23 20:40:18 INFO Database names fetched successfully. 2025-01-23 20:40:18 INFO Metadata fetched for table: NewAppointment 2025-01-23 20:40:29 INFO Database names fetched successfully. 2025-01-23 20:40:30 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 20:40:32 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/39.json 2025-01-23 20:40:39 INFO Database names fetched successfully. 2025-01-23 20:40:40 INFO Database names fetched successfully. 2025-01-23 20:43:39 INFO Database names fetched successfully. 2025-01-23 20:43:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-23 20:43:41 INFO Tokens consumed: 2970 2025-01-23 20:43:43 INFO Existing token_consumed found for month: 2025-01 2025-01-23 20:43:44 INFO token updated successfully: 2025-01 2025-01-23 20:43:44 INFO token updated successfully. 2025-01-23 20:43:44 INFO Connected to the database MHealth_Dev. 2025-01-23 20:43:44 INFO Query executed successfully. 2025-01-23 20:43:46 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 39 2025-01-23 20:43:47 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-23 20:43:48 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/40.json 2025-01-23 20:43:55 INFO Database names fetched successfully. 2025-01-24 10:44:08 INFO Date: 2025-01-24 ======================================== Time: 10:44:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 10:44:08 INFO Date: 2025-01-24 ======================================== Time: 10:44:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 10:44:08 INFO not logined 2025-01-24 10:44:08 INFO not logined 2025-01-24 10:44:08 INFO Rendering unauthenticated menu. 2025-01-24 10:44:08 INFO Rendering unauthenticated menu. 2025-01-24 10:44:30 INFO Login button clicked. 2025-01-24 10:44:30 INFO Login button clicked. 2025-01-24 10:44:34 INFO Login successful for user: abhishek 2025-01-24 10:44:34 INFO Login successful for user: abhishek 2025-01-24 10:44:36 INFO Database names fetched successfully. 2025-01-24 10:44:36 INFO Database names fetched successfully. 2025-01-24 10:44:39 INFO Database names fetched successfully. 2025-01-24 10:44:39 INFO Table details fetched successfully. 2025-01-24 10:44:39 INFO Table details fetched successfully. 2025-01-24 10:44:46 INFO Database names fetched successfully. 2025-01-24 10:44:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 10:44:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 10:44:48 INFO Tokens consumed: 2970 2025-01-24 10:44:48 INFO Tokens consumed: 2970 2025-01-24 10:44:50 INFO Existing token_consumed found for month: 2025-01 2025-01-24 10:44:50 INFO Existing token_consumed found for month: 2025-01 2025-01-24 10:44:51 INFO token updated successfully: 2025-01 2025-01-24 10:44:51 INFO token updated successfully: 2025-01 2025-01-24 10:44:51 INFO token updated successfully. 2025-01-24 10:44:51 INFO token updated successfully. 2025-01-24 10:44:51 INFO Connected to the database MHealth_Dev. 2025-01-24 10:44:51 INFO Query executed successfully. 2025-01-24 10:44:51 INFO Query executed successfully. 2025-01-24 10:44:52 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 40 2025-01-24 10:44:52 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 40 2025-01-24 10:44:54 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 10:44:54 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 10:44:54 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/41.json 2025-01-24 10:44:54 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/41.json 2025-01-24 10:44:57 INFO Database names fetched successfully. 2025-01-24 10:45:01 INFO Database names fetched successfully. 2025-01-24 10:45:01 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:45:01 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:45:58 INFO Database names fetched successfully. 2025-01-24 10:45:58 INFO Database names fetched successfully. 2025-01-24 10:45:58 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 10:45:58 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 10:46:00 INFO Tokens consumed: 2970 2025-01-24 10:46:00 INFO Tokens consumed: 2970 2025-01-24 10:46:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 10:46:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 10:46:02 INFO token updated successfully: 2025-01 2025-01-24 10:46:02 INFO token updated successfully. 2025-01-24 10:46:02 INFO token updated successfully. 2025-01-24 10:46:02 INFO Connected to the database MHealth_Dev. 2025-01-24 10:46:02 INFO Connected to the database MHealth_Dev. 2025-01-24 10:46:02 INFO Query executed successfully. 2025-01-24 10:46:04 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 41 2025-01-24 10:46:05 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 10:46:05 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 10:46:06 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/42.json 2025-01-24 10:46:06 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/42.json 2025-01-24 10:46:08 INFO Database names fetched successfully. 2025-01-24 10:46:08 INFO Database names fetched successfully. 2025-01-24 10:48:33 INFO Date: 2025-01-24 ======================================== Time: 10:48:33 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 10:48:37 INFO not logined 2025-01-24 10:48:37 INFO Rendering unauthenticated menu. 2025-01-24 10:49:02 INFO Login button clicked. 2025-01-24 10:49:06 INFO Login successful for user: abhishek 2025-01-24 10:49:15 INFO Database names fetched successfully. 2025-01-24 10:49:35 INFO Database names fetched successfully. 2025-01-24 10:49:36 INFO Table details fetched successfully. 2025-01-24 10:50:11 INFO Database names fetched successfully. 2025-01-24 10:50:11 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:51:16 INFO Database names fetched successfully. 2025-01-24 10:51:16 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 10:51:18 INFO Tokens consumed: 2970 2025-01-24 10:51:19 INFO Existing token_consumed found for month: 2025-01 2025-01-24 10:51:21 INFO token updated successfully: 2025-01 2025-01-24 10:51:21 INFO token updated successfully. 2025-01-24 10:51:21 INFO Connected to the database MHealth_Dev. 2025-01-24 10:51:21 INFO Query executed successfully. 2025-01-24 10:51:22 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 42 2025-01-24 10:51:24 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 10:51:25 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/43.json 2025-01-24 10:51:40 INFO Database names fetched successfully. 2025-01-24 10:55:54 INFO Date: 2025-01-24 ======================================== Time: 10:55:54 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 10:55:59 INFO not logined 2025-01-24 10:55:59 INFO Rendering unauthenticated menu. 2025-01-24 10:56:59 INFO Login button clicked. 2025-01-24 10:57:02 INFO Login successful for user: abhishek 2025-01-24 10:57:11 INFO Database names fetched successfully. 2025-01-24 10:57:31 INFO Database names fetched successfully. 2025-01-24 10:57:31 INFO Table details fetched successfully. 2025-01-24 10:57:54 INFO Database names fetched successfully. 2025-01-24 10:57:54 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:54 INFO Database names fetched successfully. 2025-01-24 10:57:54 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:54 INFO Database names fetched successfully. 2025-01-24 10:57:54 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:54 INFO Database names fetched successfully. 2025-01-24 10:57:54 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:55 INFO Database names fetched successfully. 2025-01-24 10:57:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:55 INFO Database names fetched successfully. 2025-01-24 10:57:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:55 INFO Database names fetched successfully. 2025-01-24 10:57:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:55 INFO Database names fetched successfully. 2025-01-24 10:57:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:55 INFO Database names fetched successfully. 2025-01-24 10:57:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:56 INFO Database names fetched successfully. 2025-01-24 10:57:56 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:56 INFO Database names fetched successfully. 2025-01-24 10:57:56 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:56 INFO Database names fetched successfully. 2025-01-24 10:57:56 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:56 INFO Database names fetched successfully. 2025-01-24 10:57:56 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:57 INFO Database names fetched successfully. 2025-01-24 10:57:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:57 INFO Database names fetched successfully. 2025-01-24 10:57:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:57 INFO Database names fetched successfully. 2025-01-24 10:57:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:57 INFO Database names fetched successfully. 2025-01-24 10:57:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:58 INFO Database names fetched successfully. 2025-01-24 10:57:58 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:58 INFO Database names fetched successfully. 2025-01-24 10:57:58 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:58 INFO Database names fetched successfully. 2025-01-24 10:57:58 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:58 INFO Database names fetched successfully. 2025-01-24 10:57:58 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:59 INFO Database names fetched successfully. 2025-01-24 10:57:59 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:59 INFO Database names fetched successfully. 2025-01-24 10:57:59 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:57:59 INFO Database names fetched successfully. 2025-01-24 10:57:59 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:00 INFO Database names fetched successfully. 2025-01-24 10:58:00 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:00 INFO Database names fetched successfully. 2025-01-24 10:58:00 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:00 INFO Database names fetched successfully. 2025-01-24 10:58:00 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:01 INFO Database names fetched successfully. 2025-01-24 10:58:01 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:01 INFO Database names fetched successfully. 2025-01-24 10:58:01 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:02 INFO Database names fetched successfully. 2025-01-24 10:58:02 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:02 INFO Database names fetched successfully. 2025-01-24 10:58:02 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:02 INFO Database names fetched successfully. 2025-01-24 10:58:02 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:03 INFO Database names fetched successfully. 2025-01-24 10:58:03 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:03 INFO Database names fetched successfully. 2025-01-24 10:58:03 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:04 INFO Database names fetched successfully. 2025-01-24 10:58:04 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:04 INFO Database names fetched successfully. 2025-01-24 10:58:04 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:05 INFO Database names fetched successfully. 2025-01-24 10:58:05 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:05 INFO Database names fetched successfully. 2025-01-24 10:58:05 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:06 INFO Database names fetched successfully. 2025-01-24 10:58:06 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:06 INFO Database names fetched successfully. 2025-01-24 10:58:06 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:06 INFO Database names fetched successfully. 2025-01-24 10:58:06 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:07 INFO Database names fetched successfully. 2025-01-24 10:58:07 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:07 INFO Database names fetched successfully. 2025-01-24 10:58:07 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:08 INFO Database names fetched successfully. 2025-01-24 10:58:08 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:08 INFO Database names fetched successfully. 2025-01-24 10:58:08 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:09 INFO Database names fetched successfully. 2025-01-24 10:58:09 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:09 INFO Database names fetched successfully. 2025-01-24 10:58:09 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:10 INFO Database names fetched successfully. 2025-01-24 10:58:10 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:10 INFO Database names fetched successfully. 2025-01-24 10:58:10 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:11 INFO Database names fetched successfully. 2025-01-24 10:58:11 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:11 INFO Database names fetched successfully. 2025-01-24 10:58:11 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:12 INFO Database names fetched successfully. 2025-01-24 10:58:12 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:13 INFO Database names fetched successfully. 2025-01-24 10:58:13 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:13 INFO Database names fetched successfully. 2025-01-24 10:58:13 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:14 INFO Database names fetched successfully. 2025-01-24 10:58:14 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:14 INFO Database names fetched successfully. 2025-01-24 10:58:14 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:15 INFO Database names fetched successfully. 2025-01-24 10:58:15 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:15 INFO Database names fetched successfully. 2025-01-24 10:58:15 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:16 INFO Database names fetched successfully. 2025-01-24 10:58:16 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:17 INFO Database names fetched successfully. 2025-01-24 10:58:17 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:18 INFO Database names fetched successfully. 2025-01-24 10:58:18 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:19 INFO Database names fetched successfully. 2025-01-24 10:58:19 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:21 INFO Database names fetched successfully. 2025-01-24 10:58:21 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:22 INFO Database names fetched successfully. 2025-01-24 10:58:22 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:24 INFO Database names fetched successfully. 2025-01-24 10:58:24 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:26 INFO Database names fetched successfully. 2025-01-24 10:58:26 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:27 INFO Database names fetched successfully. 2025-01-24 10:58:27 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:29 INFO Database names fetched successfully. 2025-01-24 10:58:29 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:30 INFO Database names fetched successfully. 2025-01-24 10:58:30 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:32 INFO Database names fetched successfully. 2025-01-24 10:58:32 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:34 INFO Database names fetched successfully. 2025-01-24 10:58:34 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:36 INFO Database names fetched successfully. 2025-01-24 10:58:36 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:37 INFO Database names fetched successfully. 2025-01-24 10:58:37 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:39 INFO Database names fetched successfully. 2025-01-24 10:58:39 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:40 INFO Database names fetched successfully. 2025-01-24 10:58:40 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:42 INFO Database names fetched successfully. 2025-01-24 10:58:42 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:43 INFO Database names fetched successfully. 2025-01-24 10:58:43 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:44 INFO Database names fetched successfully. 2025-01-24 10:58:44 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:46 INFO Database names fetched successfully. 2025-01-24 10:58:46 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:47 INFO Database names fetched successfully. 2025-01-24 10:58:47 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:48 INFO Database names fetched successfully. 2025-01-24 10:58:49 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:50 INFO Database names fetched successfully. 2025-01-24 10:58:50 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:52 INFO Database names fetched successfully. 2025-01-24 10:58:52 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:53 INFO Database names fetched successfully. 2025-01-24 10:58:53 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:55 INFO Database names fetched successfully. 2025-01-24 10:58:55 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:57 INFO Database names fetched successfully. 2025-01-24 10:58:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:58:59 INFO Database names fetched successfully. 2025-01-24 10:58:59 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:00 INFO Database names fetched successfully. 2025-01-24 10:59:00 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:02 INFO Database names fetched successfully. 2025-01-24 10:59:02 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:03 INFO Database names fetched successfully. 2025-01-24 10:59:03 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:05 INFO Database names fetched successfully. 2025-01-24 10:59:05 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:07 INFO Database names fetched successfully. 2025-01-24 10:59:07 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:09 INFO Database names fetched successfully. 2025-01-24 10:59:09 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:11 INFO Database names fetched successfully. 2025-01-24 10:59:11 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:13 INFO Database names fetched successfully. 2025-01-24 10:59:13 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:15 INFO Database names fetched successfully. 2025-01-24 10:59:15 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:16 INFO Database names fetched successfully. 2025-01-24 10:59:16 INFO Metadata fetched for table: NewAppointment 2025-01-24 10:59:18 INFO Database names fetched successfully. 2025-01-24 10:59:18 INFO Metadata fetched for table: NewAppointment 2025-01-24 11:07:38 INFO Date: 2025-01-24 ======================================== Time: 11:07:38 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:07:42 INFO not logined 2025-01-24 11:07:42 INFO Rendering unauthenticated menu. 2025-01-24 11:09:08 INFO Login button clicked. 2025-01-24 11:09:11 INFO Login successful for user: abhishek 2025-01-24 11:09:21 INFO Database names fetched successfully. 2025-01-24 11:09:36 INFO Database names fetched successfully. 2025-01-24 11:09:37 INFO Table details fetched successfully. 2025-01-24 11:10:10 INFO Database names fetched successfully. 2025-01-24 11:10:10 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:10:12 INFO Tokens consumed: 2970 2025-01-24 11:10:14 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:10:16 INFO token updated successfully: 2025-01 2025-01-24 11:10:16 INFO token updated successfully. 2025-01-24 11:10:16 INFO Connected to the database MHealth_Dev. 2025-01-24 11:10:16 INFO Query executed successfully. 2025-01-24 11:10:16 INFO Database names fetched successfully. 2025-01-24 11:20:38 INFO Date: 2025-01-24 ======================================== Time: 11:20:38 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:20:42 INFO not logined 2025-01-24 11:20:42 INFO Rendering unauthenticated menu. 2025-01-24 11:21:55 INFO Login button clicked. 2025-01-24 11:21:58 INFO Login successful for user: abhishek 2025-01-24 11:22:08 INFO Database names fetched successfully. 2025-01-24 11:23:32 INFO Database names fetched successfully. 2025-01-24 11:23:32 INFO Table details fetched successfully. 2025-01-24 11:24:17 INFO Database names fetched successfully. 2025-01-24 11:24:17 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmetns```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:24:20 INFO Tokens consumed: 2972 2025-01-24 11:24:21 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:24:23 INFO token updated successfully: 2025-01 2025-01-24 11:24:23 INFO token updated successfully. 2025-01-24 11:24:23 INFO Connected to the database MHealth_Dev. 2025-01-24 11:24:23 INFO Query executed successfully. 2025-01-24 11:24:24 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 43 2025-01-24 11:24:26 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:24:27 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/44.json 2025-01-24 11:24:54 INFO Database names fetched successfully. 2025-01-24 11:24:54 INFO Metadata fetched for table: NewAppointment 2025-01-24 11:26:59 INFO Database names fetched successfully. 2025-01-24 11:27:05 INFO Database names fetched successfully. 2025-01-24 11:27:08 INFO Date: 2025-01-24 ======================================== Time: 11:27:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:27:08 INFO Date: 2025-01-24 ======================================== Time: 11:27:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:27:08 INFO not logined 2025-01-24 11:27:08 INFO Rendering unauthenticated menu. 2025-01-24 11:27:08 INFO not logined 2025-01-24 11:27:08 INFO Rendering unauthenticated menu. 2025-01-24 11:27:48 INFO Login button clicked. 2025-01-24 11:27:48 INFO Login button clicked. 2025-01-24 11:27:51 INFO Login successful for user: abhishek 2025-01-24 11:27:51 INFO Login successful for user: abhishek 2025-01-24 11:27:51 INFO Database names fetched successfully. 2025-01-24 11:27:51 INFO Database names fetched successfully. 2025-01-24 11:34:29 INFO Date: 2025-01-24 ======================================== Time: 11:34:29 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:34:34 INFO not logined 2025-01-24 11:34:34 INFO Rendering unauthenticated menu. 2025-01-24 11:36:15 INFO Login button clicked. 2025-01-24 11:36:18 INFO Login successful for user: abhishek 2025-01-24 11:36:28 INFO Database names fetched successfully. 2025-01-24 11:37:39 INFO Database names fetched successfully. 2025-01-24 11:37:39 ERROR Error processing request: 'st.session_state has no key "table_master". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-01-24 11:46:19 INFO Date: 2025-01-24 ======================================== Time: 11:46:19 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:46:23 INFO not logined 2025-01-24 11:46:23 INFO Rendering unauthenticated menu. 2025-01-24 11:46:42 INFO Login button clicked. 2025-01-24 11:46:46 INFO Login successful for user: abhishek 2025-01-24 11:46:56 INFO Database names fetched successfully. 2025-01-24 11:47:16 INFO Database names fetched successfully. 2025-01-24 11:47:17 INFO Table details fetched successfully. 2025-01-24 11:47:48 INFO Database names fetched successfully. 2025-01-24 11:47:48 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:47:50 INFO Tokens consumed: 2970 2025-01-24 11:47:51 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:47:53 INFO token updated successfully: 2025-01 2025-01-24 11:47:53 INFO token updated successfully. 2025-01-24 11:47:53 INFO Connected to the database MHealth_Dev. 2025-01-24 11:47:53 INFO Query executed successfully. 2025-01-24 11:47:55 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 44 2025-01-24 11:47:56 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:47:57 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/45.json 2025-01-24 11:48:49 INFO Date: 2025-01-24 ======================================== Time: 11:48:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:48:49 INFO Date: 2025-01-24 ======================================== Time: 11:48:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:48:49 INFO not logined 2025-01-24 11:48:49 INFO not logined 2025-01-24 11:48:49 INFO Rendering unauthenticated menu. 2025-01-24 11:48:49 INFO Rendering unauthenticated menu. 2025-01-24 11:49:04 INFO Login button clicked. 2025-01-24 11:49:04 INFO Login button clicked. 2025-01-24 11:49:08 INFO Login successful for user: abhishek 2025-01-24 11:49:08 INFO Login successful for user: abhishek 2025-01-24 11:49:08 INFO Database names fetched successfully. 2025-01-24 11:49:12 INFO Database names fetched successfully. 2025-01-24 11:49:12 INFO Database names fetched successfully. 2025-01-24 11:49:12 INFO Table details fetched successfully. 2025-01-24 11:49:12 INFO Table details fetched successfully. 2025-01-24 11:49:19 INFO Database names fetched successfully. 2025-01-24 11:49:19 INFO Database names fetched successfully. 2025-01-24 11:49:19 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:49:19 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:49:22 INFO Tokens consumed: 2970 2025-01-24 11:49:22 INFO Tokens consumed: 2970 2025-01-24 11:49:24 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:49:25 INFO token updated successfully: 2025-01 2025-01-24 11:49:25 INFO token updated successfully: 2025-01 2025-01-24 11:49:25 INFO token updated successfully. 2025-01-24 11:49:25 INFO token updated successfully. 2025-01-24 11:49:25 INFO Connected to the database MHealth_Dev. 2025-01-24 11:49:25 INFO Connected to the database MHealth_Dev. 2025-01-24 11:49:25 INFO Query executed successfully. 2025-01-24 11:49:25 INFO Query executed successfully. 2025-01-24 11:49:26 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 45 2025-01-24 11:49:26 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 45 2025-01-24 11:49:28 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:49:28 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:49:29 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/46.json 2025-01-24 11:49:29 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/46.json 2025-01-24 11:50:26 INFO Database names fetched successfully. 2025-01-24 11:50:26 INFO Database names fetched successfully. 2025-01-24 11:50:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:50:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:50:37 INFO Tokens consumed: 2970 2025-01-24 11:50:37 INFO Tokens consumed: 2970 2025-01-24 11:50:39 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:50:39 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:50:40 INFO token updated successfully: 2025-01 2025-01-24 11:50:40 INFO token updated successfully: 2025-01 2025-01-24 11:50:40 INFO token updated successfully. 2025-01-24 11:50:40 INFO token updated successfully. 2025-01-24 11:50:40 INFO Connected to the database MHealth_Dev. 2025-01-24 11:50:40 INFO Query executed successfully. 2025-01-24 11:50:42 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 46 2025-01-24 11:50:42 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 46 2025-01-24 11:50:43 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:50:43 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:50:44 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/47.json 2025-01-24 11:52:44 INFO Date: 2025-01-24 ======================================== Time: 11:52:44 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 11:52:49 INFO not logined 2025-01-24 11:52:49 INFO Rendering unauthenticated menu. 2025-01-24 11:53:06 INFO Login button clicked. 2025-01-24 11:53:09 INFO Login successful for user: abhishek 2025-01-24 11:53:18 INFO Database names fetched successfully. 2025-01-24 11:53:49 INFO Database names fetched successfully. 2025-01-24 11:53:49 INFO Table details fetched successfully. 2025-01-24 11:54:30 INFO Database names fetched successfully. 2025-01-24 11:54:30 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:54:32 INFO Tokens consumed: 2970 2025-01-24 11:54:34 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:54:35 INFO token updated successfully: 2025-01 2025-01-24 11:54:35 INFO token updated successfully. 2025-01-24 11:54:35 INFO Connected to the database MHealth_Dev. 2025-01-24 11:54:35 INFO Query executed successfully. 2025-01-24 11:54:36 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 47 2025-01-24 11:54:38 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:54:39 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/48.json 2025-01-24 11:54:57 INFO Database names fetched successfully. 2025-01-24 11:54:57 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:54:59 INFO Tokens consumed: 2970 2025-01-24 11:55:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:55:02 INFO token updated successfully: 2025-01 2025-01-24 11:55:02 INFO token updated successfully. 2025-01-24 11:55:02 INFO Connected to the database MHealth_Dev. 2025-01-24 11:55:02 INFO Query executed successfully. 2025-01-24 11:55:04 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 48 2025-01-24 11:55:05 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:55:06 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/49.json 2025-01-24 11:55:13 INFO Database names fetched successfully. 2025-01-24 11:55:13 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:55:15 INFO Tokens consumed: 2970 2025-01-24 11:55:17 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:55:18 INFO token updated successfully: 2025-01 2025-01-24 11:55:18 INFO token updated successfully. 2025-01-24 11:55:18 INFO Connected to the database MHealth_Dev. 2025-01-24 11:55:18 INFO Query executed successfully. 2025-01-24 11:55:20 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 49 2025-01-24 11:55:21 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:55:22 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/50.json 2025-01-24 11:55:41 INFO Database names fetched successfully. 2025-01-24 11:55:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 11:55:43 INFO Tokens consumed: 2970 2025-01-24 11:55:44 INFO Existing token_consumed found for month: 2025-01 2025-01-24 11:55:46 INFO token updated successfully: 2025-01 2025-01-24 11:55:46 INFO token updated successfully. 2025-01-24 11:55:46 INFO Connected to the database MHealth_Dev. 2025-01-24 11:55:46 INFO Query executed successfully. 2025-01-24 11:55:47 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 50 2025-01-24 11:55:49 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 11:55:50 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/51.json 2025-01-24 11:57:04 INFO Database names fetched successfully. 2025-01-24 11:57:10 INFO Database names fetched successfully. 2025-01-24 11:57:13 INFO Database names fetched successfully. 2025-01-24 11:57:19 INFO Database names fetched successfully. 2025-01-24 12:12:39 INFO Date: 2025-01-24 ======================================== Time: 12:12:39 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 12:12:43 INFO not logined 2025-01-24 12:12:43 INFO Rendering unauthenticated menu. 2025-01-24 12:13:06 INFO Login button clicked. 2025-01-24 12:13:10 INFO Login successful for user: abhishek 2025-01-24 12:13:20 INFO Database names fetched successfully. 2025-01-24 12:13:52 INFO Database names fetched successfully. 2025-01-24 12:13:53 INFO Table details fetched successfully. 2025-01-24 12:14:17 INFO Database names fetched successfully. 2025-01-24 12:14:17 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 12:14:19 INFO Tokens consumed: 2970 2025-01-24 12:14:20 INFO Existing token_consumed found for month: 2025-01 2025-01-24 12:14:22 INFO token updated successfully: 2025-01 2025-01-24 12:14:22 INFO token updated successfully. 2025-01-24 12:14:22 INFO Connected to the database MHealth_Dev. 2025-01-24 12:14:22 INFO Query executed successfully. 2025-01-24 12:14:22 INFO Database names fetched successfully. 2025-01-24 12:14:22 ERROR Error processing request: There are multiple `selectbox` elements with the same auto-generated ID. When this element is created, it is assigned an internal ID based on the element type and provided parameters. Multiple elements with the same type and parameters will cause this error. To fix this error, please pass a unique `key` argument to the `selectbox` element. 2025-01-24 12:24:18 INFO Date: 2025-01-24 ======================================== Time: 12:24:18 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 12:24:22 INFO not logined 2025-01-24 12:24:22 INFO Rendering unauthenticated menu. 2025-01-24 12:30:05 INFO Login button clicked. 2025-01-24 12:30:08 INFO Login successful for user: abhishek 2025-01-24 12:30:18 INFO Database names fetched successfully. 2025-01-24 12:30:39 INFO Database names fetched successfully. 2025-01-24 12:30:40 INFO Table details fetched successfully. 2025-01-24 12:31:02 INFO Database names fetched successfully. 2025-01-24 12:31:02 INFO Metadata fetched for table: NewAppointment 2025-01-24 12:31:26 INFO Database names fetched successfully. 2025-01-24 12:31:26 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 12:31:28 INFO Tokens consumed: 2970 2025-01-24 12:31:30 INFO Existing token_consumed found for month: 2025-01 2025-01-24 12:31:32 INFO token updated successfully: 2025-01 2025-01-24 12:31:32 INFO token updated successfully. 2025-01-24 12:31:32 INFO Connected to the database MHealth_Dev. 2025-01-24 12:31:32 INFO Query executed successfully. 2025-01-24 12:31:33 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 51 2025-01-24 12:31:35 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 12:31:36 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/52.json 2025-01-24 12:40:49 INFO Date: 2025-01-24 ======================================== Time: 12:40:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 12:40:49 INFO Date: 2025-01-24 ======================================== Time: 12:40:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 12:40:49 INFO not logined 2025-01-24 12:40:49 INFO Rendering unauthenticated menu. 2025-01-24 13:11:05 INFO Date: 2025-01-24 ======================================== Time: 13:11:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 13:11:09 INFO not logined 2025-01-24 13:11:09 INFO Rendering unauthenticated menu. 2025-01-24 13:11:22 INFO Login button clicked. 2025-01-24 13:11:26 INFO Login successful for user: abhishek 2025-01-24 13:11:36 INFO Database names fetched successfully. 2025-01-24 13:12:19 INFO Database names fetched successfully. 2025-01-24 13:12:19 INFO Table details fetched successfully. 2025-01-24 13:13:33 INFO Database names fetched successfully. 2025-01-24 13:13:33 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 13:13:35 INFO Tokens consumed: 2970 2025-01-24 13:13:36 INFO Existing token_consumed found for month: 2025-01 2025-01-24 13:13:38 INFO token updated successfully: 2025-01 2025-01-24 13:13:38 INFO token updated successfully. 2025-01-24 13:13:38 INFO Connected to the database MHealth_Dev. 2025-01-24 13:13:38 INFO Query executed successfully. 2025-01-24 13:13:39 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 52 2025-01-24 13:13:41 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 13:13:42 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/53.json 2025-01-24 14:12:41 INFO Date: 2025-01-24 ======================================== Time: 14:12:41 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 14:12:45 INFO not logined 2025-01-24 14:12:45 INFO Rendering unauthenticated menu. 2025-01-24 14:14:53 INFO Login button clicked. 2025-01-24 14:14:56 INFO Login successful for user: abhishek 2025-01-24 14:15:07 INFO Database names fetched successfully. 2025-01-24 14:17:43 INFO Database names fetched successfully. 2025-01-24 14:18:54 INFO Database names fetched successfully. 2025-01-24 14:18:58 INFO Database names fetched successfully. 2025-01-24 14:19:02 INFO Date: 2025-01-24 ======================================== Time: 14:19:02 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 14:19:02 INFO Date: 2025-01-24 ======================================== Time: 14:19:02 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 14:19:02 INFO not logined 2025-01-24 14:19:02 INFO not logined 2025-01-24 14:19:02 INFO Rendering unauthenticated menu. 2025-01-24 14:19:02 INFO Rendering unauthenticated menu. 2025-01-24 14:19:21 INFO Login button clicked. 2025-01-24 14:19:21 INFO Login button clicked. 2025-01-24 14:19:24 INFO Login successful for user: abhishek 2025-01-24 14:19:24 INFO Login successful for user: abhishek 2025-01-24 14:19:24 INFO Database names fetched successfully. 2025-01-24 14:19:24 INFO Database names fetched successfully. 2025-01-24 14:21:38 INFO Date: 2025-01-24 ======================================== Time: 14:21:38 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 14:21:42 INFO not logined 2025-01-24 14:21:42 INFO Rendering unauthenticated menu. 2025-01-24 14:24:02 INFO Login button clicked. 2025-01-24 14:24:05 INFO Login successful for user: abhishek 2025-01-24 14:24:14 INFO Database names fetched successfully. 2025-01-24 14:24:49 INFO Database names fetched successfully. 2025-01-24 14:24:50 INFO Table details fetched successfully. 2025-01-24 14:25:18 INFO Database names fetched successfully. 2025-01-24 14:25:18 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 14:25:20 INFO Tokens consumed: 2970 2025-01-24 14:25:22 INFO Existing token_consumed found for month: 2025-01 2025-01-24 14:25:23 INFO token updated successfully: 2025-01 2025-01-24 14:25:23 INFO token updated successfully. 2025-01-24 14:25:23 INFO Connected to the database MHealth_Dev. 2025-01-24 14:25:23 INFO Query executed successfully. 2025-01-24 14:25:25 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 53 2025-01-24 14:25:26 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 14:25:27 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 14:25:46 INFO Database names fetched successfully. 2025-01-24 14:25:47 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 14:25:49 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 14:25:49 INFO Query saved in the library with id 54. 2025-01-24 14:34:10 INFO Date: 2025-01-24 ======================================== Time: 14:34:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 14:34:15 INFO not logined 2025-01-24 14:34:15 INFO Rendering unauthenticated menu. 2025-01-24 14:34:46 INFO Login button clicked. 2025-01-24 14:34:50 INFO Login successful for user: abhishek 2025-01-24 14:35:00 INFO Database names fetched successfully. 2025-01-24 14:36:17 INFO Database names fetched successfully. 2025-01-24 14:36:17 INFO Table details fetched successfully. 2025-01-24 14:36:39 INFO Database names fetched successfully. 2025-01-24 14:36:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 14:36:41 INFO Tokens consumed: 2969 2025-01-24 14:36:43 INFO Existing token_consumed found for month: 2025-01 2025-01-24 14:36:44 INFO token updated successfully: 2025-01 2025-01-24 14:36:44 INFO token updated successfully. 2025-01-24 14:36:44 INFO Connected to the database MHealth_Dev. 2025-01-24 14:36:44 INFO Query executed successfully. 2025-01-24 14:36:46 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 54 2025-01-24 14:36:47 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 14:36:49 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 14:37:09 INFO Database names fetched successfully. 2025-01-24 14:37:10 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 14:37:11 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 14:37:11 INFO Query saved in the library with id 55. 2025-01-24 15:00:07 INFO Date: 2025-01-24 ======================================== Time: 15:00:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 15:00:12 INFO not logined 2025-01-24 15:00:12 INFO Rendering unauthenticated menu. 2025-01-24 15:00:58 INFO Login button clicked. 2025-01-24 15:01:01 INFO Login successful for user: abhishek 2025-01-24 15:01:12 INFO Database names fetched successfully. 2025-01-24 15:02:31 INFO Database names fetched successfully. 2025-01-24 15:02:32 INFO Table details fetched successfully. 2025-01-24 15:02:57 INFO Database names fetched successfully. 2025-01-24 15:02:57 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 15:02:59 INFO Tokens consumed: 2970 2025-01-24 15:03:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 15:03:02 INFO token updated successfully: 2025-01 2025-01-24 15:03:02 INFO token updated successfully. 2025-01-24 15:03:02 INFO Connected to the database MHealth_Dev. 2025-01-24 15:03:02 INFO Query executed successfully. 2025-01-24 15:03:02 INFO Database names fetched successfully. 2025-01-24 15:09:57 INFO Database names fetched successfully. 2025-01-24 15:09:57 INFO Metadata fetched for table: NewAppointment 2025-01-24 15:22:02 INFO Date: 2025-01-24 ======================================== Time: 15:22:02 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 15:22:07 INFO not logined 2025-01-24 15:22:07 INFO Rendering unauthenticated menu. 2025-01-24 15:22:23 INFO Login button clicked. 2025-01-24 15:22:27 INFO Login successful for user: abhishek 2025-01-24 15:22:37 INFO Database names fetched successfully. 2025-01-24 15:31:14 INFO Date: 2025-01-24 ======================================== Time: 15:31:14 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 15:31:18 INFO not logined 2025-01-24 15:31:18 INFO Rendering unauthenticated menu. 2025-01-24 15:33:28 INFO Login button clicked. 2025-01-24 15:33:32 INFO Login successful for user: abhishek 2025-01-24 15:33:42 INFO Database names fetched successfully. 2025-01-24 15:42:55 INFO Date: 2025-01-24 ======================================== Time: 15:42:55 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 15:43:00 INFO not logined 2025-01-24 15:43:00 INFO Rendering unauthenticated menu. 2025-01-24 15:43:30 INFO Login button clicked. 2025-01-24 15:43:34 INFO Login successful for user: maheshsr 2025-01-24 15:43:44 INFO Database names fetched successfully. 2025-01-24 15:46:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:46:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:46:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:46:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:46:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:46:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:46:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:46:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:46:28 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-24 15:46:29 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:46:30 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-24 15:46:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:46:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-24 15:46:33 INFO Insight list generated successfully. 2025-01-24 15:46:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-24 15:46:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:46:43 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-24 15:46:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:46:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-24 15:46:46 INFO Insight list generated successfully. 2025-01-24 15:46:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-24 15:46:47 INFO Connected to the database Insightlab. 2025-01-24 15:46:47 INFO Query executed successfully. 2025-01-24 15:47:30 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-24 15:47:31 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:47:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-24 15:47:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:47:34 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-24 15:47:34 INFO Insight list generated successfully. 2025-01-24 15:47:36 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:47:36 INFO Connected to the database MHealth_Dev. 2025-01-24 15:47:36 INFO Query executed successfully. 2025-01-24 15:47:36 ERROR Error executing generated insight code: StreamlitValueAboveMaxError(_exec_kwargs={'value': 1, 'max_value': 0}) 2025-01-24 15:47:36 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-24 15:48:36 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-24 15:48:38 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:48:39 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-24 15:48:40 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:48:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-24 15:48:41 INFO Insight list generated successfully. 2025-01-24 15:48:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-24 15:48:42 INFO Connected to the database Insightlab. 2025-01-24 15:48:42 INFO Query executed successfully. 2025-01-24 15:48:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:48:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:48:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:48:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:49:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:49:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:49:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:49:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:49:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:49:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:49:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:49:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:49:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:49:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:49:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:49:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:49:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:49:19 INFO Connected to the database MHealth_Dev. 2025-01-24 15:49:19 INFO Query executed successfully. 2025-01-24 15:49:19 INFO Dataset columns displayed using AG Grid. 2025-01-24 15:54:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:54:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:54:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:54:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:54:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:55:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:55:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:55:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:55:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:55:02 INFO Connected to the database MHealth_Dev. 2025-01-24 15:55:02 INFO Query executed successfully. 2025-01-24 15:55:02 INFO Dataset columns displayed using AG Grid. 2025-01-24 15:55:03 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create insight with all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 15:55:05 INFO Tokens consumed: 884 2025-01-24 15:55:07 INFO Existing token_consumed found for month: 2025-01 2025-01-24 15:55:08 INFO token updated successfully: 2025-01 2025-01-24 15:55:08 INFO token updated successfully. 2025-01-24 15:55:10 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 175 2025-01-24 15:55:13 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-24 15:55:14 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/176.py 2025-01-24 15:55:14 INFO Code generated and written in generated_method//175.py 2025-01-24 15:55:14 WARNING result_df is not defined in the output dictionary 2025-01-24 15:55:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:55:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:55:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:55:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:55:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:55:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:55:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:55:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:55:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:55:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:55:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:55:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:55:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:55:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:55:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:55:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:55:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:55:50 INFO Connected to the database MHealth_Dev. 2025-01-24 15:55:50 INFO Query executed successfully. 2025-01-24 15:55:50 INFO Dataset columns displayed using AG Grid. 2025-01-24 15:55:50 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create insight with all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 15:55:53 INFO Tokens consumed: 881 2025-01-24 15:55:54 INFO Existing token_consumed found for month: 2025-01 2025-01-24 15:55:55 INFO token updated successfully: 2025-01 2025-01-24 15:55:55 INFO token updated successfully. 2025-01-24 15:55:57 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 176 2025-01-24 15:55:59 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-24 15:56:00 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/177.py 2025-01-24 15:56:00 INFO Code generated and written in generated_method//176.py 2025-01-24 15:56:00 INFO Insight generated and displayed using AG Grid. 2025-01-24 15:56:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-24 15:56:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-24 15:56:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-24 15:56:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:56:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-24 15:56:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-24 15:56:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-24 15:56:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-24 15:56:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-24 15:56:16 INFO Connected to the database MHealth_Dev. 2025-01-24 15:56:16 INFO Query executed successfully. 2025-01-24 15:56:16 INFO Dataset columns displayed using AG Grid. 2025-01-24 15:56:35 INFO Database names fetched successfully. 2025-01-24 15:58:56 INFO Date: 2025-01-24 ======================================== Time: 15:58:56 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 15:59:01 INFO not logined 2025-01-24 15:59:01 INFO Rendering unauthenticated menu. 2025-01-24 15:59:18 INFO Login button clicked. 2025-01-24 15:59:22 INFO Login successful for user: abhishek 2025-01-24 15:59:31 INFO Database names fetched successfully. 2025-01-24 16:02:12 INFO Database names fetched successfully. 2025-01-24 16:02:16 INFO Database names fetched successfully. 2025-01-24 16:02:18 INFO Date: 2025-01-24 ======================================== Time: 16:02:18 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:02:18 INFO Date: 2025-01-24 ======================================== Time: 16:02:18 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:02:18 INFO not logined 2025-01-24 16:02:18 INFO Rendering unauthenticated menu. 2025-01-24 16:02:18 INFO Rendering unauthenticated menu. 2025-01-24 16:02:38 INFO Login button clicked. 2025-01-24 16:02:38 INFO Login button clicked. 2025-01-24 16:02:42 INFO Login successful for user: abhishek 2025-01-24 16:02:42 INFO Login successful for user: abhishek 2025-01-24 16:02:42 INFO Database names fetched successfully. 2025-01-24 16:04:40 INFO Database names fetched successfully. 2025-01-24 16:05:13 INFO Date: 2025-01-24 ======================================== Time: 16:05:13 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:05:13 INFO Date: 2025-01-24 ======================================== Time: 16:05:13 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:05:13 INFO Date: 2025-01-24 ======================================== Time: 16:05:13 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:05:13 INFO not logined 2025-01-24 16:05:13 INFO not logined 2025-01-24 16:05:13 INFO not logined 2025-01-24 16:05:13 INFO Rendering unauthenticated menu. 2025-01-24 16:05:13 INFO Rendering unauthenticated menu. 2025-01-24 16:05:13 INFO Rendering unauthenticated menu. 2025-01-24 16:05:36 INFO Login button clicked. 2025-01-24 16:05:36 INFO Login button clicked. 2025-01-24 16:05:36 INFO Login button clicked. 2025-01-24 16:05:39 INFO Login successful for user: abhishek 2025-01-24 16:05:39 INFO Login successful for user: abhishek 2025-01-24 16:05:39 INFO Login successful for user: abhishek 2025-01-24 16:05:39 INFO Database names fetched successfully. 2025-01-24 16:05:39 INFO Database names fetched successfully. 2025-01-24 16:07:25 INFO Database names fetched successfully. 2025-01-24 16:07:25 INFO Database names fetched successfully. 2025-01-24 16:07:25 INFO Database names fetched successfully. 2025-01-24 16:07:30 INFO Database names fetched successfully. 2025-01-24 16:07:30 INFO Database names fetched successfully. 2025-01-24 16:07:30 INFO Database names fetched successfully. 2025-01-24 16:07:34 INFO Date: 2025-01-24 ======================================== Time: 16:07:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:07:34 INFO Date: 2025-01-24 ======================================== Time: 16:07:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:07:34 INFO Date: 2025-01-24 ======================================== Time: 16:07:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:07:34 INFO not logined 2025-01-24 16:07:34 INFO not logined 2025-01-24 16:07:34 INFO not logined 2025-01-24 16:07:34 INFO Rendering unauthenticated menu. 2025-01-24 16:07:34 INFO Rendering unauthenticated menu. 2025-01-24 16:07:34 INFO Rendering unauthenticated menu. 2025-01-24 16:07:34 INFO Rendering unauthenticated menu. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:52 INFO Login button clicked. 2025-01-24 16:07:56 INFO Login successful for user: abhishek 2025-01-24 16:07:56 INFO Login successful for user: abhishek 2025-01-24 16:07:56 INFO Login successful for user: abhishek 2025-01-24 16:07:56 INFO Login successful for user: abhishek 2025-01-24 16:07:56 INFO Database names fetched successfully. 2025-01-24 16:07:56 INFO Database names fetched successfully. 2025-01-24 16:07:56 INFO Database names fetched successfully. 2025-01-24 16:07:56 INFO Database names fetched successfully. 2025-01-24 16:09:43 INFO Date: 2025-01-24 ======================================== Time: 16:09:43 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 16:09:47 INFO not logined 2025-01-24 16:09:47 INFO Rendering unauthenticated menu. 2025-01-24 16:10:15 INFO Login button clicked. 2025-01-24 16:10:18 INFO Login successful for user: abhishek 2025-01-24 16:10:27 INFO Database names fetched successfully. 2025-01-24 16:10:53 INFO Database names fetched successfully. 2025-01-24 16:10:53 INFO Table details fetched successfully. 2025-01-24 16:11:22 INFO Database names fetched successfully. 2025-01-24 16:11:22 INFO Metadata fetched for table: NewAppointment 2025-01-24 16:11:47 INFO Database names fetched successfully. 2025-01-24 16:11:47 INFO Metadata fetched for table: Registration 2025-01-24 16:12:00 INFO Database names fetched successfully. 2025-01-24 16:12:00 INFO Metadata fetched for table: LabTests 2025-01-24 16:12:31 INFO Database names fetched successfully. 2025-01-24 16:12:31 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 16:12:33 INFO Tokens consumed: 2970 2025-01-24 16:12:34 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:12:36 INFO token updated successfully: 2025-01 2025-01-24 16:12:36 INFO token updated successfully. 2025-01-24 16:12:36 INFO Connected to the database MHealth_Dev. 2025-01-24 16:12:36 INFO Query executed successfully. 2025-01-24 16:12:37 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 55 2025-01-24 16:12:39 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:12:40 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/56.json 2025-01-24 16:13:27 INFO Database names fetched successfully. 2025-01-24 16:13:37 INFO Database names fetched successfully. 2025-01-24 16:13:37 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 16:13:39 INFO Tokens consumed: 2970 2025-01-24 16:13:41 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:13:42 INFO token updated successfully: 2025-01 2025-01-24 16:13:42 INFO token updated successfully. 2025-01-24 16:13:42 INFO Connected to the database MHealth_Dev. 2025-01-24 16:13:42 INFO Query executed successfully. 2025-01-24 16:13:44 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 56 2025-01-24 16:13:45 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:13:46 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/57.json 2025-01-24 16:14:03 INFO Database names fetched successfully. 2025-01-24 16:14:04 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 16:14:06 INFO Tokens consumed: 2970 2025-01-24 16:14:07 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:14:08 INFO token updated successfully: 2025-01 2025-01-24 16:14:08 INFO token updated successfully. 2025-01-24 16:14:08 INFO Connected to the database MHealth_Dev. 2025-01-24 16:14:08 INFO Query executed successfully. 2025-01-24 16:14:09 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 57 2025-01-24 16:14:11 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:14:12 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:14:31 INFO Database names fetched successfully. 2025-01-24 16:17:36 INFO Database names fetched successfully. 2025-01-24 16:17:38 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:17:39 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:17:39 INFO Query saved in the library with id 58. 2025-01-24 16:17:56 INFO Database names fetched successfully. 2025-01-24 16:17:56 INFO Metadata fetched for table: Registration 2025-01-24 16:19:13 INFO Database names fetched successfully. 2025-01-24 16:19:31 INFO Database names fetched successfully. 2025-01-24 16:20:09 INFO Database names fetched successfully. 2025-01-24 16:20:10 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:20:11 ERROR Exception while saving SQL query blob: The specified blob already exists. RequestId:bc5d8df8-901e-00b8-054d-6ec147000000 Time:2025-01-24T10:50:12.0611750Z ErrorCode:BlobAlreadyExists Content: BlobAlreadyExistsThe specified blob already exists. RequestId:bc5d8df8-901e-00b8-054d-6ec147000000 Time:2025-01-24T10:50:12.0611750Z 2025-01-24 16:20:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:20:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:20:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:20:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:20:25 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:20:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:20:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:20:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:20:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:20:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:20:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:20:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:20:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:20:52 INFO Connected to the database MHealth_Dev. 2025-01-24 16:20:52 INFO Query executed successfully. 2025-01-24 16:20:52 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:21:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:21:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:21:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:21:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:21:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:21:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:21:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:21:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:21:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:21:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:21:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:21:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:21:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:21:50 INFO Connected to the database MHealth_Dev. 2025-01-24 16:21:50 INFO Query executed successfully. 2025-01-24 16:21:50 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:21:50 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 16:21:53 INFO Tokens consumed: 906 2025-01-24 16:21:55 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:21:56 INFO token updated successfully: 2025-01 2025-01-24 16:21:56 INFO token updated successfully. 2025-01-24 16:21:57 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 3 2025-01-24 16:21:58 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:21:59 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/4.py 2025-01-24 16:21:59 INFO Code generated and written in generated_method//3.py 2025-01-24 16:21:59 WARNING result_df is not defined in the output dictionary 2025-01-24 16:22:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:22:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:22:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:22:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:22:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:22:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:22:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:22:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:22:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:22:25 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:22:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:26 INFO Connected to the database MHealth_Dev. 2025-01-24 16:22:26 INFO Query executed successfully. 2025-01-24 16:22:26 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:22:26 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 16:22:29 INFO Tokens consumed: 903 2025-01-24 16:22:31 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:22:32 INFO token updated successfully: 2025-01 2025-01-24 16:22:32 INFO token updated successfully. 2025-01-24 16:22:33 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 4 2025-01-24 16:22:34 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:22:35 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/5.py 2025-01-24 16:22:35 INFO Code generated and written in generated_method//4.py 2025-01-24 16:22:35 WARNING result_df is not defined in the output dictionary 2025-01-24 16:22:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:22:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:22:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:22:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:22:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:22:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:22:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:22:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:22:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:22:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:22:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:22:54 INFO Connected to the database MHealth_Dev. 2025-01-24 16:22:54 INFO Query executed successfully. 2025-01-24 16:22:54 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:22:54 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 16:22:56 INFO Tokens consumed: 860 2025-01-24 16:22:58 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:22:59 INFO token updated successfully: 2025-01 2025-01-24 16:22:59 INFO token updated successfully. 2025-01-24 16:23:01 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 5 2025-01-24 16:23:02 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:23:03 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/6.py 2025-01-24 16:23:03 INFO Code generated and written in generated_method//5.py 2025-01-24 16:23:03 WARNING result_df is not defined in the output dictionary 2025-01-24 16:23:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:23:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:23:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:23:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:23:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:23:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:23:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:23:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:23:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:23:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:23:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:23:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:23:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:23:17 INFO Connected to the database MHealth_Dev. 2025-01-24 16:23:17 INFO Query executed successfully. 2025-01-24 16:23:17 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:23:17 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 16:23:20 INFO Tokens consumed: 900 2025-01-24 16:23:22 INFO Existing token_consumed found for month: 2025-01 2025-01-24 16:23:23 INFO token updated successfully: 2025-01 2025-01-24 16:23:23 INFO token updated successfully. 2025-01-24 16:23:24 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 6 2025-01-24 16:23:25 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:23:26 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/7.py 2025-01-24 16:23:26 INFO Code generated and written in generated_method//6.py 2025-01-24 16:23:26 INFO Insight generated and displayed using AG Grid. 2025-01-24 16:24:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:24:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:24:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:24:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:24:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:24:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:24:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:24:48 INFO Connected to the database MHealth_Dev. 2025-01-24 16:24:48 INFO Query executed successfully. 2025-01-24 16:24:48 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:25:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:25:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:25:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:25:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:25:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:25:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:25:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:25:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:25:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:25:10 INFO Connected to the database MHealth_Dev. 2025-01-24 16:25:10 INFO Query executed successfully. 2025-01-24 16:25:10 INFO Dataset columns displayed using AG Grid. 2025-01-24 16:25:11 INFO No existing insight found for base code: SELECT * FROM NewAppointment; 2025-01-24 16:25:12 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-01-24 16:25:13 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-01-24 16:25:14 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 16:25:15 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:25 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:26 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:26 INFO Insight list generated successfully. 2025-01-24 16:25:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:34 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:34 INFO Insight list generated successfully. 2025-01-24 16:25:35 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:35 INFO Connected to the database MHealth_Dev. 2025-01-24 16:25:35 INFO Query executed successfully. 2025-01-24 16:25:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:45 INFO Insight list generated successfully. 2025-01-24 16:25:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:46 INFO Connected to the database MHealth_Dev. 2025-01-24 16:25:46 INFO Query executed successfully. 2025-01-24 16:25:54 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:55 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 16:25:55 INFO Insight list generated successfully. 2025-01-24 16:25:56 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:25:56 INFO Connected to the database MHealth_Dev. 2025-01-24 16:25:56 INFO Query executed successfully. 2025-01-24 16:28:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 16:28:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 16:28:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 16:28:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 16:28:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 16:28:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 17:37:40 INFO Date: 2025-01-24 ======================================== Time: 17:37:40 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 17:37:45 INFO not logined 2025-01-24 17:37:45 INFO Rendering unauthenticated menu. 2025-01-24 17:38:09 INFO Login button clicked. 2025-01-24 17:38:12 INFO Login successful for user: abhishek 2025-01-24 17:38:23 INFO Database names fetched successfully. 2025-01-24 17:39:35 INFO Database names fetched successfully. 2025-01-24 17:39:36 INFO Table details fetched successfully. 2025-01-24 17:42:19 INFO Database names fetched successfully. 2025-01-24 17:42:24 INFO Date: 2025-01-24 ======================================== Time: 17:42:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 17:42:24 INFO Date: 2025-01-24 ======================================== Time: 17:42:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 17:42:24 INFO not logined 2025-01-24 17:42:24 INFO not logined 2025-01-24 17:42:24 INFO Rendering unauthenticated menu. 2025-01-24 17:42:24 INFO Rendering unauthenticated menu. 2025-01-24 17:42:49 INFO Login button clicked. 2025-01-24 17:42:53 INFO Login successful for user: abhishek 2025-01-24 17:42:53 INFO Login successful for user: abhishek 2025-01-24 17:42:53 INFO Database names fetched successfully. 2025-01-24 17:42:59 INFO Database names fetched successfully. 2025-01-24 17:42:59 INFO Database names fetched successfully. 2025-01-24 17:42:59 INFO Table details fetched successfully. 2025-01-24 17:42:59 INFO Table details fetched successfully. 2025-01-24 17:44:49 INFO Date: 2025-01-24 ======================================== Time: 17:44:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 17:44:53 INFO not logined 2025-01-24 17:44:53 INFO Rendering unauthenticated menu. 2025-01-24 17:45:29 INFO Login button clicked. 2025-01-24 17:45:33 INFO Login successful for user: abhishek 2025-01-24 17:45:41 INFO Database names fetched successfully. 2025-01-24 17:46:02 INFO Database names fetched successfully. 2025-01-24 17:46:02 INFO Table details fetched successfully. 2025-01-24 17:53:48 INFO Database names fetched successfully. 2025-01-24 17:53:51 INFO Database names fetched successfully. 2025-01-24 17:53:51 INFO Metadata fetched for table: NewAppointment 2025-01-24 17:54:23 INFO Database names fetched successfully. 2025-01-24 17:54:23 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 17:54:25 INFO Tokens consumed: 2970 2025-01-24 17:54:27 INFO Existing token_consumed found for month: 2025-01 2025-01-24 17:54:28 INFO token updated successfully: 2025-01 2025-01-24 17:54:28 INFO token updated successfully. 2025-01-24 17:54:28 INFO Connected to the database MHealth_Dev. 2025-01-24 17:54:28 INFO Query executed successfully. 2025-01-24 17:54:29 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 58 2025-01-24 17:54:31 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 17:54:32 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/59.json 2025-01-24 17:55:00 INFO Database names fetched successfully. 2025-01-24 17:55:10 INFO Database names fetched successfully. 2025-01-24 17:55:10 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the registratiom```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 17:55:14 INFO Tokens consumed: 2970 2025-01-24 17:55:16 INFO Existing token_consumed found for month: 2025-01 2025-01-24 17:55:17 INFO token updated successfully: 2025-01 2025-01-24 17:55:17 INFO token updated successfully. 2025-01-24 17:55:17 INFO Connected to the database MHealth_Dev. 2025-01-24 17:55:17 INFO Query executed successfully. 2025-01-24 17:55:18 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 59 2025-01-24 17:55:20 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 17:55:21 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/60.json 2025-01-24 17:58:22 INFO Database names fetched successfully. 2025-01-24 17:58:22 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-24 17:58:24 INFO Tokens consumed: 2968 2025-01-24 17:58:26 INFO Existing token_consumed found for month: 2025-01 2025-01-24 17:58:28 INFO token updated successfully: 2025-01 2025-01-24 17:58:28 INFO token updated successfully. 2025-01-24 17:58:28 INFO Connected to the database MHealth_Dev. 2025-01-24 17:58:28 INFO Query executed successfully. 2025-01-24 17:58:29 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 60 2025-01-24 17:58:30 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 17:58:31 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/61.json 2025-01-24 17:58:38 INFO Database names fetched successfully. 2025-01-24 17:59:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 17:59:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 17:59:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 17:59:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 17:59:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 17:59:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 17:59:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 17:59:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 17:59:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 17:59:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 17:59:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 17:59:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 17:59:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 17:59:33 INFO Connected to the database MHealth_Dev. 2025-01-24 17:59:33 INFO Query executed successfully. 2025-01-24 17:59:33 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:00:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:00:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:00:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:00:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:00:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:00:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:00:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:00:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:00:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:00:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:00:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:00:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:00:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:00:11 INFO Connected to the database MHealth_Dev. 2025-01-24 18:00:11 INFO Query executed successfully. 2025-01-24 18:00:11 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:00:11 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:00:14 INFO Tokens consumed: 904 2025-01-24 18:00:15 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:00:16 INFO token updated successfully: 2025-01 2025-01-24 18:00:16 INFO token updated successfully. 2025-01-24 18:00:18 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 7 2025-01-24 18:00:19 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:00:20 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/8.py 2025-01-24 18:00:20 INFO Code generated and written in generated_method//7.py 2025-01-24 18:00:20 WARNING result_df is not defined in the output dictionary 2025-01-24 18:02:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:02:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:02:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:02:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:02:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:02:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:11 INFO Connected to the database MHealth_Dev. 2025-01-24 18:02:11 INFO Query executed successfully. 2025-01-24 18:02:11 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:02:11 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:02:14 INFO Tokens consumed: 887 2025-01-24 18:02:15 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:02:17 INFO token updated successfully: 2025-01 2025-01-24 18:02:17 INFO token updated successfully. 2025-01-24 18:02:18 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 8 2025-01-24 18:02:19 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:02:20 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/9.py 2025-01-24 18:02:20 INFO Code generated and written in generated_method//8.py 2025-01-24 18:02:20 WARNING result_df is not defined in the output dictionary 2025-01-24 18:02:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:02:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:02:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:02:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:02:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:02:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:02:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:02:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:02:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:02:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:02:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:34 INFO Connected to the database MHealth_Dev. 2025-01-24 18:02:34 INFO Query executed successfully. 2025-01-24 18:02:34 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:02:34 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:02:43 INFO Tokens consumed: 885 2025-01-24 18:02:44 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:02:45 INFO token updated successfully: 2025-01 2025-01-24 18:02:45 INFO token updated successfully. 2025-01-24 18:02:46 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 9 2025-01-24 18:02:48 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:02:49 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/10.py 2025-01-24 18:02:49 INFO Code generated and written in generated_method//9.py 2025-01-24 18:02:49 INFO Insight generated and displayed using AG Grid. 2025-01-24 18:02:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:02:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:02:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:03:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:03:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:03:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:03:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:03:03 INFO Connected to the database MHealth_Dev. 2025-01-24 18:03:03 INFO Query executed successfully. 2025-01-24 18:03:04 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:04:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:04:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:04:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:04:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:04:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:04:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:04:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:04:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:04:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:04:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:04:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:04:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:04:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:04:55 INFO Connected to the database MHealth_Dev. 2025-01-24 18:04:55 INFO Query executed successfully. 2025-01-24 18:04:55 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:04:55 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:04:59 INFO Tokens consumed: 888 2025-01-24 18:05:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:05:02 INFO token updated successfully: 2025-01 2025-01-24 18:05:02 INFO token updated successfully. 2025-01-24 18:05:04 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 10 2025-01-24 18:05:05 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:05:06 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/11.py 2025-01-24 18:05:06 INFO Code generated and written in generated_method//10.py 2025-01-24 18:05:06 WARNING result_df is not defined in the output dictionary 2025-01-24 18:05:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:05:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:05:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:05:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:05:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:05:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:05:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:05:17 INFO Connected to the database MHealth_Dev. 2025-01-24 18:05:17 INFO Query executed successfully. 2025-01-24 18:05:17 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:05:17 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:05:21 INFO Tokens consumed: 885 2025-01-24 18:05:22 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:05:23 INFO token updated successfully: 2025-01 2025-01-24 18:05:23 INFO token updated successfully. 2025-01-24 18:05:24 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 11 2025-01-24 18:05:26 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:05:27 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/12.py 2025-01-24 18:05:27 INFO Code generated and written in generated_method//11.py 2025-01-24 18:05:27 INFO Insight generated and displayed using AG Grid. 2025-01-24 18:05:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:05:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:05:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:05:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:05:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:05:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:05:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:05:38 INFO Connected to the database MHealth_Dev. 2025-01-24 18:05:38 INFO Query executed successfully. 2025-01-24 18:05:38 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:09:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:09:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:09:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:09:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:09:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:09:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:14 INFO Connected to the database MHealth_Dev. 2025-01-24 18:09:14 INFO Query executed successfully. 2025-01-24 18:09:14 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:09:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:09:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:09:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:09:25 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:09:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:09:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:27 INFO Connected to the database MHealth_Dev. 2025-01-24 18:09:27 INFO Query executed successfully. 2025-01-24 18:09:27 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:09:27 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:09:30 INFO Tokens consumed: 885 2025-01-24 18:09:31 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:09:33 INFO token updated successfully: 2025-01 2025-01-24 18:09:33 INFO token updated successfully. 2025-01-24 18:09:34 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 12 2025-01-24 18:09:35 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:09:37 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/13.py 2025-01-24 18:09:37 INFO Code generated and written in generated_method//12.py 2025-01-24 18:09:37 WARNING result_df is not defined in the output dictionary 2025-01-24 18:09:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:09:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:09:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:09:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:09:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:09:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:09:47 INFO Connected to the database MHealth_Dev. 2025-01-24 18:09:47 INFO Query executed successfully. 2025-01-24 18:09:47 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:09:47 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:09:51 INFO Tokens consumed: 888 2025-01-24 18:09:52 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:09:54 INFO token updated successfully: 2025-01 2025-01-24 18:09:54 INFO token updated successfully. 2025-01-24 18:09:55 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 13 2025-01-24 18:09:56 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:09:57 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/14.py 2025-01-24 18:09:57 INFO Code generated and written in generated_method//13.py 2025-01-24 18:09:57 WARNING result_df is not defined in the output dictionary 2025-01-24 18:10:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:10:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:10:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:10:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:10:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:10:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:10:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:10:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:10:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:10:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:10:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:11 INFO Connected to the database MHealth_Dev. 2025-01-24 18:10:11 INFO Query executed successfully. 2025-01-24 18:10:11 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:10:11 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:10:14 INFO Tokens consumed: 888 2025-01-24 18:10:16 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:10:18 INFO token updated successfully: 2025-01 2025-01-24 18:10:18 INFO token updated successfully. 2025-01-24 18:10:19 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 14 2025-01-24 18:10:21 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:10:22 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/15.py 2025-01-24 18:10:22 INFO Code generated and written in generated_method//14.py 2025-01-24 18:10:22 WARNING result_df is not defined in the output dictionary 2025-01-24 18:10:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:10:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:10:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:10:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:10:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:10:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:10:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:10:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:10:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:10:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:10:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:10:40 INFO Connected to the database MHealth_Dev. 2025-01-24 18:10:41 INFO Query executed successfully. 2025-01-24 18:10:41 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:10:41 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:10:44 INFO Tokens consumed: 904 2025-01-24 18:10:46 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:10:47 INFO token updated successfully: 2025-01 2025-01-24 18:10:47 INFO token updated successfully. 2025-01-24 18:10:49 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 15 2025-01-24 18:10:50 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:10:51 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/16.py 2025-01-24 18:10:51 INFO Code generated and written in generated_method//15.py 2025-01-24 18:10:51 WARNING result_df is not defined in the output dictionary 2025-01-24 18:11:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:11:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:11:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:11:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:11:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:11:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:11:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:11:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:11:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:11:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:11:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:11:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:11:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:11:11 INFO Connected to the database MHealth_Dev. 2025-01-24 18:11:11 INFO Query executed successfully. 2025-01-24 18:11:11 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:11:11 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:11:14 INFO Tokens consumed: 887 2025-01-24 18:11:17 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:11:18 INFO token updated successfully: 2025-01 2025-01-24 18:11:18 INFO token updated successfully. 2025-01-24 18:11:20 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 16 2025-01-24 18:11:22 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:11:23 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/17.py 2025-01-24 18:11:23 INFO Code generated and written in generated_method//16.py 2025-01-24 18:11:23 WARNING result_df is not defined in the output dictionary 2025-01-24 18:11:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:11:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:11:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:11:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:11:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:11:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:11:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:11:40 INFO Connected to the database MHealth_Dev. 2025-01-24 18:11:40 INFO Query executed successfully. 2025-01-24 18:11:40 INFO Dataset columns displayed using AG Grid. 2025-01-24 18:11:40 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight with all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 18:11:43 INFO Tokens consumed: 885 2025-01-24 18:11:45 INFO Existing token_consumed found for month: 2025-01 2025-01-24 18:11:47 INFO token updated successfully: 2025-01 2025-01-24 18:11:47 INFO token updated successfully. 2025-01-24 18:11:48 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 17 2025-01-24 18:11:50 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 18:11:51 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/18.py 2025-01-24 18:11:51 INFO Code generated and written in generated_method//17.py 2025-01-24 18:11:51 INFO Insight generated and displayed using AG Grid. 2025-01-24 18:12:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:12:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 18:12:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 18:12:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 18:12:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 18:12:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 18:12:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 18:12:08 INFO Connected to the database MHealth_Dev. 2025-01-24 18:12:08 INFO Query executed successfully. 2025-01-24 18:12:08 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:20:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:20:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:20:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:20:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:20:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:20:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:20:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:20:45 INFO Connected to the database MHealth_Dev. 2025-01-24 19:20:45 INFO Query executed successfully. 2025-01-24 19:20:45 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:23:16 INFO Date: 2025-01-24 ======================================== Time: 19:23:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:23:21 INFO not logined 2025-01-24 19:23:21 INFO Rendering unauthenticated menu. 2025-01-24 19:23:40 INFO Login button clicked. 2025-01-24 19:23:44 INFO Login successful for user: abhishek 2025-01-24 19:23:57 INFO Database names fetched successfully. 2025-01-24 19:24:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:24:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:24:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:24:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:24:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:24:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:25:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:25:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:25:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:25:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:25:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:25:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:25:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:25:12 INFO Connected to the database MHealth_Dev. 2025-01-24 19:25:12 INFO Query executed successfully. 2025-01-24 19:25:12 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:26:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:26:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:26:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:26:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:26:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:26:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:26:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:26:24 INFO Connected to the database MHealth_Dev. 2025-01-24 19:26:24 INFO Query executed successfully. 2025-01-24 19:26:24 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:26:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:26:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:26:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:26:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:26:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:26:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:26:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:26:50 INFO Connected to the database MHealth_Dev. 2025-01-24 19:26:50 INFO Query executed successfully. 2025-01-24 19:26:50 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:30:16 INFO Date: 2025-01-24 ======================================== Time: 19:30:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:30:22 INFO not logined 2025-01-24 19:30:22 INFO Rendering unauthenticated menu. 2025-01-24 19:36:58 INFO Date: 2025-01-24 ======================================== Time: 19:36:58 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:37:03 INFO not logined 2025-01-24 19:37:03 INFO Rendering unauthenticated menu. 2025-01-24 19:37:38 INFO Login button clicked. 2025-01-24 19:37:42 INFO Login successful for user: abhishek 2025-01-24 19:37:55 INFO Database names fetched successfully. 2025-01-24 19:38:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:38:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:38:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:38:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:38:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:38:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:39:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:39:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:39:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:39:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:39:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:39:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:39:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:39:11 INFO Connected to the database MHealth_Dev. 2025-01-24 19:39:11 INFO Query executed successfully. 2025-01-24 19:39:11 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:40:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:40:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:40:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:40:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:40:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:40:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:40:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:40:50 INFO Connected to the database MHealth_Dev. 2025-01-24 19:40:50 INFO Query executed successfully. 2025-01-24 19:40:50 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:40:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:41:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:41:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:41:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:41:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:41:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:41:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:41:06 INFO Connected to the database MHealth_Dev. 2025-01-24 19:41:06 INFO Query executed successfully. 2025-01-24 19:41:06 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:41:28 INFO Date: 2025-01-24 ======================================== Time: 19:41:28 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:41:28 INFO Date: 2025-01-24 ======================================== Time: 19:41:28 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:41:28 INFO not logined 2025-01-24 19:41:28 INFO not logined 2025-01-24 19:41:28 INFO Rendering unauthenticated menu. 2025-01-24 19:41:28 INFO Rendering unauthenticated menu. 2025-01-24 19:41:58 INFO Login button clicked. 2025-01-24 19:41:58 INFO Login button clicked. 2025-01-24 19:42:02 INFO Login successful for user: abhishek 2025-01-24 19:42:02 INFO Database names fetched successfully. 2025-01-24 19:42:02 INFO Database names fetched successfully. 2025-01-24 19:42:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:42:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:42:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:42:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:42:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:42:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:42:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:42:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:42:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:42:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:42:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:42:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:43:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:43:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:43:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:43:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:43:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:43:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:43:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:43:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:43:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:43:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:43:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:43:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:43:12 INFO Connected to the database MHealth_Dev. 2025-01-24 19:43:12 INFO Connected to the database MHealth_Dev. 2025-01-24 19:43:12 INFO Query executed successfully. 2025-01-24 19:43:12 INFO Query executed successfully. 2025-01-24 19:43:12 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:43:12 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:46:34 INFO Date: 2025-01-24 ======================================== Time: 19:46:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 19:46:40 INFO not logined 2025-01-24 19:46:40 INFO Rendering unauthenticated menu. 2025-01-24 19:47:00 INFO Login button clicked. 2025-01-24 19:47:04 INFO Login successful for user: abhishek 2025-01-24 19:47:16 INFO Database names fetched successfully. 2025-01-24 19:47:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:47:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:47:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:47:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:47:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:47:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:47:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:47:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:47:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:47:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:47:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:47:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:48:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:48:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:48:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:48:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:48:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:48:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:50:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:50:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:50:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:50:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:50:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:50:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:50:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:50:10 INFO Connected to the database MHealth_Dev. 2025-01-24 19:50:10 INFO Query executed successfully. 2025-01-24 19:50:10 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:51:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:51:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:51:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:51:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:51:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:51:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:51:25 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:51:25 INFO Connected to the database MHealth_Dev. 2025-01-24 19:51:25 INFO Query executed successfully. 2025-01-24 19:51:25 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:51:25 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointment`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 19:51:30 INFO Tokens consumed: 1053 2025-01-24 19:51:31 INFO Existing token_consumed found for month: 2025-01 2025-01-24 19:51:33 INFO token updated successfully: 2025-01 2025-01-24 19:51:33 INFO token updated successfully. 2025-01-24 19:51:35 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 18 2025-01-24 19:51:36 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 19:51:37 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/19.py 2025-01-24 19:51:37 INFO Code generated and written in generated_method//18.py 2025-01-24 19:51:37 INFO Insight generated and displayed using AG Grid. 2025-01-24 19:52:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:52:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:52:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:52:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:52:10 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:52:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:52:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:52:12 INFO Connected to the database MHealth_Dev. 2025-01-24 19:52:12 INFO Query executed successfully. 2025-01-24 19:52:12 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:52:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:52:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:52:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:53:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:53:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:53:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:53:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:53:04 INFO Connected to the database MHealth_Dev. 2025-01-24 19:53:04 INFO Query executed successfully. 2025-01-24 19:53:04 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:53:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:53:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:53:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:53:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:53:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:53:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:53:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:53:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:53:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:53:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:53:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:53:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:53:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:53:38 INFO Connected to the database MHealth_Dev. 2025-01-24 19:53:38 INFO Query executed successfully. 2025-01-24 19:53:39 INFO Dataset columns displayed using AG Grid. 2025-01-24 19:53:39 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['AppointmentId', 'ScheduleId', 'SlotId', 'EncounterId', 'ConditionId', 'ChiefComplaint', 'PatientId', 'PractitionerId', 'CreatedDate', 'LastUpdatedDate', 'IsNewAppointment', 'PreviousAppointmentId', 'PreviousEncounterId', 'PreviousConditionId', 'PreviousConsultationId'], 'dtypes': {'AppointmentId': 'object', 'ScheduleId': 'object', 'SlotId': 'object', 'EncounterId': 'object', 'ConditionId': 'object', 'ChiefComplaint': 'object', 'PatientId': 'object', 'PractitionerId': 'object', 'CreatedDate': 'datetime64[ns]', 'LastUpdatedDate': 'datetime64[ns]', 'IsNewAppointment': 'object', 'PreviousAppointmentId': 'object', 'PreviousEncounterId': 'object', 'PreviousConditionId': 'object', 'PreviousConsultationId': 'object'}, 'describe': {'CreatedDate': {'count': 782, 'mean': Timestamp('2024-03-31 08:10:28.655653632'), 'min': Timestamp('2024-02-02 12:34:02.290000'), '25%': Timestamp('2024-03-11 11:57:21.101750016'), '50%': Timestamp('2024-04-03 17:31:57.520000'), '75%': Timestamp('2024-04-22 10:59:24.021000192'), 'max': Timestamp('2024-05-14 15:13:11.857000')}, 'LastUpdatedDate': {'count': 782, 'mean': Timestamp('2024-03-31 08:10:28.655653632'), 'min': Timestamp('2024-02-02 12:34:02.290000'), '25%': Timestamp('2024-03-11 11:57:21.101750016'), '50%': Timestamp('2024-04-03 17:31:57.520000'), '75%': Timestamp('2024-04-22 10:59:24.021000192'), 'max': Timestamp('2024-05-14 15:13:11.857000')}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a pie chart of the no of patient for each partitioner``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-24 19:53:44 INFO Tokens consumed: 1617 2025-01-24 19:53:46 INFO Existing token_consumed found for month: 2025-01 2025-01-24 19:53:49 INFO token updated successfully: 2025-01 2025-01-24 19:53:49 INFO token updated successfully. 2025-01-24 19:53:53 INFO Plotly chart object created successfully. 2025-01-24 19:53:53 INFO Graph generated and displayed using Plotly. 2025-01-24 19:54:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:54:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:54:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:54:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 19:54:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:54:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 19:54:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:54:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 19:54:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:54:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 19:54:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:54:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 19:54:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 19:54:40 INFO Connected to the database MHealth_Dev. 2025-01-24 19:54:40 INFO Query executed successfully. 2025-01-24 19:54:40 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:13:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:13:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:13:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:13:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:13:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:13:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:13:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:13:57 INFO Connected to the database Insightlab. 2025-01-24 20:13:57 INFO Query executed successfully. 2025-01-24 20:13:57 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:14:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:14:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:14:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:14:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:14:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:14:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:14:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:14:35 INFO Connected to the database Insightlab. 2025-01-24 20:14:35 INFO Query executed successfully. 2025-01-24 20:14:35 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:15:14 INFO User logged out. 2025-01-24 20:15:14 INFO Date: 2025-01-24 ======================================== Time: 20:15:14 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 20:15:14 INFO Date: 2025-01-24 ======================================== Time: 20:15:14 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-24 20:15:14 INFO not logined 2025-01-24 20:15:14 INFO Rendering unauthenticated menu. 2025-01-24 20:15:14 INFO Rendering unauthenticated menu. 2025-01-24 20:15:44 INFO Login button clicked. 2025-01-24 20:15:44 INFO Login button clicked. 2025-01-24 20:15:48 INFO Login successful for user: abhishek 2025-01-24 20:15:48 INFO Login successful for user: abhishek 2025-01-24 20:15:48 INFO Database names fetched successfully. 2025-01-24 20:15:48 INFO Database names fetched successfully. 2025-01-24 20:15:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:15:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:15:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:15:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:15:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:15:58 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:15:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:15:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:16:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:16:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:16:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:16:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:20 INFO Connected to the database Insightlab. 2025-01-24 20:16:20 INFO Connected to the database Insightlab. 2025-01-24 20:16:20 INFO Query executed successfully. 2025-01-24 20:16:20 INFO Query executed successfully. 2025-01-24 20:16:20 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:16:20 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:16:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:16:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:16:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:16:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:16:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:16:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:16:56 INFO Connected to the database Insightlab. 2025-01-24 20:16:56 INFO Connected to the database Insightlab. 2025-01-24 20:16:56 INFO Query executed successfully. 2025-01-24 20:16:56 INFO Query executed successfully. 2025-01-24 20:16:56 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:16:56 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:16:56 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 20:16:56 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 20:16:59 INFO Tokens consumed: 905 2025-01-24 20:16:59 INFO Tokens consumed: 905 2025-01-24 20:17:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:17:01 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:17:03 INFO token updated successfully: 2025-01 2025-01-24 20:17:03 INFO token updated successfully: 2025-01 2025-01-24 20:17:03 INFO token updated successfully. 2025-01-24 20:17:03 INFO token updated successfully. 2025-01-24 20:17:04 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 19 2025-01-24 20:17:04 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 19 2025-01-24 20:17:06 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 20:17:06 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 20:17:07 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/20.py 2025-01-24 20:17:07 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/20.py 2025-01-24 20:17:07 INFO Code generated and written in generated_method//19.py 2025-01-24 20:17:07 INFO Code generated and written in generated_method//19.py 2025-01-24 20:17:07 WARNING result_df is not defined in the output dictionary 2025-01-24 20:17:07 WARNING result_df is not defined in the output dictionary 2025-01-24 20:17:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:17:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:17:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:17:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:17:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:17:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:17:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:17:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:17:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:17:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:17:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:17:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:17:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:17:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:17:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:17:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:17:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:17:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:17:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:17:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:17:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:17:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:17:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:17:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:17:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:17:24 INFO Connected to the database Insightlab. 2025-01-24 20:17:24 INFO Connected to the database Insightlab. 2025-01-24 20:17:24 INFO Query executed successfully. 2025-01-24 20:17:24 INFO Query executed successfully. 2025-01-24 20:17:24 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:17:24 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:17:24 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 20:17:24 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-24 20:17:27 INFO Tokens consumed: 944 2025-01-24 20:17:29 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:17:29 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:17:32 INFO token updated successfully: 2025-01 2025-01-24 20:17:32 INFO token updated successfully: 2025-01 2025-01-24 20:17:32 INFO token updated successfully. 2025-01-24 20:17:32 INFO token updated successfully. 2025-01-24 20:17:33 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 20 2025-01-24 20:17:35 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-01-24 20:17:36 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/21.py 2025-01-24 20:17:36 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/21.py 2025-01-24 20:17:36 INFO Code generated and written in generated_method//20.py 2025-01-24 20:17:36 INFO Code generated and written in generated_method//20.py 2025-01-24 20:17:36 INFO Insight generated and displayed using AG Grid. 2025-01-24 20:17:36 INFO Insight generated and displayed using AG Grid. 2025-01-24 20:18:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:18:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:18:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:18:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:18:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:18:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:18:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:18:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:18:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:18:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:18:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:18:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:18:25 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:18:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:18:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:18:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:18:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:18:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:18:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:18:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:18:28 INFO Connected to the database Insightlab. 2025-01-24 20:18:28 INFO Connected to the database Insightlab. 2025-01-24 20:18:28 INFO Query executed successfully. 2025-01-24 20:18:28 INFO Query executed successfully. 2025-01-24 20:18:28 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:18:28 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:18:28 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient with average age``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-24 20:18:28 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime 19 risk_score float64`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start', 'risk_score'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object', 'risk_score': 'float64'}, 'describe': {'Age': {'count': 20.0, 'mean': 64.35, 'std': 5.234249556627257, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 68.25, 'max': 73.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12662.05, 'std': 1722.9582111497718, 'min': 10001.0, '25%': 10701.0, '50%': 13201.5, '75%': 14202.0, 'max': 14605.0}, 'risk_score': {'count': 20.0, 'mean': 0.7724999964237214, 'std': 0.16025884270617705, 'min': 0.5199999809265137, '25%': 0.6500000059604645, '50%': 0.8149999976158142, '75%': 0.9024999886751175, 'max': 0.9900000095367432}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient with average age``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-24 20:18:54 INFO Tokens consumed: 1697 2025-01-24 20:18:55 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:18:55 INFO Existing token_consumed found for month: 2025-01 2025-01-24 20:18:57 INFO token updated successfully: 2025-01 2025-01-24 20:18:57 INFO token updated successfully: 2025-01 2025-01-24 20:18:57 INFO token updated successfully. 2025-01-24 20:18:57 INFO token updated successfully. 2025-01-24 20:18:58 INFO Plotly chart object created successfully. 2025-01-24 20:18:58 INFO Plotly chart object created successfully. 2025-01-24 20:18:58 INFO Graph generated and displayed using Plotly. 2025-01-24 20:18:58 INFO Graph generated and displayed using Plotly. 2025-01-24 20:19:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:19:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-01-24 20:19:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-01-24 20:19:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:19:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:19:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:19:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-01-24 20:19:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:19:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-01-24 20:19:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:19:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-01-24 20:19:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:19:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-01-24 20:19:35 INFO Connected to the database MHealth_Dev. 2025-01-24 20:19:35 INFO Connected to the database MHealth_Dev. 2025-01-24 20:19:35 INFO Query executed successfully. 2025-01-24 20:19:35 INFO Query executed successfully. 2025-01-24 20:19:35 INFO Dataset columns displayed using AG Grid. 2025-01-24 20:19:35 INFO Dataset columns displayed using AG Grid. 2025-01-27 00:40:24 INFO Date: 2025-01-27 ======================================== Time: 00:40:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 00:40:28 INFO not logined 2025-01-27 00:40:28 INFO Rendering unauthenticated menu. 2025-01-27 00:40:56 INFO Login button clicked. 2025-01-27 00:41:00 INFO Login successful for user: maheshsr 2025-01-27 00:41:10 INFO Database names fetched successfully. 2025-01-27 09:55:45 INFO Date: 2025-01-27 ======================================== Time: 09:55:45 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 09:55:51 INFO not logined 2025-01-27 09:55:51 INFO Rendering unauthenticated menu. 2025-01-27 09:56:17 INFO Login button clicked. 2025-01-27 09:56:20 INFO Login successful for user: maheshsr 2025-01-27 09:56:34 INFO Database names fetched successfully. 2025-01-27 09:57:39 INFO Database names fetched successfully. 2025-01-27 09:57:44 INFO Database names fetched successfully. 2025-01-27 09:57:54 INFO Database names fetched successfully. 2025-01-27 09:57:57 INFO Database names fetched successfully. 2025-01-27 09:58:00 INFO Database names fetched successfully. 2025-01-27 09:58:01 INFO Table details fetched successfully. 2025-01-27 09:58:29 INFO Database names fetched successfully. 2025-01-27 09:58:29 INFO Metadata fetched for table: NewAppointment 2025-01-27 10:01:40 INFO Database names fetched successfully. 2025-01-27 10:01:40 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 10:01:42 INFO Tokens consumed: 2970 2025-01-27 10:01:44 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:01:45 INFO token updated successfully: 2025-01 2025-01-27 10:01:45 INFO token updated successfully. 2025-01-27 10:01:45 INFO Connected to the database MHealth_Dev. 2025-01-27 10:01:45 INFO Query executed successfully. 2025-01-27 10:01:47 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 136 2025-01-27 10:01:49 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:01:50 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/137.json 2025-01-27 10:02:15 INFO Database names fetched successfully. 2025-01-27 10:02:15 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 10:02:17 INFO Tokens consumed: 2969 2025-01-27 10:02:19 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:02:20 INFO token updated successfully: 2025-01 2025-01-27 10:02:20 INFO token updated successfully. 2025-01-27 10:02:20 INFO Connected to the database MHealth_Dev. 2025-01-27 10:02:20 INFO Query executed successfully. 2025-01-27 10:02:21 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 137 2025-01-27 10:02:23 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:02:24 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/138.json 2025-01-27 10:02:36 INFO Database names fetched successfully. 2025-01-27 10:02:36 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the medication```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 10:02:38 INFO Tokens consumed: 2968 2025-01-27 10:02:40 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:02:41 INFO token updated successfully: 2025-01 2025-01-27 10:02:41 INFO token updated successfully. 2025-01-27 10:02:41 INFO Connected to the database MHealth_Dev. 2025-01-27 10:02:41 INFO Query executed successfully. 2025-01-27 10:02:43 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 138 2025-01-27 10:02:45 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:02:46 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/139.json 2025-01-27 10:07:36 INFO Date: 2025-01-27 ======================================== Time: 10:07:36 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 10:07:40 INFO not logined 2025-01-27 10:07:40 INFO Rendering unauthenticated menu. 2025-01-27 10:08:20 INFO Login button clicked. 2025-01-27 10:08:24 INFO Login successful for user: maheshsr 2025-01-27 10:08:32 INFO Database names fetched successfully. 2025-01-27 10:08:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:08:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:08:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:08:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:08:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:08:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:08:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:08:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:09:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:09:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:09:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:09:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:09:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:09:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:09:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:09:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:09:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:09:30 INFO Connected to the database MHealth_Dev. 2025-01-27 10:09:30 INFO Query executed successfully. 2025-01-27 10:09:30 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:12:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:12:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:12:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:12:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:12:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:12:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:12:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:12:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:12:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:12:30 INFO Connected to the database MHealth_Dev. 2025-01-27 10:12:30 INFO Query executed successfully. 2025-01-27 10:12:30 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:14:27 INFO Date: 2025-01-27 ======================================== Time: 10:14:27 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 10:14:31 INFO not logined 2025-01-27 10:14:31 INFO Rendering unauthenticated menu. 2025-01-27 10:14:49 INFO Login button clicked. 2025-01-27 10:14:53 INFO Login successful for user: maheshsr 2025-01-27 10:15:01 INFO Database names fetched successfully. 2025-01-27 10:15:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:15:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:15:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:15:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:15:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:15:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:15:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:15:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:15:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:15:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:15:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:15:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:15:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:15:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:15:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:15:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:15:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:15:54 INFO Connected to the database MHealth_Dev. 2025-01-27 10:15:54 INFO Query executed successfully. 2025-01-27 10:15:54 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:16:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:16:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:16:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:16:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:16:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:16:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:16:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:16:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:16:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:16:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:16:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:16:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:16:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:16:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:16:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:16:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:16:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:16:54 INFO Connected to the database MHealth_Dev. 2025-01-27 10:16:54 INFO Query executed successfully. 2025-01-27 10:16:54 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:16:54 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:16:58 INFO Tokens consumed: 928 2025-01-27 10:16:59 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:17:00 INFO token updated successfully: 2025-01 2025-01-27 10:17:00 INFO token updated successfully. 2025-01-27 10:17:03 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 177 2025-01-27 10:17:04 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:17:05 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/178.py 2025-01-27 10:17:05 INFO Code generated and written in generated_method//177.py 2025-01-27 10:17:05 WARNING result_df is not defined in the output dictionary 2025-01-27 10:18:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:18:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:18:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:18:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:18:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:18:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:18:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:18:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:18:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:18:09 INFO Connected to the database MHealth_Dev. 2025-01-27 10:18:09 INFO Query executed successfully. 2025-01-27 10:18:09 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:18:09 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:18:13 INFO Tokens consumed: 1017 2025-01-27 10:18:15 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:18:16 INFO token updated successfully: 2025-01 2025-01-27 10:18:16 INFO token updated successfully. 2025-01-27 10:18:18 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 178 2025-01-27 10:18:20 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:18:21 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/179.py 2025-01-27 10:18:21 INFO Code generated and written in generated_method//178.py 2025-01-27 10:18:21 INFO Insight generated and displayed using AG Grid. 2025-01-27 10:18:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:18:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:18:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:19:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:19:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:19:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:19:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:19:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:19:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:19:05 INFO Connected to the database MHealth_Dev. 2025-01-27 10:19:05 INFO Query executed successfully. 2025-01-27 10:19:05 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:19:05 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:19:09 INFO Tokens consumed: 1019 2025-01-27 10:19:10 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:19:12 INFO token updated successfully: 2025-01 2025-01-27 10:19:12 INFO token updated successfully. 2025-01-27 10:19:14 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 179 2025-01-27 10:19:16 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:19:17 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/180.py 2025-01-27 10:19:17 INFO Code generated and written in generated_method//179.py 2025-01-27 10:19:17 WARNING result_df is not defined in the output dictionary 2025-01-27 10:19:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:19:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:19:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:19:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:19:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:19:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:19:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:19:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:19:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:19:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:19:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:19:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:19:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:19:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:19:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:19:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:19:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:19:46 INFO Connected to the database MHealth_Dev. 2025-01-27 10:19:46 INFO Query executed successfully. 2025-01-27 10:19:46 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:19:46 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:19:49 INFO Tokens consumed: 859 2025-01-27 10:19:50 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:19:52 INFO token updated successfully: 2025-01 2025-01-27 10:19:52 INFO token updated successfully. 2025-01-27 10:19:54 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 180 2025-01-27 10:19:56 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:19:57 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/181.py 2025-01-27 10:19:57 INFO Code generated and written in generated_method//180.py 2025-01-27 10:19:57 WARNING result_df is not defined in the output dictionary 2025-01-27 10:21:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:21:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:21:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:21:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:21:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:21:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:21:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:21:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:21:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:21:41 INFO Connected to the database Insightlab. 2025-01-27 10:21:41 INFO Query executed successfully. 2025-01-27 10:21:41 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:22:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:22:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:22:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:22:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:22:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:22:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:22:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:22:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:22:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:22:28 INFO Connected to the database Insightlab. 2025-01-27 10:22:28 INFO Query executed successfully. 2025-01-27 10:22:28 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:22:28 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:22:31 INFO Tokens consumed: 958 2025-01-27 10:22:32 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:22:34 INFO token updated successfully: 2025-01 2025-01-27 10:22:34 INFO token updated successfully. 2025-01-27 10:22:36 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 181 2025-01-27 10:22:38 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:22:39 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/182.py 2025-01-27 10:22:39 INFO Code generated and written in generated_method//181.py 2025-01-27 10:22:39 WARNING result_df is not defined in the output dictionary 2025-01-27 10:23:23 INFO Date: 2025-01-27 ======================================== Time: 10:23:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 10:23:23 INFO Date: 2025-01-27 ======================================== Time: 10:23:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 10:23:23 INFO not logined 2025-01-27 10:23:23 INFO not logined 2025-01-27 10:23:23 INFO Rendering unauthenticated menu. 2025-01-27 10:23:23 INFO Rendering unauthenticated menu. 2025-01-27 10:23:42 INFO Login button clicked. 2025-01-27 10:23:45 INFO Login successful for user: maheshsr 2025-01-27 10:23:45 INFO Login successful for user: maheshsr 2025-01-27 10:23:45 INFO Database names fetched successfully. 2025-01-27 10:23:45 INFO Database names fetched successfully. 2025-01-27 10:23:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:23:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:23:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:23:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:23:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:23:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:23:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:23:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:23:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:23:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:23:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:23:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:23:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:23:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:24:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:24:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:24:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:24:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:24:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:12 INFO Connected to the database Insightlab. 2025-01-27 10:24:12 INFO Connected to the database Insightlab. 2025-01-27 10:24:12 INFO Query executed successfully. 2025-01-27 10:24:12 INFO Query executed successfully. 2025-01-27 10:24:12 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:24:12 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:24:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:24:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:24:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:24:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:24:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:24:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:24:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:24:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:24:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:24:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:24:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:24:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:24:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:24:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:24:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:24:32 INFO Connected to the database Insightlab. 2025-01-27 10:24:32 INFO Query executed successfully. 2025-01-27 10:24:32 INFO Query executed successfully. 2025-01-27 10:24:32 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:24:32 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:24:32 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``get all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:24:32 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``get all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:24:35 INFO Tokens consumed: 945 2025-01-27 10:24:36 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:24:36 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:24:38 INFO token updated successfully: 2025-01 2025-01-27 10:24:38 INFO token updated successfully: 2025-01 2025-01-27 10:24:38 INFO token updated successfully. 2025-01-27 10:24:38 INFO token updated successfully. 2025-01-27 10:24:40 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 182 2025-01-27 10:24:42 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:24:42 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:24:43 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/183.py 2025-01-27 10:24:43 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/183.py 2025-01-27 10:24:43 INFO Code generated and written in generated_method//182.py 2025-01-27 10:24:43 INFO Code generated and written in generated_method//182.py 2025-01-27 10:24:43 WARNING result_df is not defined in the output dictionary 2025-01-27 10:25:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:25:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:25:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:25:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:25:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:13 INFO Connected to the database Insightlab. 2025-01-27 10:25:13 INFO Query executed successfully. 2025-01-27 10:25:13 INFO Query executed successfully. 2025-01-27 10:25:13 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:25:13 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:25:13 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``get all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:25:13 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``get all the appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:25:17 INFO Tokens consumed: 954 2025-01-27 10:25:17 INFO Tokens consumed: 954 2025-01-27 10:25:18 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:25:18 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:25:19 INFO token updated successfully: 2025-01 2025-01-27 10:25:19 INFO token updated successfully: 2025-01 2025-01-27 10:25:19 INFO token updated successfully. 2025-01-27 10:25:19 INFO token updated successfully. 2025-01-27 10:25:22 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 183 2025-01-27 10:25:24 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:25:24 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:25:25 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/184.py 2025-01-27 10:25:25 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/184.py 2025-01-27 10:25:25 INFO Code generated and written in generated_method//183.py 2025-01-27 10:25:25 INFO Code generated and written in generated_method//183.py 2025-01-27 10:25:25 INFO Insight generated and displayed using AG Grid. 2025-01-27 10:25:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:25:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:25:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:25:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:25:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:25:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:25:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:25:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:25:49 INFO Connected to the database MHealth_Dev. 2025-01-27 10:25:49 INFO Query executed successfully. 2025-01-27 10:25:49 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:25:49 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:28:30 INFO Date: 2025-01-27 ======================================== Time: 10:28:30 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 10:28:34 INFO not logined 2025-01-27 10:28:34 INFO Rendering unauthenticated menu. 2025-01-27 10:28:52 INFO Login button clicked. 2025-01-27 10:28:55 INFO Login successful for user: maheshsr 2025-01-27 10:29:03 INFO Database names fetched successfully. 2025-01-27 10:29:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:29:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:29:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:29:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:29:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:29:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:29:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:29:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:29:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:29:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:29:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:29:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:29:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:29:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:29:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:29:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:30:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:30:00 INFO Connected to the database Insightlab. 2025-01-27 10:30:00 INFO Query executed successfully. 2025-01-27 10:30:00 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:30:00 ERROR Error in displaying graph: %s 2025-01-27 10:31:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:31:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:31:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:31:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:31:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:31:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:31:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:31:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:31:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:31:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:31:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:31:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:31:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:31:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:31:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:31:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:31:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:31:14 INFO Connected to the database Insightlab. 2025-01-27 10:31:14 INFO Query executed successfully. 2025-01-27 10:31:14 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:31:14 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 10:31:17 INFO Tokens consumed: 935 2025-01-27 10:31:19 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:31:20 INFO token updated successfully: 2025-01 2025-01-27 10:31:20 INFO token updated successfully. 2025-01-27 10:31:22 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 184 2025-01-27 10:31:24 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 10:31:25 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/185.py 2025-01-27 10:31:25 INFO Code generated and written in generated_method//184.py 2025-01-27 10:31:26 INFO Insight generated and displayed using AG Grid. 2025-01-27 10:31:26 ERROR Error in displaying graph: %s 2025-01-27 10:32:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:32:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:32:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:32:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:32:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:32:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:32:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:32:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:32:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:32:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:32:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:32:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:32:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:32:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:32:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:32:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:32:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:32:36 INFO Connected to the database Insightlab. 2025-01-27 10:32:36 INFO Query executed successfully. 2025-01-27 10:32:36 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:32:36 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 10:32:42 INFO Tokens consumed: 1641 2025-01-27 10:32:43 INFO Existing token_consumed found for month: 2025-01 2025-01-27 10:32:44 INFO token updated successfully: 2025-01 2025-01-27 10:32:44 INFO token updated successfully. 2025-01-27 10:32:48 INFO Plotly chart object created successfully. 2025-01-27 10:32:48 INFO Graph generated and displayed using Plotly. 2025-01-27 10:32:48 ERROR Error in displaying graph: %s 2025-01-27 10:34:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:34:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 10:34:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 10:34:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 10:34:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 10:34:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 10:34:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 10:34:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 10:34:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 10:34:10 INFO Connected to the database MHealth_Dev. 2025-01-27 10:34:10 INFO Query executed successfully. 2025-01-27 10:34:10 INFO Dataset columns displayed using AG Grid. 2025-01-27 10:34:10 ERROR Error in displaying graph: %s 2025-01-27 11:21:03 INFO Date: 2025-01-27 ======================================== Time: 11:21:03 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 11:21:07 INFO not logined 2025-01-27 11:21:07 INFO Rendering unauthenticated menu. 2025-01-27 11:22:03 INFO Login button clicked. 2025-01-27 11:22:06 INFO Login successful for user: maheshsr 2025-01-27 11:22:16 INFO Database names fetched successfully. 2025-01-27 11:28:34 INFO Date: 2025-01-27 ======================================== Time: 11:28:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 11:28:38 INFO not logined 2025-01-27 11:28:38 INFO Rendering unauthenticated menu. 2025-01-27 11:29:36 INFO Login button clicked. 2025-01-27 11:29:41 INFO Login successful for user: maheshsr 2025-01-27 11:29:50 INFO Database names fetched successfully. 2025-01-27 11:30:35 INFO Database names fetched successfully. 2025-01-27 11:30:35 INFO Table details fetched successfully. 2025-01-27 11:30:35 ERROR Error fetching metadata for table : '' 2025-01-27 11:30:35 ERROR Error while loading the metadata: '' 2025-01-27 11:31:15 INFO Database names fetched successfully. 2025-01-27 11:31:15 INFO Metadata fetched for table: NewAppointment 2025-01-27 11:31:46 INFO Database names fetched successfully. 2025-01-27 11:31:46 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointmetns```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 11:31:48 INFO Tokens consumed: 2972 2025-01-27 11:31:49 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:31:50 INFO token updated successfully: 2025-01 2025-01-27 11:31:50 INFO token updated successfully. 2025-01-27 11:31:50 INFO Connected to the database MHealth_Dev. 2025-01-27 11:31:50 INFO Query executed successfully. 2025-01-27 11:31:52 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 139 2025-01-27 11:31:54 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:31:55 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:32:16 INFO Database names fetched successfully. 2025-01-27 11:32:27 INFO Database names fetched successfully. 2025-01-27 11:32:27 INFO Metadata fetched for table: Registration 2025-01-27 11:32:44 INFO Database names fetched successfully. 2025-01-27 11:32:45 INFO Blob exists check for query_library/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:32:46 INFO SQL query blob saved successfully: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:32:46 INFO Query saved in the library with id 140. 2025-01-27 11:33:02 INFO Database names fetched successfully. 2025-01-27 11:33:02 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 11:33:04 INFO Tokens consumed: 2969 2025-01-27 11:33:05 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:33:06 INFO token updated successfully: 2025-01 2025-01-27 11:33:06 INFO token updated successfully. 2025-01-27 11:33:06 INFO Connected to the database MHealth_Dev. 2025-01-27 11:33:06 INFO Query executed successfully. 2025-01-27 11:33:08 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 140 2025-01-27 11:33:10 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:33:11 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/141.json 2025-01-27 11:33:31 INFO Database names fetched successfully. 2025-01-27 11:33:31 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 11:33:33 INFO Tokens consumed: 2971 2025-01-27 11:33:34 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:33:35 INFO token updated successfully: 2025-01 2025-01-27 11:33:35 INFO token updated successfully. 2025-01-27 11:33:35 INFO Connected to the database MHealth_Dev. 2025-01-27 11:33:35 INFO Query executed successfully. 2025-01-27 11:33:37 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 141 2025-01-27 11:33:39 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:33:40 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/142.json 2025-01-27 11:35:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:35:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:35:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:35:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:35:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:35:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:35:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:35:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:36:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:36:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:36:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:36:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:36:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:36:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:36:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:36:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:36:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:36:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:36:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:36:50 INFO Connected to the database MHealth_Dev. 2025-01-27 11:36:50 INFO Query executed successfully. 2025-01-27 11:36:50 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:37:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:37:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:37:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:37:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:37:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:37:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:37:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:37:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:37:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:37:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:37:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:37:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:37:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:37:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:37:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:37:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:37:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:37:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:37:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:37:28 INFO Connected to the database MHealth_Dev. 2025-01-27 11:37:28 INFO Query executed successfully. 2025-01-27 11:37:28 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:37:28 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:37:30 INFO Tokens consumed: 884 2025-01-27 11:37:32 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:37:33 INFO token updated successfully: 2025-01 2025-01-27 11:37:33 INFO token updated successfully. 2025-01-27 11:37:36 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 185 2025-01-27 11:37:38 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:37:39 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/186.py 2025-01-27 11:37:39 INFO Code generated and written in generated_method//185.py 2025-01-27 11:37:39 WARNING result_df is not defined in the output dictionary 2025-01-27 11:37:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:37:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:37:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:37:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:37:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:37:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:37:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:37:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:37:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:37:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:37:53 INFO Connected to the database MHealth_Dev. 2025-01-27 11:37:53 INFO Query executed successfully. 2025-01-27 11:37:53 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:37:53 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:37:56 INFO Tokens consumed: 905 2025-01-27 11:37:58 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:37:59 INFO token updated successfully: 2025-01 2025-01-27 11:37:59 INFO token updated successfully. 2025-01-27 11:38:01 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 186 2025-01-27 11:38:03 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:38:04 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/187.py 2025-01-27 11:38:04 INFO Code generated and written in generated_method//186.py 2025-01-27 11:38:04 WARNING result_df is not defined in the output dictionary 2025-01-27 11:38:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:38:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:38:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:38:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:38:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:38:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:38:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:38:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:38:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:38:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:38:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:38:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:38:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:38:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:38:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:38:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:38:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:38:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:38:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:38:18 INFO Connected to the database MHealth_Dev. 2025-01-27 11:38:18 INFO Query executed successfully. 2025-01-27 11:38:18 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:38:18 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:38:21 INFO Tokens consumed: 884 2025-01-27 11:38:22 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:38:23 INFO token updated successfully: 2025-01 2025-01-27 11:38:23 INFO token updated successfully. 2025-01-27 11:38:26 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 187 2025-01-27 11:38:28 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:38:29 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/188.py 2025-01-27 11:38:29 INFO Code generated and written in generated_method//187.py 2025-01-27 11:38:29 WARNING result_df is not defined in the output dictionary 2025-01-27 11:40:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:40:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:41:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:41:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:41:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:41:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:41:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:41:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:41:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:41:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:41:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:41:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:41:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:41:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:41:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:41:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:41:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:41:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:07 INFO Connected to the database MHealth_Dev. 2025-01-27 11:41:07 INFO Query executed successfully. 2025-01-27 11:41:07 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:41:08 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:41:10 INFO Tokens consumed: 884 2025-01-27 11:41:12 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:41:13 INFO token updated successfully: 2025-01 2025-01-27 11:41:13 INFO token updated successfully. 2025-01-27 11:41:15 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 188 2025-01-27 11:41:17 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:41:18 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/189.py 2025-01-27 11:41:18 INFO Code generated and written in generated_method//188.py 2025-01-27 11:41:18 WARNING result_df is not defined in the output dictionary 2025-01-27 11:41:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:41:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:41:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:41:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:41:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:41:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:41:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:41:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:41:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:41:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:41:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:41:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:41:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:41:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:41:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:41:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:41:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:33 INFO Connected to the database MHealth_Dev. 2025-01-27 11:41:33 INFO Query executed successfully. 2025-01-27 11:41:33 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:41:33 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all the appointments `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:41:36 INFO Tokens consumed: 901 2025-01-27 11:41:37 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:41:38 INFO token updated successfully: 2025-01 2025-01-27 11:41:38 INFO token updated successfully. 2025-01-27 11:41:41 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 189 2025-01-27 11:41:43 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:41:44 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/190.py 2025-01-27 11:41:44 INFO Code generated and written in generated_method//189.py 2025-01-27 11:41:44 INFO Insight generated and displayed using AG Grid. 2025-01-27 11:41:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:41:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:41:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:41:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:41:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:41:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:41:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:41:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:41:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:42:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:42:00 INFO Connected to the database MHealth_Dev. 2025-01-27 11:42:00 INFO Query executed successfully. 2025-01-27 11:42:00 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:42:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:42:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:42:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:42:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:42:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:42:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:42:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:42:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:42:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:42:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:42:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:42:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:42:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:42:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:42:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:42:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:42:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:42:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:42:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:42:35 INFO Connected to the database MHealth_Dev. 2025-01-27 11:42:35 INFO Query executed successfully. 2025-01-27 11:42:35 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:42:35 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['AppointmentId', 'ScheduleId', 'SlotId', 'EncounterId', 'ConditionId', 'ChiefComplaint', 'PatientId', 'PractitionerId', 'CreatedDate', 'LastUpdatedDate', 'IsNewAppointment', 'PreviousAppointmentId', 'PreviousEncounterId', 'PreviousConditionId', 'PreviousConsultationId'], 'dtypes': {'AppointmentId': 'object', 'ScheduleId': 'object', 'SlotId': 'object', 'EncounterId': 'object', 'ConditionId': 'object', 'ChiefComplaint': 'object', 'PatientId': 'object', 'PractitionerId': 'object', 'CreatedDate': 'datetime64[ns]', 'LastUpdatedDate': 'datetime64[ns]', 'IsNewAppointment': 'object', 'PreviousAppointmentId': 'object', 'PreviousEncounterId': 'object', 'PreviousConditionId': 'object', 'PreviousConsultationId': 'object'}, 'describe': {'CreatedDate': {'count': 782, 'mean': Timestamp('2024-03-31 08:10:28.655653632'), 'min': Timestamp('2024-02-02 12:34:02.290000'), '25%': Timestamp('2024-03-11 11:57:21.101750016'), '50%': Timestamp('2024-04-03 17:31:57.520000'), '75%': Timestamp('2024-04-22 10:59:24.021000192'), 'max': Timestamp('2024-05-14 15:13:11.857000')}, 'LastUpdatedDate': {'count': 782, 'mean': Timestamp('2024-03-31 08:10:28.655653632'), 'min': Timestamp('2024-02-02 12:34:02.290000'), '25%': Timestamp('2024-03-11 11:57:21.101750016'), '50%': Timestamp('2024-04-03 17:31:57.520000'), '75%': Timestamp('2024-04-22 10:59:24.021000192'), 'max': Timestamp('2024-05-14 15:13:11.857000')}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a graph of the average appointment``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 11:42:38 INFO Tokens consumed: 1625 2025-01-27 11:42:40 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:42:41 INFO token updated successfully: 2025-01 2025-01-27 11:42:41 INFO token updated successfully. 2025-01-27 11:42:45 INFO Plotly chart object created successfully. 2025-01-27 11:42:45 INFO Graph generated and displayed using Plotly. 2025-01-27 11:43:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:43:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:43:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:43:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:43:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:43:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:43:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:43:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:43:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:43:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:43:18 INFO Connected to the database Insightlab. 2025-01-27 11:43:18 INFO Query executed successfully. 2025-01-27 11:43:18 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:44:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:44:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:44:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:44:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:45:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:45:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:45:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:45:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:45:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:45:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:45:05 INFO Connected to the database MHealth_Dev. 2025-01-27 11:45:05 INFO Query executed successfully. 2025-01-27 11:45:05 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:46:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:46:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:46:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:46:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:46:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:46:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:46:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:46:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:46:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:46:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:46:38 INFO Connected to the database Insightlab. 2025-01-27 11:46:38 INFO Query executed successfully. 2025-01-27 11:46:38 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:50:58 INFO Date: 2025-01-27 ======================================== Time: 11:50:58 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 11:51:03 INFO not logined 2025-01-27 11:51:03 INFO Rendering unauthenticated menu. 2025-01-27 11:51:19 INFO Login button clicked. 2025-01-27 11:51:22 INFO Login successful for user: maheshsr 2025-01-27 11:51:31 INFO Database names fetched successfully. 2025-01-27 11:51:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:51:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:51:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:51:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:51:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:51:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:51:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:51:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:51:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:52:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:52:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:52:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:52:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:52:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:52:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:52:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:52:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:52:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:52:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:52:23 INFO Connected to the database MHealth_Dev. 2025-01-27 11:52:23 INFO Query executed successfully. 2025-01-27 11:52:23 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:52:23 ERROR Error in displaying graph: %s 2025-01-27 11:53:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:53:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:53:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:53:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:53:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:53:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:53:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:53:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:53:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:53:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:53:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:53:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:53:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:53:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:53:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:53:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:53:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:53:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:53:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:53:49 INFO Connected to the database MHealth_Dev. 2025-01-27 11:53:49 INFO Query executed successfully. 2025-01-27 11:53:49 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:53:49 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:53:53 INFO Tokens consumed: 910 2025-01-27 11:53:54 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:53:55 INFO token updated successfully: 2025-01 2025-01-27 11:53:55 INFO token updated successfully. 2025-01-27 11:53:58 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 190 2025-01-27 11:54:00 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:54:01 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/191.py 2025-01-27 11:54:01 INFO Code generated and written in generated_method//190.py 2025-01-27 11:54:01 INFO Insight generated and displayed using AG Grid. 2025-01-27 11:54:01 ERROR Error in displaying graph: %s 2025-01-27 11:54:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:54:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:54:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:54:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:54:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:54:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:54:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:54:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:54:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:54:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:54:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:54:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:54:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:54:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:54:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:54:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:54:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:54:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:54:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:54:40 INFO Connected to the database MHealth_Dev. 2025-01-27 11:54:40 INFO Query executed successfully. 2025-01-27 11:54:40 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:54:40 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:54:44 INFO Tokens consumed: 964 2025-01-27 11:54:45 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:54:46 INFO token updated successfully: 2025-01 2025-01-27 11:54:46 INFO token updated successfully. 2025-01-27 11:54:48 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 191 2025-01-27 11:54:50 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:54:51 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/192.py 2025-01-27 11:54:51 INFO Code generated and written in generated_method//191.py 2025-01-27 11:54:51 INFO Insight generated and displayed using AG Grid. 2025-01-27 11:54:51 ERROR Error in displaying graph: %s 2025-01-27 11:55:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:55:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:55:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:55:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:55:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:55:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:55:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:55:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:55:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:55:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:55:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:55:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:55:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:55:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:55:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:55:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:55:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:55:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:55:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:55:33 INFO Connected to the database Insightlab. 2025-01-27 11:55:33 INFO Query executed successfully. 2025-01-27 11:55:33 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:55:33 ERROR Error in displaying graph: %s 2025-01-27 11:55:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:55:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:55:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:55:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:55:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:55:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:55:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:55:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:55:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:55:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:55:55 INFO Connected to the database MHealth_Dev. 2025-01-27 11:55:55 INFO Query executed successfully. 2025-01-27 11:55:55 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:55:55 ERROR Error in displaying graph: %s 2025-01-27 11:55:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:55:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:56:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:56:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:56:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:56:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:56:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:56:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:56:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:56:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:56:08 INFO Connected to the database MHealth_Dev. 2025-01-27 11:56:08 INFO Query executed successfully. 2025-01-27 11:56:08 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:56:08 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:56:11 INFO Tokens consumed: 858 2025-01-27 11:56:12 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:56:13 INFO token updated successfully: 2025-01 2025-01-27 11:56:13 INFO token updated successfully. 2025-01-27 11:56:16 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 192 2025-01-27 11:56:18 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:56:19 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/193.py 2025-01-27 11:56:19 INFO Code generated and written in generated_method//192.py 2025-01-27 11:56:19 WARNING result_df is not defined in the output dictionary 2025-01-27 11:56:19 ERROR Error in displaying graph: %s 2025-01-27 11:57:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 11:57:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:57:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 11:58:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 11:58:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 11:58:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 11:58:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 11:58:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 11:58:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 11:58:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 11:58:05 INFO Connected to the database MHealth_Dev. 2025-01-27 11:58:05 INFO Query executed successfully. 2025-01-27 11:58:05 INFO Dataset columns displayed using AG Grid. 2025-01-27 11:58:05 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 11:58:08 INFO Tokens consumed: 858 2025-01-27 11:58:10 INFO Existing token_consumed found for month: 2025-01 2025-01-27 11:58:11 INFO token updated successfully: 2025-01 2025-01-27 11:58:11 INFO token updated successfully. 2025-01-27 11:58:13 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 193 2025-01-27 11:58:15 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 11:58:16 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/194.py 2025-01-27 11:58:16 INFO Code generated and written in generated_method//193.py 2025-01-27 11:58:16 WARNING result_df is not defined in the output dictionary 2025-01-27 11:58:16 ERROR Error in displaying graph: %s 2025-01-27 12:00:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:00:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:00:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:00:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:00:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:00:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:00:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:00:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:00:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:00:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:00:38 INFO Connected to the database Insightlab. 2025-01-27 12:00:38 INFO Query executed successfully. 2025-01-27 12:00:38 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:00:38 ERROR Error in displaying graph: %s 2025-01-27 12:00:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:00:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:00:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:00:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:00:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:00:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:00:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:00:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:00:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:01:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:01:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:01:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:01:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:01:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:01:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:01:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:01:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:01:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:01:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:01:05 INFO Connected to the database MHealth_Dev. 2025-01-27 12:01:05 INFO Query executed successfully. 2025-01-27 12:01:05 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:01:05 ERROR Error in displaying graph: %s 2025-01-27 12:01:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:01:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:01:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:01:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:01:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:01:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:01:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:01:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:01:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:01:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:01:16 INFO Connected to the database MHealth_Dev. 2025-01-27 12:01:16 INFO Query executed successfully. 2025-01-27 12:01:16 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:01:16 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments of a month `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 12:01:19 INFO Tokens consumed: 961 2025-01-27 12:01:21 INFO Existing token_consumed found for month: 2025-01 2025-01-27 12:01:22 INFO token updated successfully: 2025-01 2025-01-27 12:01:22 INFO token updated successfully. 2025-01-27 12:01:25 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 194 2025-01-27 12:01:27 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 12:01:28 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/195.py 2025-01-27 12:01:28 INFO Code generated and written in generated_method//194.py 2025-01-27 12:01:28 INFO Insight generated and displayed using AG Grid. 2025-01-27 12:01:28 ERROR Error in displaying graph: %s 2025-01-27 12:01:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:01:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:01:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:01:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:01:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:01:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:01:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:01:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:01:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:01:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:01:41 INFO Connected to the database MHealth_Dev. 2025-01-27 12:01:41 INFO Query executed successfully. 2025-01-27 12:01:41 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:01:41 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of all appointments of a month `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 AppointmentId string 1 ScheduleId string 2 SlotId string 3 EncounterId string 4 ConditionId string 5 ChiefComplaint string 6 PatientId string 7 PractitionerId string 8 CreatedDate datetime64[ns] 9 LastUpdatedDate datetime64[ns] 10 IsNewAppointment string 11 PreviousAppointmentId string 12 PreviousEncounterId string 13 PreviousConditionId string 14 PreviousConsultationId string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 12:01:45 INFO Tokens consumed: 971 2025-01-27 12:01:46 INFO Existing token_consumed found for month: 2025-01 2025-01-27 12:01:47 INFO token updated successfully: 2025-01 2025-01-27 12:01:47 INFO token updated successfully. 2025-01-27 12:01:50 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 195 2025-01-27 12:01:52 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 12:01:53 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/196.py 2025-01-27 12:01:53 INFO Code generated and written in generated_method//195.py 2025-01-27 12:01:53 INFO Insight generated and displayed using AG Grid. 2025-01-27 12:01:53 ERROR Error in displaying graph: %s 2025-01-27 12:04:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:04:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:04:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:04:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:04:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:04:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:04:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:04:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:04:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:04:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:04:55 INFO Connected to the database Insightlab. 2025-01-27 12:04:55 INFO Query executed successfully. 2025-01-27 12:04:55 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:04:55 ERROR Error in displaying graph: %s 2025-01-27 12:14:18 INFO Date: 2025-01-27 ======================================== Time: 12:14:18 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:14:22 INFO not logined 2025-01-27 12:14:22 INFO Rendering unauthenticated menu. 2025-01-27 12:14:44 INFO Login button clicked. 2025-01-27 12:14:48 INFO Login successful for user: maheshsr 2025-01-27 12:14:57 INFO Database names fetched successfully. 2025-01-27 12:15:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:15:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:15:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:15:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:15:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:15:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:15:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:15:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:15:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:15:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:15:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:15:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:15:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:15:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:15:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:15:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:15:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:15:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:16:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:16:01 INFO Connected to the database Insightlab. 2025-01-27 12:16:01 INFO Query executed successfully. 2025-01-27 12:16:01 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:17:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:17:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:17:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:17:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:17:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:17:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:18:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:18:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:18:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:03 INFO Connected to the database Insightlab. 2025-01-27 12:18:03 INFO Query executed successfully. 2025-01-27 12:18:03 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:18:07 INFO Date: 2025-01-27 ======================================== Time: 12:18:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:18:07 INFO Date: 2025-01-27 ======================================== Time: 12:18:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:18:07 INFO not logined 2025-01-27 12:18:07 INFO Rendering unauthenticated menu. 2025-01-27 12:18:07 INFO Rendering unauthenticated menu. 2025-01-27 12:18:25 INFO Login button clicked. 2025-01-27 12:18:25 INFO Login button clicked. 2025-01-27 12:18:26 INFO Login button clicked. 2025-01-27 12:18:26 INFO Login button clicked. 2025-01-27 12:18:30 INFO Login successful for user: maheshsr 2025-01-27 12:18:30 INFO Login successful for user: maheshsr 2025-01-27 12:18:30 INFO Database names fetched successfully. 2025-01-27 12:18:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:18:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:18:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:18:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:18:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:18:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:18:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:18:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:18:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:18:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:18:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:18:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:18:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:18:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:18:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:18:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:18:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:18:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:18:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:18:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:18:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:18:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:18:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:18:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:18:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:18:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:18:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:18:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:18:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:18:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:18:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:18:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:18:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:18:56 INFO Connected to the database Insightlab. 2025-01-27 12:18:56 INFO Connected to the database Insightlab. 2025-01-27 12:18:56 INFO Query executed successfully. 2025-01-27 12:18:56 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:18:56 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:23:16 INFO Date: 2025-01-27 ======================================== Time: 12:23:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:23:21 INFO not logined 2025-01-27 12:23:21 INFO Rendering unauthenticated menu. 2025-01-27 12:24:05 INFO Login button clicked. 2025-01-27 12:24:08 INFO Login successful for user: maheshsr 2025-01-27 12:24:16 INFO Database names fetched successfully. 2025-01-27 12:25:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:25:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:25:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:25:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:25:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:25:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:25:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:25:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:25:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:26:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:26:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:26:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:26:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:26:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:26:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:26:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:26:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:26:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:26:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:26:13 INFO Connected to the database Insightlab. 2025-01-27 12:26:13 INFO Query executed successfully. 2025-01-27 12:26:13 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:35:22 INFO Date: 2025-01-27 ======================================== Time: 12:35:22 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:35:26 INFO not logined 2025-01-27 12:35:26 INFO Rendering unauthenticated menu. 2025-01-27 12:36:05 INFO Login button clicked. 2025-01-27 12:36:09 INFO Login successful for user: maheshsr 2025-01-27 12:36:19 INFO Database names fetched successfully. 2025-01-27 12:36:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:36:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:36:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:36:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:36:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:36:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:36:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:36:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:36:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:37:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:37:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:37:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:37:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:37:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:37:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:37:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:37:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:37:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:37:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:37:17 INFO Connected to the database Insightlab. 2025-01-27 12:37:17 INFO Query executed successfully. 2025-01-27 12:37:17 INFO Dataset columns displayed using AG Grid. 2025-01-27 12:44:54 INFO Date: 2025-01-27 ======================================== Time: 12:44:54 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 12:44:58 INFO not logined 2025-01-27 12:44:58 INFO Rendering unauthenticated menu. 2025-01-27 12:45:16 INFO Login button clicked. 2025-01-27 12:45:20 INFO Login successful for user: maheshsr 2025-01-27 12:45:29 INFO Database names fetched successfully. 2025-01-27 12:45:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:45:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:45:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:45:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:45:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:45:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:45:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:45:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:45:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:46:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 12:46:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 12:46:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 12:46:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 12:46:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 12:46:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 12:46:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 12:46:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:46:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 12:46:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 12:46:22 INFO Connected to the database Insightlab. 2025-01-27 12:46:22 INFO Query executed successfully. 2025-01-27 12:46:22 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:05:28 INFO Date: 2025-01-27 ======================================== Time: 13:05:28 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 13:05:32 INFO not logined 2025-01-27 13:05:32 INFO Rendering unauthenticated menu. 2025-01-27 13:05:49 INFO Login button clicked. 2025-01-27 13:05:53 INFO Login successful for user: maheshsr 2025-01-27 13:06:03 INFO Database names fetched successfully. 2025-01-27 13:06:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:06:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:06:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:06:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:06:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:06:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:06:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:06:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:06:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:06:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:06:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:06:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:06:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:07:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:07:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:07:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:07:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:07:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:07:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:07:05 INFO Connected to the database Insightlab. 2025-01-27 13:07:05 INFO Query executed successfully. 2025-01-27 13:07:05 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:25:28 INFO Date: 2025-01-27 ======================================== Time: 13:25:28 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 13:25:32 INFO not logined 2025-01-27 13:25:32 INFO Rendering unauthenticated menu. 2025-01-27 13:26:34 INFO Login button clicked. 2025-01-27 13:26:37 INFO Login successful for user: maheshsr 2025-01-27 13:26:47 INFO Database names fetched successfully. 2025-01-27 13:27:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:27:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:27:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:27:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:27:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:27:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:27:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:27:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:27:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:27:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:27:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:27:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:27:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:27:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:27:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:27:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:27:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:27:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:27:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:27:43 INFO Connected to the database Insightlab. 2025-01-27 13:27:43 INFO Query executed successfully. 2025-01-27 13:27:43 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:29:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:29:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:29:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:29:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:29:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:29:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:29:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:29:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:29:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:29:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:29:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:29:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:29:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:29:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:29:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:29:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:29:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:29:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:29:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:29:59 INFO Connected to the database Insightlab. 2025-01-27 13:29:59 INFO Query executed successfully. 2025-01-27 13:29:59 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:30:00 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient of age above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 13:30:02 INFO Tokens consumed: 937 2025-01-27 13:30:04 INFO Existing token_consumed found for month: 2025-01 2025-01-27 13:30:05 INFO token updated successfully: 2025-01 2025-01-27 13:30:05 INFO token updated successfully. 2025-01-27 13:30:07 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 196 2025-01-27 13:30:09 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 13:30:10 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/197.py 2025-01-27 13:30:10 INFO Code generated and written in generated_method//196.py 2025-01-27 13:30:10 INFO Insight generated and displayed using AG Grid. 2025-01-27 13:32:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:32:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:32:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:32:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:32:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:32:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:32:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:32:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:32:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:32:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:33:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:33:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:33:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:33:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:33:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:33:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:33:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:33:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:33:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:33:04 INFO Connected to the database Insightlab. 2025-01-27 13:33:04 INFO Query executed successfully. 2025-01-27 13:33:04 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:33:04 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a graph of patient with the average age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 13:33:08 INFO Tokens consumed: 1579 2025-01-27 13:33:09 INFO Existing token_consumed found for month: 2025-01 2025-01-27 13:33:10 INFO token updated successfully: 2025-01 2025-01-27 13:33:10 INFO token updated successfully. 2025-01-27 13:33:14 INFO Plotly chart object created successfully. 2025-01-27 13:33:14 INFO Graph generated and displayed using Plotly. 2025-01-27 13:33:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:33:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:33:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:33:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:33:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:33:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:33:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:33:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:33:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:33:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:33:49 INFO Connected to the database Insightlab. 2025-01-27 13:33:49 INFO Query executed successfully. 2025-01-27 13:33:49 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:33:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:33:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:33:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:33:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:33:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:34:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:34:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:34:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:34:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:34:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:34:04 INFO Connected to the database MHealth_Dev. 2025-01-27 13:34:04 INFO Query executed successfully. 2025-01-27 13:34:04 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:37:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:37:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 13:37:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:37:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:37:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:37:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 13:37:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:37:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 13:37:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:37:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 13:37:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:37:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 13:37:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:37:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:37:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:37:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 13:37:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:37:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:37:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 13:37:13 INFO Connected to the database MHealth_Dev. 2025-01-27 13:37:13 INFO Query executed successfully. 2025-01-27 13:37:13 INFO Dataset columns displayed using AG Grid. 2025-01-27 13:37:15 INFO Existing insight found for base code: SELECT * FROM NewAppointment; 2025-01-27 13:37:16 INFO Insight updated successfully: 5 2025-01-27 13:37:16 INFO Insight updated successfully. 2025-01-27 13:37:37 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 13:37:38 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 13:37:39 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:37:40 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:37:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 13:37:41 INFO Insight list generated successfully. 2025-01-27 13:38:38 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 13:38:39 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 13:38:40 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:38:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:38:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 13:38:42 INFO Insight list generated successfully. 2025-01-27 13:38:43 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:38:43 INFO Connected to the database Insightlab. 2025-01-27 13:38:43 INFO Query executed successfully. 2025-01-27 13:38:43 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-27 13:39:14 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 13:39:15 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 13:39:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 13:39:17 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 13:39:18 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 13:39:18 INFO Insight list generated successfully. 2025-01-27 13:39:19 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 13:39:19 INFO Connected to the database MHealth_Dev. 2025-01-27 13:39:19 INFO Query executed successfully. 2025-01-27 13:39:19 ERROR Error executing generated insight code: KeyError('Age') 2025-01-27 14:00:10 INFO Date: 2025-01-27 ======================================== Time: 14:00:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 14:00:14 INFO not logined 2025-01-27 14:00:14 INFO Rendering unauthenticated menu. 2025-01-27 14:00:31 INFO Login button clicked. 2025-01-27 14:00:34 INFO Login successful for user: maheshsr 2025-01-27 14:00:44 INFO Database names fetched successfully. 2025-01-27 14:01:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:01:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:01:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:01:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:01:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:01:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:01:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:01:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:01:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:01:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:01:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:01:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:01:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:01:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:01:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:01:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:01:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:01:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:01:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:01:50 INFO Connected to the database Insightlab. 2025-01-27 14:01:50 INFO Query executed successfully. 2025-01-27 14:01:50 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:02:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:02:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:02:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:02:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:02:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:02:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:02:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:02:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:02:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:02:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:02:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:02:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:02:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:02:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:02:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:02:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:02:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:02:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:02:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:02:52 INFO Connected to the database Insightlab. 2025-01-27 14:02:52 INFO Query executed successfully. 2025-01-27 14:02:52 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:03:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:03:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:03:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:03:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:03:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:03:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:03:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:03:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:03:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:03:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:03:08 INFO Connected to the database Insightlab. 2025-01-27 14:03:08 INFO Query executed successfully. 2025-01-27 14:03:08 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:09:50 INFO Date: 2025-01-27 ======================================== Time: 14:09:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 14:09:54 INFO not logined 2025-01-27 14:09:54 INFO Rendering unauthenticated menu. 2025-01-27 14:10:12 INFO Login button clicked. 2025-01-27 14:10:15 INFO Login successful for user: maheshsr 2025-01-27 14:10:25 INFO Database names fetched successfully. 2025-01-27 14:10:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:10:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:10:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:10:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:10:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:10:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:10:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:10:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:10:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:11:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:11:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:11:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:11:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:11:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:11:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:11:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:11:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:11:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:11:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:11:32 INFO Connected to the database Insightlab. 2025-01-27 14:11:32 INFO Query executed successfully. 2025-01-27 14:11:32 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:12:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:12:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:12:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:12:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:12:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:12:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:12:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:12:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:12:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:12:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:12:14 INFO Connected to the database Insightlab. 2025-01-27 14:12:14 INFO Query executed successfully. 2025-01-27 14:12:14 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:13:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:13:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:13:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:13:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:13:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:13:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:13:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:13:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:13:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:13:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:13:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:13:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:13:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:13:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:13:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:13:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:13:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:13:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:13:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:13:16 INFO Connected to the database Insightlab. 2025-01-27 14:13:16 INFO Query executed successfully. 2025-01-27 14:13:16 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:13:16 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 14:13:19 INFO Tokens consumed: 933 2025-01-27 14:13:21 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:13:22 INFO token updated successfully: 2025-01 2025-01-27 14:13:22 INFO token updated successfully. 2025-01-27 14:13:24 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 197 2025-01-27 14:13:26 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:13:27 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/198.py 2025-01-27 14:13:27 INFO Code generated and written in generated_method//197.py 2025-01-27 14:13:27 INFO Insight generated and displayed using AG Grid. 2025-01-27 14:13:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:13:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:13:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:13:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:13:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:13:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:13:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:13:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:13:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:14:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:14:00 INFO Connected to the database MHealth_Dev. 2025-01-27 14:14:00 INFO Query executed successfully. 2025-01-27 14:14:00 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:22:27 INFO Date: 2025-01-27 ======================================== Time: 14:22:27 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 14:22:31 INFO not logined 2025-01-27 14:22:31 INFO Rendering unauthenticated menu. 2025-01-27 14:22:43 INFO Login button clicked. 2025-01-27 14:22:47 INFO Login successful for user: maheshsr 2025-01-27 14:22:57 INFO Database names fetched successfully. 2025-01-27 14:23:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:23:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:23:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:23:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:23:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:23:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:23:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:23:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:23:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:23:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:23:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:23:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:23:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:23:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:23:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:23:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:23:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:23:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:23:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:23:53 INFO Connected to the database Insightlab. 2025-01-27 14:23:53 INFO Query executed successfully. 2025-01-27 14:23:53 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:24:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:24:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:24:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:24:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:24:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:24:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:24:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:24:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:24:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:24:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:24:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:24:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:24:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:24:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:24:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:24:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:24:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:24:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:25:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:25:00 INFO Connected to the database Insightlab. 2025-01-27 14:25:00 INFO Query executed successfully. 2025-01-27 14:25:00 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:25:00 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 14:25:04 INFO Tokens consumed: 931 2025-01-27 14:25:05 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:25:07 INFO token updated successfully: 2025-01 2025-01-27 14:25:07 INFO token updated successfully. 2025-01-27 14:25:09 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 198 2025-01-27 14:25:11 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:25:12 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/199.py 2025-01-27 14:25:12 INFO Code generated and written in generated_method//198.py 2025-01-27 14:25:12 INFO Insight generated and displayed using AG Grid. 2025-01-27 14:25:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:25:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:25:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:25:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:26:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:26:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:26:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:26:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:26:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:26:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:26:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:26:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:26:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:26:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:26:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:26:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:26:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:26:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:26:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:26:06 INFO Connected to the database Insightlab. 2025-01-27 14:26:06 INFO Query executed successfully. 2025-01-27 14:26:06 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:26:06 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a scattered graph of patient based on age``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 14:26:11 INFO Tokens consumed: 1558 2025-01-27 14:26:12 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:26:13 INFO token updated successfully: 2025-01 2025-01-27 14:26:13 INFO token updated successfully. 2025-01-27 14:26:17 INFO Plotly chart object created successfully. 2025-01-27 14:26:17 INFO Graph generated and displayed using Plotly. 2025-01-27 14:26:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:26:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:26:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:26:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:26:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:26:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:26:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:26:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:26:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:26:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:26:57 INFO Connected to the database MHealth_Dev. 2025-01-27 14:26:57 INFO Query executed successfully. 2025-01-27 14:26:57 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:34:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:34:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:34:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:34:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:34:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:34:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:34:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:34:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:34:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:34:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:34:41 INFO Connected to the database MHealth_Dev. 2025-01-27 14:34:41 INFO Query executed successfully. 2025-01-27 14:34:41 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:34:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:34:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:34:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:34:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:35:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:35:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:35:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:35:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:35:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:35:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:35:06 INFO Connected to the database Insightlab. 2025-01-27 14:35:06 INFO Query executed successfully. 2025-01-27 14:35:06 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:37:54 INFO Date: 2025-01-27 ======================================== Time: 14:37:54 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 14:37:58 INFO not logined 2025-01-27 14:37:58 INFO Rendering unauthenticated menu. 2025-01-27 14:38:18 INFO Login button clicked. 2025-01-27 14:38:22 INFO Login successful for user: maheshsr 2025-01-27 14:38:30 INFO Database names fetched successfully. 2025-01-27 14:38:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:38:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:38:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:38:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:38:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:38:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:38:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:38:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:38:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:40:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:40:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:40:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:40:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:40:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:40:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:40:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:40:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:40:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:40:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:41:00 INFO Connected to the database Insightlab. 2025-01-27 14:41:00 INFO Query executed successfully. 2025-01-27 14:41:00 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:42:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:42:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:42:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:42:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:42:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:42:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:42:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:42:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:42:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:42:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:42:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:42:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:42:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:42:16 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:42:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:42:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:42:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:42:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:42:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:42:19 INFO Connected to the database Insightlab. 2025-01-27 14:42:19 INFO Query executed successfully. 2025-01-27 14:42:19 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:42:19 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate an pie chart of patient based on the age group ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 14:42:26 INFO Tokens consumed: 1699 2025-01-27 14:42:27 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:42:28 INFO token updated successfully: 2025-01 2025-01-27 14:42:28 INFO token updated successfully. 2025-01-27 14:42:29 ERROR Error creating plotly chart object: All arrays must be of the same length 2025-01-27 14:42:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:42:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:42:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:42:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:42:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:42:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:42:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:42:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:42:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:42:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:42:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:42:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:42:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:42:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:42:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:43:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:43:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:01 INFO Connected to the database Insightlab. 2025-01-27 14:43:01 INFO Query executed successfully. 2025-01-27 14:43:02 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:43:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:43:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:43:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:43:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:43:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:43:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:43:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:43:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:43:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:43:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:43:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:43:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:43:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:43:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:43:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:43:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:43:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:43:47 INFO Connected to the database Insightlab. 2025-01-27 14:43:47 INFO Query executed successfully. 2025-01-27 14:43:47 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:43:47 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate bar graph of patient based on the age group ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 14:43:51 INFO Tokens consumed: 1645 2025-01-27 14:43:52 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:43:54 INFO token updated successfully: 2025-01 2025-01-27 14:43:54 INFO token updated successfully. 2025-01-27 14:43:57 INFO Plotly chart object created successfully. 2025-01-27 14:43:57 INFO Graph generated and displayed using Plotly. 2025-01-27 14:46:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:46:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:46:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:46:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:46:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:46:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:46:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:46:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:46:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:46:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:46:52 INFO Connected to the database MHealth_Dev. 2025-01-27 14:46:52 INFO Query executed successfully. 2025-01-27 14:46:52 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:49:21 INFO Date: 2025-01-27 ======================================== Time: 14:49:21 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-27 14:49:25 INFO not logined 2025-01-27 14:49:25 INFO Rendering unauthenticated menu. 2025-01-27 14:50:09 INFO Login button clicked. 2025-01-27 14:50:12 INFO Login successful for user: maheshsr 2025-01-27 14:50:20 INFO Database names fetched successfully. 2025-01-27 14:51:04 INFO Database names fetched successfully. 2025-01-27 14:51:04 INFO Table details fetched successfully. 2025-01-27 14:51:04 ERROR Error fetching metadata for table : '' 2025-01-27 14:51:04 ERROR Error while loading the metadata: '' 2025-01-27 14:52:54 INFO Database names fetched successfully. 2025-01-27 14:53:07 INFO Database names fetched successfully. 2025-01-27 14:53:07 INFO Metadata fetched for table: NewAppointment 2025-01-27 14:53:39 INFO Database names fetched successfully. 2025-01-27 14:53:39 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 14:53:41 INFO Tokens consumed: 2970 2025-01-27 14:53:43 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:53:44 INFO token updated successfully: 2025-01 2025-01-27 14:53:44 INFO token updated successfully. 2025-01-27 14:53:44 INFO Connected to the database MHealth_Dev. 2025-01-27 14:53:44 INFO Query executed successfully. 2025-01-27 14:53:46 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 142 2025-01-27 14:53:48 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:53:49 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:54:36 INFO Database names fetched successfully. 2025-01-27 14:54:37 INFO Blob exists check for query_library/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:54:38 INFO SQL query blob saved successfully: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:54:38 INFO Query saved in the library with id 143. 2025-01-27 14:54:55 INFO Database names fetched successfully. 2025-01-27 14:54:55 INFO Metadata fetched for table: Registration 2025-01-27 14:55:01 INFO Database names fetched successfully. 2025-01-27 14:55:33 INFO Database names fetched successfully. 2025-01-27 14:55:33 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get the registration```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-27 14:55:35 INFO Tokens consumed: 2968 2025-01-27 14:55:37 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:55:38 INFO token updated successfully: 2025-01 2025-01-27 14:55:38 INFO token updated successfully. 2025-01-27 14:55:38 INFO Connected to the database MHealth_Dev. 2025-01-27 14:55:38 INFO Query executed successfully. 2025-01-27 14:55:40 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 143 2025-01-27 14:55:42 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:55:43 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/144.json 2025-01-27 14:56:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:56:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:56:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:56:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:56:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:56:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:56:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:56:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:56:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:56:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:57:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:57:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:57:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:57:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:57:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:57:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:57:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:57:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:57:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:57:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:57:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:57:14 INFO Connected to the database Insightlab. 2025-01-27 14:57:14 INFO Query executed successfully. 2025-01-27 14:57:14 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:57:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:57:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:57:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:57:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:57:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:57:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:57:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:57:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:57:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:57:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:57:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:57:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:57:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:57:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:57:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:57:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:57:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:57:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:57:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:57:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:57:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:57:56 INFO Connected to the database Insightlab. 2025-01-27 14:57:56 INFO Query executed successfully. 2025-01-27 14:57:56 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:57:56 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-01-27 14:57:59 INFO Tokens consumed: 935 2025-01-27 14:58:00 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:58:02 INFO token updated successfully: 2025-01 2025-01-27 14:58:02 INFO token updated successfully. 2025-01-27 14:58:04 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 199 2025-01-27 14:58:07 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-27 14:58:08 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/200.py 2025-01-27 14:58:08 INFO Code generated and written in generated_method//199.py 2025-01-27 14:58:08 INFO Insight generated and displayed using AG Grid. 2025-01-27 14:58:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:58:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 14:58:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:58:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 14:58:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:58:48 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 14:58:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:58:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 14:58:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:58:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 14:58:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:58:50 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 14:58:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:58:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 14:58:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:58:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 14:58:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:58:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:58:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:58:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 14:58:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 14:58:55 INFO Connected to the database Insightlab. 2025-01-27 14:58:55 INFO Query executed successfully. 2025-01-27 14:58:55 INFO Dataset columns displayed using AG Grid. 2025-01-27 14:58:55 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date datetime 5 identifier_assigner string 6 active string 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date datetime 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start datetime`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'object', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'Age': {'count': 20.0, 'mean': 65.0, 'std': 6.164414002968976, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 20.0, 'mean': 12521.8, 'std': 1589.0576684576963, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a pie chart of patient with average age ``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-01-27 14:58:59 INFO Tokens consumed: 1559 2025-01-27 14:59:00 INFO Existing token_consumed found for month: 2025-01 2025-01-27 14:59:01 INFO token updated successfully: 2025-01 2025-01-27 14:59:01 INFO token updated successfully. 2025-01-27 14:59:04 INFO Plotly chart object created successfully. 2025-01-27 14:59:05 INFO Graph generated and displayed using Plotly. 2025-01-27 15:03:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/143.json 2025-01-27 15:03:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/140.json 2025-01-27 15:03:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 15:03:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/123.json 2025-01-27 15:03:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/115.json 2025-01-27 15:03:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/64.json 2025-01-27 15:03:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/19.json 2025-01-27 15:03:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 15:03:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-01-27 15:03:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:03:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/130.json 2025-01-27 15:03:36 INFO Connected to the database MHealth_Dev. 2025-01-27 15:03:36 INFO Query executed successfully. 2025-01-27 15:03:36 INFO Dataset columns displayed using AG Grid. 2025-01-27 15:03:47 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:03:48 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 15:03:49 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 15:03:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:03:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 15:03:51 INFO Insight list generated successfully. 2025-01-27 15:03:59 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:03:59 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 15:04:01 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 15:04:02 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:04:03 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 15:04:03 INFO Insight list generated successfully. 2025-01-27 15:04:04 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:04:04 INFO Connected to the database MHealth_Dev. 2025-01-27 15:04:04 INFO Query executed successfully. 2025-01-27 15:04:05 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-01-27 15:04:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:04:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 15:04:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 15:04:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:04:47 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 15:04:47 INFO Insight list generated successfully. 2025-01-27 15:04:48 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:04:48 INFO Connected to the database MHealth_Dev. 2025-01-27 15:04:48 INFO Query executed successfully. 2025-01-27 15:04:48 ERROR Error executing generated insight code: KeyError('Age') 2025-01-27 15:05:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:05:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-01-27 15:05:43 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-01-27 15:05:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-01-27 15:05:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-01-27 15:05:45 INFO Insight list generated successfully. 2025-01-27 15:05:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-01-27 15:05:46 INFO Connected to the database MHealth_Dev. 2025-01-27 15:05:46 INFO Query executed successfully. 2025-01-27 15:05:46 ERROR Error executing generated insight code: KeyError('Age') 2025-01-29 16:40:05 INFO Date: 2025-01-29 ======================================== Time: 16:40:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-29 16:40:10 INFO not logined 2025-01-29 16:40:10 INFO Rendering unauthenticated menu. 2025-01-29 16:40:41 INFO Login button clicked. 2025-01-29 16:40:45 INFO Login successful for user: maheshsr 2025-01-29 16:40:57 INFO Database names fetched successfully. 2025-01-29 16:41:22 INFO Database names fetched successfully. 2025-01-29 16:41:48 INFO Table details fetched successfully. 2025-01-29 16:42:18 INFO Database names fetched successfully. 2025-01-29 16:42:18 INFO Metadata fetched for table: NewAppointment 2025-01-29 16:42:26 INFO Database names fetched successfully. 2025-01-29 16:42:29 INFO Database names fetched successfully. 2025-01-29 16:42:29 INFO Metadata fetched for table: Registration 2025-01-29 16:42:46 INFO Database names fetched successfully. 2025-01-29 16:42:46 INFO Table details fetched successfully. 2025-01-29 16:43:00 INFO Database names fetched successfully. 2025-01-29 16:43:26 INFO Table details fetched successfully. 2025-01-29 16:43:29 INFO Database names fetched successfully. 2025-01-29 16:43:29 INFO Metadata fetched for table: NewAppointment 2025-01-29 16:43:37 INFO Database names fetched successfully. 2025-01-29 16:43:37 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Prescription_Details': 'MHealth_Dev - Prescription_Details - ', 'Registration': 'MHealth_Dev - Registration - ', 'NewAppointment': 'MHealth_Dev - NewAppointment - ', 'LabTests': 'MHealth_Dev - LabTests - ', 'TextChat': 'MHealth_Dev - TextChat - ', 'Medication': 'MHealth_Dev - Medication - ', 'Feedbacks': 'MHealth_Dev - Feedbacks - ', 'AssessRisk\u200b': 'MHealth_Dev - AssessRisk\u200b - ', 'Notification': 'MHealth_Dev - Notification - ', 'EventLogs': 'MHealth_Dev - EventLogs - ', 'Medications_Details': 'MHealth_Dev - Medications_Details - ', 'QuestionChoice': 'MHealth_Dev - QuestionChoice - ', 'Specialty': 'MHealth_Dev - Specialty - ', 'ConsultationSummary': 'MHealth_Dev - ConsultationSummary - ', 'ProcedureDetails': 'MHealth_Dev - ProcedureDetails - '}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'AssessRisk\u200b': [{'name': 'RiskID', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskScore', 'type': 'nvarchar', 'description': ''}, {'name': 'MeasureDate', 'type': 'datetime', 'description': ''}, {'name': 'RiskModel', 'type': 'nvarchar', 'description': ''}, {'name': 'RiskCategory', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessedBy', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}], 'ConsultationSummary': [{'name': 'ConsultationSummaryId', 'type': 'int', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'ComplaintHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicalHistory', 'type': 'nvarchar', 'description': ''}, {'name': 'PhysicalExaminationSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'AssessmentSummary', 'type': 'nvarchar', 'description': ''}, {'name': 'TreatmentPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Other', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'VisitDate', 'type': 'datetime', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'LinkedEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'PractitionerName', 'type': 'nvarchar', 'description': ''}], 'EventLogs': [{'name': 'EventId', 'type': 'int', 'description': ''}, {'name': 'CreatedOn', 'type': 'datetime', 'description': ''}, {'name': 'Level', 'type': 'nvarchar', 'description': ''}, {'name': 'Module', 'type': 'nvarchar', 'description': ''}, {'name': 'Feature', 'type': 'nvarchar', 'description': ''}, {'name': 'Event', 'type': 'nvarchar', 'description': ''}], 'Feedbacks': [{'name': 'FeedbackId', 'type': 'int', 'description': ''}, {'name': 'Comments', 'type': 'nvarchar', 'description': ''}, {'name': 'StarRating', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'LabTests': [{'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'LabTests', 'type': 'nvarchar', 'description': ''}, {'name': 'FollowUp', 'type': 'nvarchar', 'description': ''}], 'Medication': [{'name': 'MedicationId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}], 'Medications_Details': [{'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationSummaryId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationName', 'type': 'nvarchar', 'description': ''}, {'name': 'Dosage', 'type': 'nvarchar', 'description': ''}, {'name': 'Duration', 'type': 'nvarchar', 'description': ''}, {'name': 'Strength', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'FhirMedicationId', 'type': 'nvarchar', 'description': ''}], 'NewAppointment': [{'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ScheduleId', 'type': 'nvarchar', 'description': ''}, {'name': 'SlotId', 'type': 'nvarchar', 'description': ''}, {'name': 'EncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'ChiefComplaint', 'type': 'nvarchar', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsNewAppointment', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousAppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousEncounterId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConditionId', 'type': 'nvarchar', 'description': ''}, {'name': 'PreviousConsultationId', 'type': 'nvarchar', 'description': ''}], 'Notification': [{'name': 'NotificationId', 'type': 'int', 'description': ''}, {'name': 'Sender', 'type': 'nvarchar', 'description': ''}, {'name': 'Receiver', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'ConsultationId', 'type': 'nvarchar', 'description': ''}, {'name': 'PrescriptionId', 'type': 'nvarchar', 'description': ''}, {'name': 'IsRead', 'type': 'bit', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}, {'name': 'Title', 'type': 'nvarchar', 'description': ''}], 'Prescription_Details': [{'name': 'PrescriptionId', 'type': 'int', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'OrganizationId', 'type': 'nvarchar', 'description': ''}, {'name': 'AppointmentId', 'type': 'nvarchar', 'description': ''}, {'name': 'MedicationId', 'type': 'int', 'description': ''}, {'name': 'IsShared', 'type': 'bit', 'description': ''}, {'name': 'Labtests', 'type': 'nvarchar', 'description': ''}, {'name': 'Followup', 'type': 'nvarchar', 'description': ''}, {'name': 'CreatedDate', 'type': 'datetime', 'description': ''}], 'ProcedureDetails': [{'name': 'ProcedureCode', 'type': 'nvarchar', 'description': ''}, {'name': 'Procedure', 'type': 'nvarchar', 'description': ''}, {'name': 'isPreAuthRequired', 'type': 'nvarchar', 'description': ''}], 'QuestionChoice': [{'name': 'QuestionChoiceId', 'type': 'bigint', 'description': ''}, {'name': 'PatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'PractitionerId', 'type': 'nvarchar', 'description': ''}, {'name': 'QuestionId', 'type': 'bigint', 'description': ''}, {'name': 'Answer', 'type': 'nvarchar', 'description': ''}, {'name': 'IsActive', 'type': 'bit', 'description': ''}], 'Registration': [{'name': 'RegistrationId', 'type': 'int', 'description': ''}, {'name': 'FirstName', 'type': 'nvarchar', 'description': ''}, {'name': 'LastName', 'type': 'nvarchar', 'description': ''}, {'name': 'EmailId', 'type': 'nvarchar', 'description': ''}, {'name': 'Gender', 'type': 'nvarchar', 'description': ''}, {'name': 'DOB', 'type': 'date', 'description': ''}, {'name': 'Password', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPatientId', 'type': 'nvarchar', 'description': ''}, {'name': 'MemberPlan', 'type': 'nvarchar', 'description': ''}, {'name': 'Mobile', 'type': 'nvarchar', 'description': ''}, {'name': 'FHIRPractitionerId', 'type': 'varchar', 'description': ''}, {'name': 'IsPractitioner', 'type': 'bit', 'description': ''}, {'name': 'Rating', 'type': 'nvarchar', 'description': ''}, {'name': 'Speciality', 'type': 'nvarchar', 'description': ''}, {'name': 'Image', 'type': 'varchar', 'description': ''}], 'Specialty': [{'name': 'Code', 'type': 'nvarchar', 'description': ''}, {'name': 'Specialty', 'type': 'nvarchar', 'description': ''}], 'TextChat': [{'name': 'ChatId', 'type': 'int', 'description': ''}, {'name': 'MemberId', 'type': 'nvarchar', 'description': ''}, {'name': 'Message', 'type': 'nvarchar', 'description': ''}, {'name': 'LastUpdatedDate', 'type': 'datetime', 'description': ''}]}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the appointments```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-01-29 16:43:42 INFO Tokens consumed: 2970 2025-01-29 16:43:43 INFO Existing token_consumed found for month: 2025-01 2025-01-29 16:43:44 INFO token updated successfully: 2025-01 2025-01-29 16:43:44 INFO token updated successfully. 2025-01-29 16:43:44 INFO Connected to the database MHealth_Dev. 2025-01-29 16:43:44 INFO Query executed successfully. 2025-01-29 16:43:46 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 144 2025-01-29 16:43:48 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-01-29 16:43:49 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/145.json 2025-01-29 16:44:33 INFO Database names fetched successfully. 2025-01-29 16:45:04 INFO Database names fetched successfully. 2025-01-29 16:45:05 INFO Database names fetched successfully. 2025-01-29 16:45:11 INFO Database names fetched successfully. 2025-01-29 16:45:13 INFO Database names fetched successfully. 2025-01-29 19:40:03 INFO Date: 2025-01-29 ======================================== Time: 19:40:03 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-01-29 19:40:08 INFO not logined 2025-01-29 19:40:08 INFO Rendering unauthenticated menu. 2025-02-04 19:47:07 INFO Date: 2025-02-04 ======================================== Time: 19:47:07 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-04 19:47:11 INFO not logined 2025-02-04 19:47:11 INFO Rendering unauthenticated menu. 2025-02-04 19:48:24 INFO Login button clicked. 2025-02-04 19:48:27 INFO Login successful for user: abhishek 2025-02-04 19:48:35 INFO Database names fetched successfully. 2025-02-04 19:49:31 INFO Database names fetched successfully. 2025-02-04 19:49:31 INFO Table details fetched successfully. 2025-02-04 19:50:37 INFO Database names fetched successfully. 2025-02-04 19:50:37 INFO Metadata fetched for table: Patient 2025-02-17 11:37:49 INFO Date: 2025-02-17 ======================================== Time: 11:37:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-17 11:37:53 INFO not logined 2025-02-17 11:37:53 INFO Rendering unauthenticated menu. 2025-02-17 11:38:27 INFO Login button clicked. 2025-02-17 11:38:30 INFO Login successful for user: maheshsr 2025-02-17 11:38:55 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-17 12:45:28 INFO Date: 2025-02-17 ======================================== Time: 12:45:28 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-17 12:45:28 INFO not logined 2025-02-17 12:45:28 INFO not logined 2025-02-17 12:45:28 INFO Rendering unauthenticated menu. 2025-02-17 12:45:28 INFO Rendering unauthenticated menu. 2025-02-17 12:45:49 INFO Login button clicked. 2025-02-17 12:45:49 INFO Login button clicked. 2025-02-17 12:45:53 INFO Login successful for user: maheshsr 2025-02-17 12:45:53 INFO Login successful for user: maheshsr 2025-02-17 12:46:09 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-17 12:46:09 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]SQL Server Network Interfaces: Error Locating Server/Instance Specified [xFFFFFFFF]. (-1) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (-1)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-17 13:07:25 INFO Date: 2025-02-17 ======================================== Time: 13:07:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-17 13:07:30 INFO not logined 2025-02-17 13:07:30 INFO Rendering unauthenticated menu. 2025-02-17 13:07:48 INFO Login button clicked. 2025-02-17 13:07:52 INFO Login successful for user: maheshsr 2025-02-17 13:08:11 ERROR Error fetching database names: Not an executable object: "\n SELECT name \n FROM sys.databases\n WHERE name NOT IN ('master', 'tempdb', 'model', 'msdb');\n " 2025-02-18 20:32:22 INFO Date: 2025-02-18 ======================================== Time: 20:32:22 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-18 20:32:27 INFO not logined 2025-02-18 20:32:27 INFO Rendering unauthenticated menu. 2025-02-18 21:39:35 INFO Date: 2025-02-18 ======================================== Time: 21:39:35 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-18 21:39:35 INFO Date: 2025-02-18 ======================================== Time: 21:39:35 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-18 21:39:35 INFO Login button clicked. 2025-02-18 21:39:35 INFO Login button clicked. 2025-02-18 21:39:43 INFO Login successful for user: maheshsr 2025-02-18 21:40:41 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-18 21:40:41 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-19 15:18:10 INFO Date: 2025-02-19 ======================================== Time: 15:18:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-19 15:18:15 INFO not logined 2025-02-19 15:18:15 INFO Rendering unauthenticated menu. 2025-02-19 15:18:38 INFO Login button clicked. 2025-02-19 15:18:41 INFO Login successful for user: abhishek 2025-02-19 15:19:32 ERROR Error fetching database names: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)') (Background on this error at: https://sqlalche.me/e/20/e3q8) 2025-02-19 15:23:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-02-19 15:23:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-02-19 15:23:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-02-19 15:23:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-02-19 15:23:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-19 15:23:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-19 15:23:57 ERROR Error fetching database names: Not an executable object: "\n SELECT name \n FROM sys.databases\n WHERE name NOT IN ('master', 'tempdb', 'model', 'msdb');\n " 2025-02-20 14:04:55 INFO Date: 2025-02-20 ======================================== Time: 14:04:55 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-20 14:04:55 INFO not logined 2025-02-20 14:04:55 INFO not logined 2025-02-20 14:04:55 INFO Rendering unauthenticated menu. 2025-02-20 14:04:55 INFO Rendering unauthenticated menu. 2025-02-21 13:24:11 INFO Date: 2025-02-21 ======================================== Time: 13:24:11 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 13:24:16 INFO not logined 2025-02-21 13:24:16 INFO Rendering unauthenticated menu. 2025-02-21 13:24:36 INFO Login button clicked. 2025-02-21 13:24:41 INFO Login successful for user: abhishek 2025-02-21 13:39:00 INFO Date: 2025-02-21 ======================================== Time: 13:39:00 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 13:39:05 INFO not logined 2025-02-21 13:39:05 INFO Rendering unauthenticated menu. 2025-02-21 13:39:23 INFO Login button clicked. 2025-02-21 13:39:27 INFO Login successful for user: abhishek 2025-02-21 13:41:22 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-02-21 13:42:02 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-02-21 13:51:34 INFO Date: 2025-02-21 ======================================== Time: 13:51:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 13:51:40 INFO not logined 2025-02-21 13:51:40 INFO Rendering unauthenticated menu. 2025-02-21 13:52:08 INFO Login button clicked. 2025-02-21 13:52:12 INFO Login successful for user: abhishek 2025-02-21 13:54:09 ERROR Error processing request: 'st.session_state has no key "selected_db". Did you forget to initialize it? More info: https://docs.streamlit.io/develop/concepts/architecture/session-state#initialization' 2025-02-21 14:04:19 INFO Date: 2025-02-21 ======================================== Time: 14:04:19 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 14:04:24 INFO not logined 2025-02-21 14:04:24 INFO Rendering unauthenticated menu. 2025-02-21 14:06:02 INFO Login button clicked. 2025-02-21 14:06:06 INFO Login successful for user: abhishek 2025-02-21 14:07:11 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-21 14:07:17 INFO Tokens consumed: 1486 2025-02-21 14:07:20 INFO No existing token_consumed found for month: 2025-02 2025-02-21 14:07:21 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-02-21 14:07:22 INFO Blob exists check for token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 14:07:24 INFO New token created: token_consumed/3418c428-10c1-70a4-55f6-370d11e8b253/2025-02.json 2025-02-21 14:07:24 INFO Query executed successfully. 2025-02-21 14:07:26 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 61 2025-02-21 14:07:28 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 14:07:30 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/62.json 2025-02-21 14:15:32 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the risk score```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-21 14:15:39 INFO Tokens consumed: 1489 2025-02-21 14:15:42 INFO Existing token_consumed found for month: 2025-02 2025-02-21 14:15:43 INFO token updated successfully: 2025-02 2025-02-21 14:15:43 INFO token updated successfully. 2025-02-21 14:15:43 INFO Query executed successfully. 2025-02-21 14:15:45 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 62 2025-02-21 14:15:48 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 14:15:50 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/63.json 2025-02-21 14:16:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-02-21 14:16:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-02-21 14:16:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-02-21 14:16:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-02-21 14:16:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 14:16:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 14:16:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-02-21 14:16:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-02-21 14:16:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-02-21 14:16:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-02-21 14:16:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-02-21 14:16:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 14:16:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-02-21 14:16:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 14:16:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-02-21 14:16:48 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/58.json 2025-02-21 14:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-02-21 14:16:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/55.json 2025-02-21 14:16:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 14:16:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/54.json 2025-02-21 14:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 14:16:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/11.json 2025-02-21 14:16:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 14:16:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 14:16:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 14:16:57 ERROR Error while querying the DB : no such table: Patient 2025-02-21 14:16:57 ERROR Error loading dataset: 'NoneType' object has no attribute 'columns' 2025-02-21 14:55:16 INFO Date: 2025-02-21 ======================================== Time: 14:55:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 14:55:21 INFO not logined 2025-02-21 14:55:21 INFO Rendering unauthenticated menu. 2025-02-21 14:56:54 INFO Login button clicked. 2025-02-21 14:56:59 INFO Login successful for user: abhishek 2025-02-21 14:58:25 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````create a data set of all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-21 14:58:31 INFO Tokens consumed: 1490 2025-02-21 14:58:33 INFO Existing token_consumed found for month: 2025-02 2025-02-21 14:58:35 INFO token updated successfully: 2025-02 2025-02-21 14:58:35 INFO token updated successfully. 2025-02-21 14:58:35 INFO Query executed successfully. 2025-02-21 14:58:36 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-02-21 14:58:38 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-02-21 14:58:40 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:00:32 INFO Blob exists check for query_library/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-02-21 15:00:33 INFO SQL query blob saved successfully: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:00:33 INFO Query saved in the library with id 1. 2025-02-21 15:00:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:01:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:01:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:01:41 INFO Query executed successfully. 2025-02-21 15:01:41 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:02:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:02:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:02:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:02:39 INFO Query executed successfully. 2025-02-21 15:02:39 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:02:39 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:02:42 INFO Tokens consumed: 937 2025-02-21 15:02:48 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:02:49 INFO token updated successfully: 2025-02 2025-02-21 15:02:49 INFO token updated successfully. 2025-02-21 15:02:51 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-02-21 15:02:52 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: False 2025-02-21 15:02:54 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/1.py 2025-02-21 15:02:54 INFO Code generated and written in generated_method//0.py 2025-02-21 15:02:54 INFO Insight generated and displayed using AG Grid. 2025-02-21 15:03:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:03:23 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:03:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:03:24 INFO Query executed successfully. 2025-02-21 15:03:24 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:03:24 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-21 15:03:29 INFO Tokens consumed: 1694 2025-02-21 15:03:31 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:03:32 INFO token updated successfully: 2025-02 2025-02-21 15:03:32 INFO token updated successfully. 2025-02-21 15:03:36 INFO Plotly chart object created successfully. 2025-02-21 15:03:37 INFO Graph generated and displayed using Plotly. 2025-02-21 15:04:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:04:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:04:52 INFO Query executed successfully. 2025-02-21 15:04:52 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:05:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:05:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:05:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:05:14 INFO Query executed successfully. 2025-02-21 15:05:14 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:05:15 INFO No existing insight found for base code: SELECT * FROM Patient 2025-02-21 15:05:17 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-02-21 15:05:17 INFO Creating a new folder in the blob storage: 2025-02-21 15:05:20 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-02-21 15:05:21 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:05:23 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:06:24 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:06:24 INFO Insight list generated successfully. 2025-02-21 15:06:31 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:06:31 INFO Insight list generated successfully. 2025-02-21 15:06:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:06:33 INFO Query executed successfully. 2025-02-21 15:07:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:21 INFO Query executed successfully. 2025-02-21 15:07:21 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:07:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:07:38 INFO Query executed successfully. 2025-02-21 15:07:38 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:07:38 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:07:42 INFO Tokens consumed: 937 2025-02-21 15:07:44 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:07:46 INFO token updated successfully: 2025-02 2025-02-21 15:07:46 INFO token updated successfully. 2025-02-21 15:07:47 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 15:07:49 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:07:50 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/2.py 2025-02-21 15:07:50 INFO Code generated and written in generated_method//1.py 2025-02-21 15:07:50 INFO Insight generated and displayed using AG Grid. 2025-02-21 15:08:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:08:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:08:38 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:08:38 INFO Query executed successfully. 2025-02-21 15:08:38 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:08:38 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a scattered graph of no. of patient on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-21 15:08:52 INFO Tokens consumed: 2077 2025-02-21 15:08:55 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:08:57 INFO token updated successfully: 2025-02 2025-02-21 15:08:57 INFO token updated successfully. 2025-02-21 15:08:57 INFO Plotly chart object created successfully. 2025-02-21 15:08:57 ERROR Error in generating graph: %s 2025-02-21 15:09:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:09:31 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:09:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:09:33 INFO Query executed successfully. 2025-02-21 15:09:33 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:24:22 INFO Date: 2025-02-21 ======================================== Time: 15:24:22 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 15:24:27 INFO not logined 2025-02-21 15:24:27 INFO Rendering unauthenticated menu. 2025-02-21 15:25:45 INFO Login button clicked. 2025-02-21 15:25:49 INFO Login successful for user: abhishek 2025-02-21 15:26:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:27:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:27:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:27:01 INFO Query executed successfully. 2025-02-21 15:27:01 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:28:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:28:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:28:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:28:15 INFO Query executed successfully. 2025-02-21 15:28:15 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:28:15 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:28:19 INFO Tokens consumed: 936 2025-02-21 15:28:22 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:28:24 INFO token updated successfully: 2025-02 2025-02-21 15:28:24 INFO token updated successfully. 2025-02-21 15:28:25 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 2 2025-02-21 15:28:27 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:28:28 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/3.py 2025-02-21 15:28:28 INFO Code generated and written in generated_method//2.py 2025-02-21 15:28:29 ERROR Error executing generated code 2 for generate an insight of patient whose age is above 50: invalid syntax (, line 1) 2025-02-21 15:28:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:28:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:28:56 INFO Query executed successfully. 2025-02-21 15:28:56 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:28:56 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:28:59 INFO Tokens consumed: 939 2025-02-21 15:29:01 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:29:03 INFO token updated successfully: 2025-02 2025-02-21 15:29:03 INFO token updated successfully. 2025-02-21 15:29:05 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 3 2025-02-21 15:29:06 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:29:08 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/4.py 2025-02-21 15:29:08 INFO Code generated and written in generated_method//3.py 2025-02-21 15:29:08 INFO Insight generated and displayed using AG Grid. 2025-02-21 15:29:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:29:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:29:44 INFO Query executed successfully. 2025-02-21 15:29:44 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:30:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:30:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:30:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:30:14 INFO Query executed successfully. 2025-02-21 15:30:14 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:30:14 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a scattered graph number of patient v/s age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-21 15:30:26 INFO Tokens consumed: 2136 2025-02-21 15:30:28 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:30:30 INFO token updated successfully: 2025-02 2025-02-21 15:30:30 INFO token updated successfully. 2025-02-21 15:30:33 INFO Plotly chart object created successfully. 2025-02-21 15:31:30 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:31:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:31:32 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:31:33 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:31:33 INFO Query executed successfully. 2025-02-21 15:31:33 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:31:35 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-02-21 15:31:37 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-02-21 15:31:38 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 15:31:39 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:31:41 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 15:37:23 INFO Date: 2025-02-21 ======================================== Time: 15:37:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 15:37:27 INFO not logined 2025-02-21 15:37:27 INFO Rendering unauthenticated menu. 2025-02-21 15:37:46 INFO Login button clicked. 2025-02-21 15:37:50 INFO Login successful for user: abhishek 2025-02-21 15:38:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:39:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:39:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:39:36 INFO Query executed successfully. 2025-02-21 15:39:36 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:40:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:40:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:40:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:40:22 INFO Query executed successfully. 2025-02-21 15:40:22 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:40:22 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:40:25 INFO Tokens consumed: 939 2025-02-21 15:40:28 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:40:29 INFO token updated successfully: 2025-02 2025-02-21 15:40:29 INFO token updated successfully. 2025-02-21 15:40:31 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 4 2025-02-21 15:40:32 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:40:34 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/5.py 2025-02-21 15:40:34 INFO Code generated and written in generated_method//4.py 2025-02-21 15:40:34 INFO Insight generated and displayed using AG Grid. 2025-02-21 15:41:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:41:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:41:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:41:44 INFO Query executed successfully. 2025-02-21 15:41:44 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:41:46 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-02-21 15:41:47 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-02-21 15:41:49 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 15:41:50 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:41:51 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 15:45:12 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:45:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:45:13 INFO Query executed successfully. 2025-02-21 15:45:13 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:45:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:45:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:45:40 INFO Query executed successfully. 2025-02-21 15:45:40 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:45:42 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-02-21 15:45:44 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-02-21 15:45:45 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 15:45:46 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:45:48 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 15:47:34 INFO Date: 2025-02-21 ======================================== Time: 15:47:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 15:47:39 INFO not logined 2025-02-21 15:47:39 INFO Rendering unauthenticated menu. 2025-02-21 15:49:17 INFO Login button clicked. 2025-02-21 15:49:21 INFO Login successful for user: abhishek 2025-02-21 15:52:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:57:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:57:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:57:16 INFO Query executed successfully. 2025-02-21 15:57:16 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:58:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:58:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:58:52 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:58:52 INFO Query executed successfully. 2025-02-21 15:58:52 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:58:52 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 15:58:55 INFO Tokens consumed: 939 2025-02-21 15:58:58 INFO Existing token_consumed found for month: 2025-02 2025-02-21 15:58:59 INFO token updated successfully: 2025-02 2025-02-21 15:58:59 INFO token updated successfully. 2025-02-21 15:59:01 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 5 2025-02-21 15:59:03 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:59:04 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/6.py 2025-02-21 15:59:04 INFO Code generated and written in generated_method//5.py 2025-02-21 15:59:04 INFO Insight generated and displayed using AG Grid. 2025-02-21 15:59:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:59:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:59:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 15:59:39 INFO Query executed successfully. 2025-02-21 15:59:39 INFO Dataset columns displayed using AG Grid. 2025-02-21 15:59:41 ERROR Error while retrieving insight: Expecting value: line 1 column 1 (char 0) 2025-02-21 15:59:42 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: True 2025-02-21 15:59:44 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 15:59:45 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 15:59:47 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 16:04:16 INFO Date: 2025-02-21 ======================================== Time: 16:04:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 16:04:21 INFO not logined 2025-02-21 16:04:21 INFO Rendering unauthenticated menu. 2025-02-21 16:04:45 INFO Login button clicked. 2025-02-21 16:04:49 INFO Login successful for user: abhishek 2025-02-21 16:05:19 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:08:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:08:18 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:08:18 INFO Query executed successfully. 2025-02-21 16:08:18 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:09:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:01 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:02 INFO Query executed successfully. 2025-02-21 16:09:02 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:09:02 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 16:09:10 INFO Tokens consumed: 937 2025-02-21 16:09:12 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:09:14 INFO token updated successfully: 2025-02 2025-02-21 16:09:14 INFO token updated successfully. 2025-02-21 16:09:15 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 6 2025-02-21 16:09:17 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:09:18 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/7.py 2025-02-21 16:09:18 INFO Code generated and written in generated_method//6.py 2025-02-21 16:09:18 INFO Insight generated and displayed using AG Grid. 2025-02-21 16:09:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:45 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:46 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:09:46 INFO Query executed successfully. 2025-02-21 16:09:46 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:09:48 WARNING Skipping empty file: %s 2025-02-21 16:09:49 INFO Existing insight found for base code: %s 2025-02-21 16:09:51 INFO Insight updated successfully: %s 2025-02-21 16:09:51 INFO Insight updated successfully. 2025-02-21 16:11:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json 2025-02-21 16:11:33 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 16:11:34 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:11:34 INFO Insight list generated successfully. 2025-02-21 16:12:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:12:47 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:12:49 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:12:49 INFO Query executed successfully. 2025-02-21 16:12:49 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:13:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:02 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:02 INFO Query executed successfully. 2025-02-21 16:13:02 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:13:03 INFO No existing insight found for base code: %s 2025-02-21 16:13:04 INFO Blob exists check for insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-02-21 16:13:04 INFO Creating a new folder in the blob storage: 2025-02-21 16:13:07 INFO Latest file number in insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-02-21 16:13:08 INFO Blob exists check for insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:13:09 INFO New insight created: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:24 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:13:24 INFO Query executed successfully. 2025-02-21 16:13:24 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:13:26 WARNING Skipping empty file: %s 2025-02-21 16:13:27 INFO Existing insight found for base code: %s 2025-02-21 16:13:28 INFO Insight updated successfully: %s 2025-02-21 16:13:28 INFO Insight updated successfully. 2025-02-21 16:15:03 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:15:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:15:04 INFO Query executed successfully. 2025-02-21 16:15:04 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:15:07 WARNING Skipping empty file: %s 2025-02-21 16:15:08 INFO Existing insight found for base code: %s 2025-02-21 16:15:09 INFO Insight updated successfully: %s 2025-02-21 16:15:09 INFO Insight updated successfully. 2025-02-21 16:17:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:18:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:18:00 INFO Query executed successfully. 2025-02-21 16:18:00 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:18:02 WARNING Skipping empty file: %s 2025-02-21 16:18:04 INFO Existing insight found for base code: %s 2025-02-21 16:18:05 INFO Insight updated successfully: %s 2025-02-21 16:18:05 INFO Insight updated successfully. 2025-02-21 16:24:12 INFO Date: 2025-02-21 ======================================== Time: 16:24:12 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 16:24:16 INFO not logined 2025-02-21 16:24:16 INFO Rendering unauthenticated menu. 2025-02-21 16:24:46 INFO Login button clicked. 2025-02-21 16:24:50 INFO Login successful for user: abhishek 2025-02-21 16:25:27 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:25:50 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:25:51 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:25:52 INFO Query executed successfully. 2025-02-21 16:25:52 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:27:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:27:05 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:27:06 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:27:06 INFO Query executed successfully. 2025-02-21 16:27:06 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:27:06 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 16:27:10 INFO Tokens consumed: 939 2025-02-21 16:27:12 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:27:14 INFO token updated successfully: 2025-02 2025-02-21 16:27:14 INFO token updated successfully. 2025-02-21 16:27:15 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 7 2025-02-21 16:27:17 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:27:18 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/8.py 2025-02-21 16:27:18 INFO Code generated and written in generated_method//7.py 2025-02-21 16:27:19 INFO Insight generated and displayed using AG Grid. 2025-02-21 16:28:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:28:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:28:37 INFO Query executed successfully. 2025-02-21 16:28:37 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:28:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:28:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:28:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:28:54 INFO Query executed successfully. 2025-02-21 16:28:54 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:28:55 INFO No existing insight found for base code: %s 2025-02-21 16:28:57 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253: False 2025-02-21 16:28:57 INFO Creating a new folder in the blob storage: 2025-02-21 16:29:00 INFO Latest file number in insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: 0 2025-02-21 16:29:01 INFO Blob exists check for insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:29:02 INFO New insight created: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:29:07 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:29:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:29:09 INFO Query executed successfully. 2025-02-21 16:29:09 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:29:10 INFO Existing insight found for base code: %s 2025-02-21 16:29:12 INFO Insight updated successfully: %s 2025-02-21 16:29:12 INFO Insight updated successfully. 2025-02-21 16:35:50 INFO Date: 2025-02-21 ======================================== Time: 16:35:50 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 16:35:55 INFO not logined 2025-02-21 16:35:55 INFO Rendering unauthenticated menu. 2025-02-21 16:36:26 INFO Login button clicked. 2025-02-21 16:36:30 INFO Login successful for user: abhishek 2025-02-21 16:37:26 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:37:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:37:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:37:57 INFO Query executed successfully. 2025-02-21 16:37:57 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:38:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:38:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:38:36 INFO Query executed successfully. 2025-02-21 16:38:36 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:38:36 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 16:38:40 INFO Tokens consumed: 939 2025-02-21 16:38:42 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:38:44 INFO token updated successfully: 2025-02 2025-02-21 16:38:44 INFO token updated successfully. 2025-02-21 16:38:46 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 8 2025-02-21 16:38:48 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:38:49 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/9.py 2025-02-21 16:38:49 INFO Code generated and written in generated_method//8.py 2025-02-21 16:38:49 INFO Insight generated and displayed using AG Grid. 2025-02-21 16:39:13 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:39:14 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:39:14 INFO Query executed successfully. 2025-02-21 16:39:14 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:39:16 INFO Existing insight found for base code: %s 2025-02-21 16:39:17 INFO Insight updated successfully: %s 2025-02-21 16:39:17 INFO Insight updated successfully. 2025-02-21 16:40:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:28 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:29 INFO Query executed successfully. 2025-02-21 16:40:29 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:40:31 INFO Existing insight found for base code: %s 2025-02-21 16:40:32 INFO Insight updated successfully: %s 2025-02-21 16:40:32 INFO Insight updated successfully. 2025-02-21 16:40:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:44 INFO Insight list generated successfully. 2025-02-21 16:40:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:50 INFO Insight list generated successfully. 2025-02-21 16:40:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:40:51 INFO Query executed successfully. 2025-02-21 16:41:04 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:20 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:21 INFO Query executed successfully. 2025-02-21 16:41:21 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:41:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:57 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:41:57 INFO Query executed successfully. 2025-02-21 16:41:57 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:41:57 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate scattered graph patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-21 16:42:00 INFO Tokens consumed: 1509 2025-02-21 16:42:03 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:42:04 INFO token updated successfully: 2025-02 2025-02-21 16:42:04 INFO token updated successfully. 2025-02-21 16:42:05 ERROR Error creating plotly chart object: [Errno 2] No such file or directory: 'data.csv' 2025-02-21 16:42:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:42:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:42:40 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:42:40 INFO Query executed successfully. 2025-02-21 16:42:40 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:43:08 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:43:09 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:43:09 INFO Query executed successfully. 2025-02-21 16:43:09 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:43:09 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate scattered graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-21 16:43:18 INFO Tokens consumed: 2013 2025-02-21 16:43:21 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:43:22 INFO token updated successfully: 2025-02 2025-02-21 16:43:22 INFO token updated successfully. 2025-02-21 16:43:25 INFO Plotly chart object created successfully. 2025-02-21 16:44:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:37 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:37 INFO Query executed successfully. 2025-02-21 16:44:37 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:44:39 INFO Existing insight found for base code: %s 2025-02-21 16:44:41 INFO Insight updated successfully: %s 2025-02-21 16:44:41 INFO Insight updated successfully. 2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:55 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:56 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:44:56 INFO Query executed successfully. 2025-02-21 16:44:56 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:44:59 INFO Existing insight found for base code: %s 2025-02-21 16:45:01 INFO Insight updated successfully: %s 2025-02-21 16:45:01 INFO Insight updated successfully. 2025-02-21 16:45:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:45:16 INFO Insight list generated successfully. 2025-02-21 16:45:22 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:45:22 INFO Insight list generated successfully. 2025-02-21 16:45:23 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:45:23 INFO Query executed successfully. 2025-02-21 16:55:32 INFO Date: 2025-02-21 ======================================== Time: 16:55:32 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 16:55:36 INFO not logined 2025-02-21 16:55:36 INFO Rendering unauthenticated menu. 2025-02-21 16:55:56 INFO Login button clicked. 2025-02-21 16:56:00 INFO Login successful for user: abhishek 2025-02-21 16:56:29 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:57:21 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:57:22 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:57:22 INFO Query executed successfully. 2025-02-21 16:57:22 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:58:34 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:58:35 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:58:36 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:58:36 INFO Query executed successfully. 2025-02-21 16:58:36 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:58:36 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 50`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 16:58:40 INFO Tokens consumed: 939 2025-02-21 16:58:42 INFO Existing token_consumed found for month: 2025-02 2025-02-21 16:58:44 INFO token updated successfully: 2025-02 2025-02-21 16:58:44 INFO token updated successfully. 2025-02-21 16:58:45 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 9 2025-02-21 16:58:47 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 16:58:48 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/10.py 2025-02-21 16:58:48 INFO Code generated and written in generated_method//9.py 2025-02-21 16:58:48 INFO Insight generated and displayed using AG Grid. 2025-02-21 16:59:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:59:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:59:44 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:59:44 INFO Query executed successfully. 2025-02-21 16:59:44 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:59:46 INFO Existing insight found for base code: %s 2025-02-21 16:59:46 ERROR 1 2025-02-21 16:59:47 INFO Insight updated successfully: %s 2025-02-21 16:59:47 INFO Insight updated successfully. 2025-02-21 16:59:53 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:59:54 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 16:59:54 INFO Query executed successfully. 2025-02-21 16:59:54 INFO Dataset columns displayed using AG Grid. 2025-02-21 16:59:56 INFO Existing insight found for base code: %s 2025-02-21 16:59:56 ERROR 1 2025-02-21 16:59:57 INFO Insight updated successfully: %s 2025-02-21 16:59:57 INFO Insight updated successfully. 2025-02-21 17:00:05 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:00:06 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json 2025-02-21 17:00:06 INFO Insight list generated successfully. 2025-02-21 17:00:15 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:00:16 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json 2025-02-21 17:00:16 INFO Insight list generated successfully. 2025-02-21 17:00:17 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:00:17 INFO Query executed successfully. 2025-02-21 17:00:21 ERROR Error generating chart: StreamlitDuplicateElementId() 2025-02-21 17:00:58 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:01:00 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json 2025-02-21 17:01:00 INFO Insight list generated successfully. 2025-02-21 17:01:01 INFO Blob content retrieved successfully from: insight_library/Population Analyst/3418c428-10c1-70a4-55f6-370d11e8b253/['1', 'json'].json 2025-02-21 17:01:01 INFO Query executed successfully. 2025-02-21 17:01:10 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:01:10 INFO Insight list generated successfully. 2025-02-21 17:01:15 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:01:15 INFO Insight list generated successfully. 2025-02-21 17:01:17 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:01:17 INFO Query executed successfully. 2025-02-21 17:08:38 INFO Date: 2025-02-21 ======================================== Time: 17:08:38 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 17:08:42 INFO not logined 2025-02-21 17:08:42 INFO Rendering unauthenticated menu. 2025-02-21 17:13:33 INFO Login button clicked. 2025-02-21 17:13:37 INFO Login successful for user: abhishek 2025-02-21 17:14:11 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:15:39 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:15:41 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:15:41 INFO Query executed successfully. 2025-02-21 17:15:41 INFO Dataset columns displayed using AG Grid. 2025-02-21 17:16:15 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:16:16 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:16:17 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:16:17 INFO Query executed successfully. 2025-02-21 17:16:17 INFO Dataset columns displayed using AG Grid. 2025-02-21 17:16:17 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 17:16:22 INFO Tokens consumed: 977 2025-02-21 17:16:25 INFO Existing token_consumed found for month: 2025-02 2025-02-21 17:16:26 INFO token updated successfully: 2025-02 2025-02-21 17:16:26 INFO token updated successfully. 2025-02-21 17:16:28 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 10 2025-02-21 17:16:29 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 17:16:31 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/11.py 2025-02-21 17:16:31 INFO Code generated and written in generated_method//10.py 2025-02-21 17:16:31 ERROR Error executing generated code 10 for generate an insight of patient whose age is above 60: invalid syntax (, line 1) 2025-02-21 17:17:42 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:17:43 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:17:43 INFO Query executed successfully. 2025-02-21 17:17:43 INFO Dataset columns displayed using AG Grid. 2025-02-21 17:17:43 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``generate an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'df' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks. 2025-02-21 17:17:46 INFO Tokens consumed: 937 2025-02-21 17:17:48 INFO Existing token_consumed found for month: 2025-02 2025-02-21 17:17:50 INFO token updated successfully: 2025-02 2025-02-21 17:17:50 INFO token updated successfully. 2025-02-21 17:17:52 INFO Latest file number in generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: 11 2025-02-21 17:17:53 INFO Blob exists check for generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 17:17:55 INFO Python method blob saved successfully: generated_method/3418c428-10c1-70a4-55f6-370d11e8b253/12.py 2025-02-21 17:17:55 INFO Code generated and written in generated_method//11.py 2025-02-21 17:17:55 INFO Insight generated and displayed using AG Grid. 2025-02-21 17:18:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:18:59 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:19:00 INFO Blob content retrieved successfully from: query_library/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:19:00 INFO Query executed successfully. 2025-02-21 17:19:00 INFO Dataset columns displayed using AG Grid. 2025-02-21 17:19:02 INFO Existing insight found for base code: %s 2025-02-21 17:19:04 INFO Insight updated successfully: %s 2025-02-21 17:19:04 INFO Insight updated successfully. 2025-02-21 17:19:24 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:19:24 INFO Insight list generated successfully. 2025-02-21 17:20:02 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:20:02 INFO Insight list generated successfully. 2025-02-21 17:20:03 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/3418c428-10c1-70a4-55f6-370d11e8b253/1.json 2025-02-21 17:20:03 INFO Query executed successfully. 2025-02-21 18:33:48 INFO Date: 2025-02-21 ======================================== Time: 18:33:48 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 18:33:53 INFO not logined 2025-02-21 18:33:53 INFO Rendering unauthenticated menu. 2025-02-21 18:34:31 INFO Login button clicked. 2025-02-21 18:34:36 INFO Login successful for user: abhishek 2025-02-21 18:35:41 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-21 18:35:44 INFO Tokens consumed: 1487 2025-02-21 18:35:47 INFO Existing token_consumed found for month: 2025-02 2025-02-21 18:35:48 INFO token updated successfully: 2025-02 2025-02-21 18:35:48 INFO token updated successfully. 2025-02-21 18:35:48 INFO Query executed successfully. 2025-02-21 18:35:49 INFO Latest file number in generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: 1 2025-02-21 18:35:50 INFO Blob exists check for generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/: True 2025-02-21 18:35:52 INFO SQL query blob saved successfully: generated_sql/3418c428-10c1-70a4-55f6-370d11e8b253/2.json 2025-02-21 18:40:10 INFO User logged out. 2025-02-21 18:40:11 INFO Date: 2025-02-21 ======================================== Time: 18:40:11 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 18:40:11 INFO not logined 2025-02-21 18:40:11 INFO not logined 2025-02-21 18:40:11 INFO Rendering unauthenticated menu. 2025-02-21 18:40:11 INFO Rendering unauthenticated menu. 2025-02-21 23:19:16 INFO Date: 2025-02-21 ======================================== Time: 23:19:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-21 23:19:20 INFO not logined 2025-02-21 23:19:20 INFO Rendering unauthenticated menu. 2025-02-21 23:20:05 INFO Login button clicked. 2025-02-21 23:20:10 INFO Login successful for user: maheshsr 2025-02-21 23:21:20 INFO Insight list generated successfully. 2025-02-24 10:07:14 INFO Date: 2025-02-24 ======================================== Time: 10:07:14 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 10:07:18 INFO not logined 2025-02-24 10:07:18 INFO Rendering unauthenticated menu. 2025-02-24 10:07:38 INFO Login button clicked. 2025-02-24 10:07:41 INFO Login successful for user: maheshsr 2025-02-24 10:08:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:09:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:18 INFO Query executed successfully. 2025-02-24 10:15:18 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:15:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:15:52 INFO Query executed successfully. 2025-02-24 10:15:52 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:15:52 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 10:15:55 INFO Tokens consumed: 1025 2025-02-24 10:15:57 INFO Existing token_consumed found for month: 2025-02 2025-02-24 10:15:58 INFO token updated successfully: 2025-02 2025-02-24 10:15:58 INFO token updated successfully. 2025-02-24 10:15:58 INFO # create a dataset of patient whose age is above 60 import duckdb result_df = duckdb.query(''' SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 ''').to_df() output = {'result_df': result_df} 2025-02-24 10:15:59 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 2 2025-02-24 10:16:00 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 10:16:01 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/3.py 2025-02-24 10:16:01 INFO Code generated and written in generated_method//2.py 2025-02-24 10:16:01 ERROR Error executing the query: %s 2025-02-24 10:32:10 INFO Date: 2025-02-24 ======================================== Time: 10:32:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 10:32:15 INFO not logined 2025-02-24 10:32:15 INFO Rendering unauthenticated menu. 2025-02-24 10:32:47 INFO Login button clicked. 2025-02-24 10:32:50 INFO Login successful for user: maheshsr 2025-02-24 10:33:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:33:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:33:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:33:35 INFO Query executed successfully. 2025-02-24 10:33:35 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:34:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:34:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:34:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:34:09 INFO Query executed successfully. 2025-02-24 10:34:09 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:34:09 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60 `` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 10:34:13 INFO Tokens consumed: 1031 2025-02-24 10:34:15 INFO Existing token_consumed found for month: 2025-02 2025-02-24 10:34:16 INFO token updated successfully: 2025-02 2025-02-24 10:34:16 INFO token updated successfully. 2025-02-24 10:34:16 INFO ```python # Task: create a dataset of patient whose age is above 60 import duckdb query = """ SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 """ result_df = duckdb.query(query).to_df() output = {'result_df': result_df} ``` 2025-02-24 10:34:18 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 3 2025-02-24 10:34:19 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 10:34:20 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/4.py 2025-02-24 10:34:20 INFO Code generated and written in generated_method//3.py 2025-02-24 10:34:20 ERROR Error executing the query: %s 2025-02-24 10:37:49 INFO Date: 2025-02-24 ======================================== Time: 10:37:49 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 10:37:54 INFO not logined 2025-02-24 10:37:54 INFO Rendering unauthenticated menu. 2025-02-24 10:38:43 INFO Login button clicked. 2025-02-24 10:38:46 INFO Login successful for user: maheshsr 2025-02-24 10:39:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:40:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:40:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:40:30 INFO Query executed successfully. 2025-02-24 10:40:30 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:41:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:41:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:41:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:41:21 INFO Query executed successfully. 2025-02-24 10:41:21 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:41:21 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 10:41:24 INFO Tokens consumed: 1024 2025-02-24 10:41:26 INFO Existing token_consumed found for month: 2025-02 2025-02-24 10:41:27 INFO token updated successfully: 2025-02 2025-02-24 10:41:27 INFO token updated successfully. 2025-02-24 10:41:27 INFO # create a dataset of patient whose age is above 60 import duckdb result_df = duckdb.query(''' SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 ''').to_df() output['result_df'] = result_df 2025-02-24 10:41:28 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 4 2025-02-24 10:41:30 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 10:41:31 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/5.py 2025-02-24 10:41:31 INFO Code generated and written in generated_method//4.py 2025-02-24 10:41:31 ERROR SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 2025-02-24 10:41:31 INFO Insight generated and displayed using AG Grid. 2025-02-24 10:43:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:43:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:43:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:43:52 INFO Query executed successfully. 2025-02-24 10:43:52 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:43:52 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 10:43:57 INFO Tokens consumed: 1025 2025-02-24 10:43:59 INFO Existing token_consumed found for month: 2025-02 2025-02-24 10:44:00 INFO token updated successfully: 2025-02 2025-02-24 10:44:00 INFO token updated successfully. 2025-02-24 10:44:00 INFO # create a dataset of patient whose age is above 60 import duckdb result_df = duckdb.query(''' SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 ''').to_df() output = {'result_df': result_df} 2025-02-24 10:44:01 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 5 2025-02-24 10:44:03 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 10:44:04 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/6.py 2025-02-24 10:44:04 INFO Code generated and written in generated_method//5.py 2025-02-24 10:44:04 ERROR SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 2025-02-24 10:44:04 INFO Insight generated and displayed using AG Grid. 2025-02-24 10:44:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:44:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:44:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 10:44:15 INFO Query executed successfully. 2025-02-24 10:44:15 INFO Dataset columns displayed using AG Grid. 2025-02-24 10:44:15 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 10:44:19 INFO Tokens consumed: 1024 2025-02-24 10:44:21 INFO Existing token_consumed found for month: 2025-02 2025-02-24 10:44:22 INFO token updated successfully: 2025-02 2025-02-24 10:44:22 INFO token updated successfully. 2025-02-24 10:44:22 INFO # create a dataset of patient whose age is above 70 import duckdb result_df = duckdb.query(''' SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 70 ''').to_df() output['result_df'] = result_df 2025-02-24 10:44:23 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 6 2025-02-24 10:44:25 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 10:44:26 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/7.py 2025-02-24 10:44:26 INFO Code generated and written in generated_method//6.py 2025-02-24 10:44:26 ERROR SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 70 2025-02-24 10:44:26 INFO Insight generated and displayed using AG Grid. 2025-02-24 11:44:25 INFO Date: 2025-02-24 ======================================== Time: 11:44:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 11:44:29 INFO not logined 2025-02-24 11:44:29 INFO Rendering unauthenticated menu. 2025-02-24 11:44:45 INFO Login button clicked. 2025-02-24 11:44:49 INFO Login successful for user: maheshsr 2025-02-24 11:45:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:45:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:45:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:45:34 INFO Query executed successfully. 2025-02-24 11:45:34 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:46:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:46:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:46:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:46:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:46:53 INFO Query executed successfully. 2025-02-24 11:46:53 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:46:53 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 11:46:58 INFO Tokens consumed: 1026 2025-02-24 11:47:00 INFO Existing token_consumed found for month: 2025-02 2025-02-24 11:47:01 INFO token updated successfully: 2025-02 2025-02-24 11:47:01 INFO token updated successfully. 2025-02-24 11:47:03 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 7 2025-02-24 11:47:04 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 11:47:05 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/8.py 2025-02-24 11:47:05 INFO Code generated and written in generated_method//7.py 2025-02-24 11:47:05 INFO Insight generated and displayed using AG Grid. 2025-02-24 11:49:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:49:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:49:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:49:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:49:06 INFO Query executed successfully. 2025-02-24 11:49:06 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:49:07 INFO No existing insight found for base code: %s 2025-02-24 11:49:08 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03: False 2025-02-24 11:49:08 INFO Creating a new folder in the blob storage: 2025-02-24 11:49:10 INFO Latest file number in insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: 0 2025-02-24 11:49:11 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 11:49:11 ERROR Error while creating new insight: Object of type DataFrame is not JSON serializable 2025-02-24 11:49:32 INFO Insight list generated successfully. 2025-02-24 11:49:38 INFO Insight list generated successfully. 2025-02-24 11:49:41 INFO Insight list generated successfully. 2025-02-24 11:49:47 INFO Insight list generated successfully. 2025-02-24 11:55:34 INFO Date: 2025-02-24 ======================================== Time: 11:55:34 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 11:55:38 INFO not logined 2025-02-24 11:55:39 INFO Rendering unauthenticated menu. 2025-02-24 11:56:27 INFO Login button clicked. 2025-02-24 11:56:31 INFO Login successful for user: maheshsr 2025-02-24 11:56:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:57:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:57:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:57:24 INFO Query executed successfully. 2025-02-24 11:57:24 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:58:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:58:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:58:20 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:58:20 INFO Query executed successfully. 2025-02-24 11:58:20 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:58:20 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 11:58:25 INFO Tokens consumed: 1024 2025-02-24 11:58:27 INFO Existing token_consumed found for month: 2025-02 2025-02-24 11:58:28 INFO token updated successfully: 2025-02 2025-02-24 11:58:28 INFO token updated successfully. 2025-02-24 11:58:29 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 8 2025-02-24 11:58:30 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 11:58:31 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/9.py 2025-02-24 11:58:31 INFO Code generated and written in generated_method//8.py 2025-02-24 11:58:31 ERROR Error executing the query: %s 2025-02-24 11:58:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:58:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:58:59 INFO Query executed successfully. 2025-02-24 11:58:59 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:58:59 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 11:59:03 INFO Tokens consumed: 1025 2025-02-24 11:59:05 INFO Existing token_consumed found for month: 2025-02 2025-02-24 11:59:06 INFO token updated successfully: 2025-02 2025-02-24 11:59:06 INFO token updated successfully. 2025-02-24 11:59:07 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 9 2025-02-24 11:59:09 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 11:59:09 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/10.py 2025-02-24 11:59:09 INFO Code generated and written in generated_method//9.py 2025-02-24 11:59:09 INFO Insight generated and displayed using AG Grid. 2025-02-24 11:59:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:59:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:59:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:59:40 INFO Query executed successfully. 2025-02-24 11:59:40 INFO Dataset columns displayed using AG Grid. 2025-02-24 11:59:41 INFO No existing insight found for base code: %s 2025-02-24 11:59:42 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03: True 2025-02-24 11:59:43 INFO Latest file number in insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: 0 2025-02-24 11:59:44 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 11:59:45 INFO New insight created: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 11:59:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:02 INFO Query executed successfully. 2025-02-24 12:00:02 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:00:11 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:11 INFO Insight list generated successfully. 2025-02-24 12:00:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:16 INFO Insight list generated successfully. 2025-02-24 12:00:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:00:16 INFO Query executed successfully. 2025-02-24 12:00:16 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "insight_code"') 2025-02-24 12:17:02 INFO Date: 2025-02-24 ======================================== Time: 12:17:02 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 12:17:07 INFO not logined 2025-02-24 12:17:07 INFO Rendering unauthenticated menu. 2025-02-24 12:19:16 INFO Login button clicked. 2025-02-24 12:19:19 INFO Login successful for user: maheshsr 2025-02-24 12:19:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:20:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:20:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:20:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:20:33 INFO Query executed successfully. 2025-02-24 12:20:33 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:21:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:11 INFO Query executed successfully. 2025-02-24 12:21:11 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:21:11 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 12:21:14 INFO Tokens consumed: 1025 2025-02-24 12:21:16 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:21:17 INFO token updated successfully: 2025-02 2025-02-24 12:21:17 INFO token updated successfully. 2025-02-24 12:21:19 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 10 2025-02-24 12:21:20 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:21:20 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/11.py 2025-02-24 12:21:20 INFO Code generated and written in generated_method//10.py 2025-02-24 12:21:21 INFO Insight generated and displayed using AG Grid. 2025-02-24 12:21:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:46 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:46 INFO Query executed successfully. 2025-02-24 12:21:46 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:21:46 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 12:21:49 INFO Tokens consumed: 1025 2025-02-24 12:21:50 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:21:52 INFO token updated successfully: 2025-02 2025-02-24 12:21:52 INFO token updated successfully. 2025-02-24 12:21:53 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 11 2025-02-24 12:21:54 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:21:55 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/12.py 2025-02-24 12:21:55 INFO Code generated and written in generated_method//11.py 2025-02-24 12:21:55 INFO Insight generated and displayed using AG Grid. 2025-02-24 12:21:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:21:58 INFO Query executed successfully. 2025-02-24 12:21:58 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:22:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:22:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:22:08 INFO Query executed successfully. 2025-02-24 12:22:08 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:22:08 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 12:22:13 INFO Tokens consumed: 1025 2025-02-24 12:22:15 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:22:16 INFO token updated successfully: 2025-02 2025-02-24 12:22:16 INFO token updated successfully. 2025-02-24 12:22:18 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 12 2025-02-24 12:22:19 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:22:20 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/13.py 2025-02-24 12:22:20 INFO Code generated and written in generated_method//12.py 2025-02-24 12:22:20 INFO Insight generated and displayed using AG Grid. 2025-02-24 12:24:16 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:24:16 INFO Insight list generated successfully. 2025-02-24 12:24:20 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:24:20 INFO Insight list generated successfully. 2025-02-24 12:24:21 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:24:21 INFO Query executed successfully. 2025-02-24 12:24:21 ERROR Error executing generated insight code: ValueError('Expected object or value') 2025-02-24 12:32:26 INFO Date: 2025-02-24 ======================================== Time: 12:32:26 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 12:32:30 INFO not logined 2025-02-24 12:32:30 INFO Rendering unauthenticated menu. 2025-02-24 12:32:52 INFO Login button clicked. 2025-02-24 12:32:55 INFO Login successful for user: maheshsr 2025-02-24 12:34:03 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-24 12:34:05 INFO Tokens consumed: 1486 2025-02-24 12:34:07 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:34:08 INFO token updated successfully: 2025-02 2025-02-24 12:34:08 INFO token updated successfully. 2025-02-24 12:34:08 INFO Query executed successfully. 2025-02-24 12:34:09 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 1 2025-02-24 12:34:10 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:34:11 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/2.json 2025-02-24 12:34:45 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the risk score```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-24 12:34:48 INFO Tokens consumed: 1494 2025-02-24 12:34:49 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:34:50 INFO token updated successfully: 2025-02 2025-02-24 12:34:50 INFO token updated successfully. 2025-02-24 12:34:50 INFO Query executed successfully. 2025-02-24 12:34:51 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 2 2025-02-24 12:34:52 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:34:53 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/3.json 2025-02-24 12:35:18 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:35:18 INFO Insight list generated successfully. 2025-02-24 12:35:23 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:35:23 INFO Insight list generated successfully. 2025-02-24 12:35:24 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:35:24 INFO Query executed successfully. 2025-02-24 12:35:24 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "{"') 2025-02-24 12:39:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:39:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:39:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:39:26 INFO Query executed successfully. 2025-02-24 12:39:26 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:40:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:05 INFO Query executed successfully. 2025-02-24 12:40:05 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:40:05 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 12:40:08 INFO Tokens consumed: 1023 2025-02-24 12:40:10 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:40:12 INFO token updated successfully: 2025-02 2025-02-24 12:40:12 INFO token updated successfully. 2025-02-24 12:40:13 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 13 2025-02-24 12:40:15 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:40:16 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/14.py 2025-02-24 12:40:16 INFO Code generated and written in generated_method//13.py 2025-02-24 12:40:16 INFO Insight generated and displayed using AG Grid. 2025-02-24 12:40:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:40:25 INFO Query executed successfully. 2025-02-24 12:40:25 INFO Dataset columns displayed using AG Grid. 2025-02-24 12:40:25 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 12:40:28 INFO Tokens consumed: 1023 2025-02-24 12:40:30 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:40:31 INFO token updated successfully: 2025-02 2025-02-24 12:40:31 INFO token updated successfully. 2025-02-24 12:40:33 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 14 2025-02-24 12:40:34 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:40:35 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/15.py 2025-02-24 12:40:35 INFO Code generated and written in generated_method//14.py 2025-02-24 12:40:35 INFO Insight generated and displayed using AG Grid. 2025-02-24 12:48:24 INFO Date: 2025-02-24 ======================================== Time: 12:48:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 12:48:28 INFO not logined 2025-02-24 12:48:28 INFO Rendering unauthenticated menu. 2025-02-24 12:48:45 INFO Login button clicked. 2025-02-24 12:48:48 INFO Login successful for user: maheshsr 2025-02-24 12:49:19 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:49:19 INFO Insight list generated successfully. 2025-02-24 12:49:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:49:41 INFO Insight list generated successfully. 2025-02-24 12:49:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:49:42 INFO Query executed successfully. 2025-02-24 12:49:42 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "{"') 2025-02-24 12:50:28 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-24 12:50:29 INFO Tokens consumed: 1486 2025-02-24 12:50:31 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:50:32 INFO token updated successfully: 2025-02 2025-02-24 12:50:32 INFO token updated successfully. 2025-02-24 12:50:32 INFO Query executed successfully. 2025-02-24 12:50:33 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 3 2025-02-24 12:50:35 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:50:36 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/4.json 2025-02-24 12:50:54 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the risk score```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-24 12:50:56 INFO Tokens consumed: 1494 2025-02-24 12:50:58 INFO Existing token_consumed found for month: 2025-02 2025-02-24 12:50:59 INFO token updated successfully: 2025-02 2025-02-24 12:50:59 INFO token updated successfully. 2025-02-24 12:50:59 INFO Query executed successfully. 2025-02-24 12:51:00 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 4 2025-02-24 12:51:01 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 12:51:03 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/5.json 2025-02-24 12:51:38 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:52:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:52:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 12:52:15 INFO Query executed successfully. 2025-02-24 12:52:15 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:05:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:05:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:05:28 INFO Query executed successfully. 2025-02-24 13:05:28 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:05:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:05:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:05:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:05:53 INFO Query executed successfully. 2025-02-24 13:05:53 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:05:53 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 13:05:56 INFO Tokens consumed: 1025 2025-02-24 13:05:58 INFO Existing token_consumed found for month: 2025-02 2025-02-24 13:06:00 INFO token updated successfully: 2025-02 2025-02-24 13:06:00 INFO token updated successfully. 2025-02-24 13:06:01 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 15 2025-02-24 13:06:03 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 13:06:04 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/16.py 2025-02-24 13:06:04 INFO Code generated and written in generated_method//15.py 2025-02-24 13:06:04 INFO Insight generated and displayed using AG Grid. 2025-02-24 13:38:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:38:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:38:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:38:44 INFO Query executed successfully. 2025-02-24 13:38:44 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:38:44 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 13:38:48 INFO Tokens consumed: 1024 2025-02-24 13:38:50 INFO Existing token_consumed found for month: 2025-02 2025-02-24 13:38:51 INFO token updated successfully: 2025-02 2025-02-24 13:38:51 INFO token updated successfully. 2025-02-24 13:38:52 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 16 2025-02-24 13:38:53 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 13:38:54 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/17.py 2025-02-24 13:38:54 INFO Code generated and written in generated_method//16.py 2025-02-24 13:38:54 INFO Insight generated and displayed using AG Grid. 2025-02-24 13:39:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:02 INFO Query executed successfully. 2025-02-24 13:39:02 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:39:02 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 13:39:06 INFO Tokens consumed: 1025 2025-02-24 13:39:07 INFO Existing token_consumed found for month: 2025-02 2025-02-24 13:39:09 INFO token updated successfully: 2025-02 2025-02-24 13:39:09 INFO token updated successfully. 2025-02-24 13:39:10 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 17 2025-02-24 13:39:11 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 13:39:12 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/18.py 2025-02-24 13:39:12 INFO Code generated and written in generated_method//17.py 2025-02-24 13:39:12 INFO Insight generated and displayed using AG Grid. 2025-02-24 13:39:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:39:37 INFO Query executed successfully. 2025-02-24 13:39:37 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:39:37 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```create a bar graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-24 13:39:42 INFO Tokens consumed: 1721 2025-02-24 13:39:43 INFO Existing token_consumed found for month: 2025-02 2025-02-24 13:39:45 INFO token updated successfully: 2025-02 2025-02-24 13:39:45 INFO token updated successfully. 2025-02-24 13:39:48 INFO Plotly chart object created successfully. 2025-02-24 13:39:48 INFO Graph generated and displayed using Plotly. 2025-02-24 13:41:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:41:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:41:07 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:41:07 INFO Query executed successfully. 2025-02-24 13:41:07 INFO Dataset columns displayed using AG Grid. 2025-02-24 13:41:07 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```create scattered graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-24 13:41:15 INFO Tokens consumed: 2020 2025-02-24 13:41:17 INFO Existing token_consumed found for month: 2025-02 2025-02-24 13:41:18 INFO token updated successfully: 2025-02 2025-02-24 13:41:18 INFO token updated successfully. 2025-02-24 13:41:19 INFO Plotly chart object created successfully. 2025-02-24 13:41:19 INFO Graph generated and displayed using Plotly. 2025-02-24 13:55:31 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:55:31 INFO Insight list generated successfully. 2025-02-24 13:55:42 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:55:42 INFO Insight list generated successfully. 2025-02-24 13:55:43 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 13:55:43 INFO Query executed successfully. 2025-02-24 13:55:43 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "{"') 2025-02-24 14:10:24 INFO Date: 2025-02-24 ======================================== Time: 14:10:24 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 14:10:28 INFO not logined 2025-02-24 14:10:28 INFO Rendering unauthenticated menu. 2025-02-24 14:10:41 INFO Login button clicked. 2025-02-24 14:10:45 INFO Login successful for user: maheshsr 2025-02-24 14:13:59 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 14:13:59 INFO Insight list generated successfully. 2025-02-24 14:14:28 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 14:14:28 INFO Insight list generated successfully. 2025-02-24 14:14:29 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 14:14:29 INFO Query executed successfully. 2025-02-24 14:14:29 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "insight_code"') 2025-02-24 15:13:20 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:13:20 INFO Insight list generated successfully. 2025-02-24 15:13:21 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:13:21 INFO Query executed successfully. 2025-02-24 15:13:21 ERROR Error executing generated insight code: ParserException('Parser Error: syntax error at or near "insight_code"') 2025-02-24 15:19:30 INFO Date: 2025-02-24 ======================================== Time: 15:19:30 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:19:35 INFO not logined 2025-02-24 15:19:35 INFO Rendering unauthenticated menu. 2025-02-24 15:20:06 INFO Login button clicked. 2025-02-24 15:20:10 INFO Login successful for user: maheshsr 2025-02-24 15:23:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:24:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:24:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:24:36 INFO Query executed successfully. 2025-02-24 15:24:36 INFO Dataset columns displayed using AG Grid. 2025-02-24 15:26:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:26:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:26:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:26:10 INFO Query executed successfully. 2025-02-24 15:26:10 INFO Dataset columns displayed using AG Grid. 2025-02-24 15:26:10 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 15:26:17 INFO Tokens consumed: 1025 2025-02-24 15:26:18 INFO Existing token_consumed found for month: 2025-02 2025-02-24 15:26:20 INFO token updated successfully: 2025-02 2025-02-24 15:26:20 INFO token updated successfully. 2025-02-24 15:26:21 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 18 2025-02-24 15:26:22 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 15:26:23 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/19.py 2025-02-24 15:26:23 INFO Code generated and written in generated_method//18.py 2025-02-24 15:26:23 INFO Insight generated and displayed using AG Grid. 2025-02-24 15:27:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:27:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:27:31 INFO Query executed successfully. 2025-02-24 15:27:31 INFO Dataset columns displayed using AG Grid. 2025-02-24 15:27:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:27:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:27:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:27:36 INFO Query executed successfully. 2025-02-24 15:27:36 INFO Dataset columns displayed using AG Grid. 2025-02-24 15:27:37 INFO No existing insight found for base code: %s 2025-02-24 15:27:38 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03: True 2025-02-24 15:27:39 INFO Latest file number in insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: 0 2025-02-24 15:27:40 INFO Blob exists check for insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 15:27:41 INFO New insight created: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:28:15 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:28:15 INFO Insight list generated successfully. 2025-02-24 15:28:21 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:28:21 INFO Insight list generated successfully. 2025-02-24 15:28:22 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:28:22 INFO Query executed successfully. 2025-02-24 15:28:22 ERROR SELECT id, identifier_value, identifier_use, identifier_type, identifier_start_date, identifier_assigner, active, official_name_family, official_name_given, usual_name_given, gender, birth_date, Age, home_address_line, home_address_city, home_address_district, home_address_state, home_address_postalCode, home_address_period_start FROM mydf WHERE Age > 60 2025-02-24 15:29:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 15:44:14 INFO Date: 2025-02-24 ======================================== Time: 15:44:14 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:44:18 INFO not logined 2025-02-24 15:44:18 INFO Rendering unauthenticated menu. 2025-02-24 15:44:48 INFO Login button clicked. 2025-02-24 15:44:51 INFO Login successful for user: maheshsr 2025-02-24 15:49:01 INFO Date: 2025-02-24 ======================================== Time: 15:49:01 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:49:01 INFO not logined 2025-02-24 15:49:01 INFO Rendering unauthenticated menu. 2025-02-24 15:49:01 INFO Rendering unauthenticated menu. 2025-02-24 15:49:22 INFO Login button clicked. 2025-02-24 15:49:22 INFO Login button clicked. 2025-02-24 15:49:26 INFO Login successful for user: maheshsr 2025-02-24 15:49:26 INFO Login successful for user: maheshsr 2025-02-24 15:51:15 INFO Date: 2025-02-24 ======================================== Time: 15:51:15 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:51:20 INFO not logined 2025-02-24 15:51:20 INFO Rendering unauthenticated menu. 2025-02-24 15:51:39 INFO Login button clicked. 2025-02-24 15:51:42 INFO Login successful for user: maheshsr 2025-02-24 15:55:05 INFO Date: 2025-02-24 ======================================== Time: 15:55:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:55:05 INFO Date: 2025-02-24 ======================================== Time: 15:55:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 15:55:05 INFO not logined 2025-02-24 15:55:05 INFO not logined 2025-02-24 15:55:05 INFO Rendering unauthenticated menu. 2025-02-24 15:55:05 INFO Rendering unauthenticated menu. 2025-02-24 16:22:52 INFO Login button clicked. 2025-02-24 16:22:52 INFO Login button clicked. 2025-02-24 16:22:55 INFO Login successful for user: maheshsr 2025-02-24 16:22:55 INFO Login successful for user: maheshsr 2025-02-24 16:53:32 INFO Date: 2025-02-24 ======================================== Time: 16:53:32 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:53:32 INFO Date: 2025-02-24 ======================================== Time: 16:53:32 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:53:32 INFO not logined 2025-02-24 16:53:32 INFO not logined 2025-02-24 16:53:32 INFO Rendering unauthenticated menu. 2025-02-24 16:53:32 INFO Rendering unauthenticated menu. 2025-02-24 16:53:59 INFO Login button clicked. 2025-02-24 16:53:59 INFO Login button clicked. 2025-02-24 16:53:59 INFO Login button clicked. 2025-02-24 16:54:02 INFO Login successful for user: maheshsr 2025-02-24 16:54:02 INFO Login successful for user: maheshsr 2025-02-24 16:54:02 INFO Login successful for user: maheshsr 2025-02-24 16:54:06 INFO User logged out. 2025-02-24 16:54:06 INFO User logged out. 2025-02-24 16:54:06 INFO User logged out. 2025-02-24 16:54:06 INFO Date: 2025-02-24 ======================================== Time: 16:54:06 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:54:06 INFO Date: 2025-02-24 ======================================== Time: 16:54:06 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:54:06 INFO Date: 2025-02-24 ======================================== Time: 16:54:06 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:54:06 INFO not logined 2025-02-24 16:54:06 INFO not logined 2025-02-24 16:54:06 INFO not logined 2025-02-24 16:54:06 INFO not logined 2025-02-24 16:54:06 INFO Rendering unauthenticated menu. 2025-02-24 16:54:06 INFO Rendering unauthenticated menu. 2025-02-24 16:56:10 INFO Date: 2025-02-24 ======================================== Time: 16:56:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-24 16:56:14 INFO not logined 2025-02-24 16:56:14 INFO Rendering unauthenticated menu. 2025-02-24 16:56:39 INFO Login button clicked. 2025-02-24 16:56:42 INFO Login successful for user: maheshsr 2025-02-24 16:57:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:57:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:58:13 INFO Query executed successfully. 2025-02-24 16:58:13 INFO Dataset columns displayed using AG Grid. 2025-02-24 16:58:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:59:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:59:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 16:59:01 INFO Query executed successfully. 2025-02-24 16:59:01 INFO Dataset columns displayed using AG Grid. 2025-02-24 16:59:01 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 16:59:05 INFO Tokens consumed: 1027 2025-02-24 16:59:07 INFO Existing token_consumed found for month: 2025-02 2025-02-24 16:59:08 INFO token updated successfully: 2025-02 2025-02-24 16:59:08 INFO token updated successfully. 2025-02-24 16:59:10 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 19 2025-02-24 16:59:11 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 16:59:12 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/20.py 2025-02-24 16:59:12 INFO Code generated and written in generated_method//19.py 2025-02-24 16:59:12 ERROR Error executing the query: %s 2025-02-24 17:00:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:00:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:00:42 INFO Query executed successfully. 2025-02-24 17:00:42 INFO Dataset columns displayed using AG Grid. 2025-02-24 17:00:42 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 17:00:45 INFO Tokens consumed: 1027 2025-02-24 17:00:47 INFO Existing token_consumed found for month: 2025-02 2025-02-24 17:00:48 INFO token updated successfully: 2025-02 2025-02-24 17:00:48 INFO token updated successfully. 2025-02-24 17:00:49 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 20 2025-02-24 17:00:51 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 17:00:52 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/21.py 2025-02-24 17:00:52 INFO Code generated and written in generated_method//20.py 2025-02-24 17:00:52 ERROR Error executing the query: %s 2025-02-24 17:39:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:39:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:39:14 INFO Query executed successfully. 2025-02-24 17:39:14 INFO Dataset columns displayed using AG Grid. 2025-02-24 17:39:14 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 17:39:18 INFO Tokens consumed: 1027 2025-02-24 17:39:21 INFO Existing token_consumed found for month: 2025-02 2025-02-24 17:39:23 INFO token updated successfully: 2025-02 2025-02-24 17:39:23 INFO token updated successfully. 2025-02-24 17:39:25 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 21 2025-02-24 17:39:26 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 17:39:28 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/22.py 2025-02-24 17:39:28 INFO Code generated and written in generated_method//21.py 2025-02-24 17:39:28 ERROR Error executing the query: %s 2025-02-24 17:45:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:45:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-24 17:45:40 INFO Query executed successfully. 2025-02-24 17:45:40 INFO Dataset columns displayed using AG Grid. 2025-02-24 17:45:40 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-24 17:45:46 INFO Tokens consumed: 1026 2025-02-24 17:45:48 INFO Existing token_consumed found for month: 2025-02 2025-02-24 17:45:49 INFO token updated successfully: 2025-02 2025-02-24 17:45:49 INFO token updated successfully. 2025-02-24 17:45:50 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 22 2025-02-24 17:45:51 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-24 17:45:52 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/23.py 2025-02-24 17:45:52 INFO Code generated and written in generated_method//22.py 2025-02-24 17:45:52 ERROR Error executing the query: %s 2025-02-25 07:14:44 INFO Date: 2025-02-25 ======================================== Time: 07:14:44 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 07:14:50 INFO not logined 2025-02-25 07:14:50 INFO Rendering unauthenticated menu. 2025-02-25 07:16:04 INFO Login button clicked. 2025-02-25 07:16:09 INFO Login successful for user: maheshsr 2025-02-25 07:17:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:18:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:18:12 ERROR Exception while retrieving blob content: The specified blob does not exist. RequestId:cbf019ab-801e-001d-5727-87973d000000 Time:2025-02-25T01:48:14.5537655Z ErrorCode:BlobNotFound Content: BlobNotFoundThe specified blob does not exist. RequestId:cbf019ab-801e-001d-5727-87973d000000 Time:2025-02-25T01:48:14.5537655Z 2025-02-25 07:18:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:18:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:18:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:18:30 INFO Query executed successfully. 2025-02-25 07:18:30 INFO Dataset columns displayed using AG Grid. 2025-02-25 07:20:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:20:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:21:24 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:22:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:22:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:28:59 INFO Date: 2025-02-25 ======================================== Time: 07:28:59 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 07:29:03 INFO not logined 2025-02-25 07:29:03 INFO Rendering unauthenticated menu. 2025-02-25 07:29:42 INFO Login button clicked. 2025-02-25 07:29:46 INFO Login successful for user: maheshsr 2025-02-25 07:31:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:35:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 07:35:55 ERROR Exception while retrieving blob content: The specified blob does not exist. RequestId:566977cc-801e-0032-6129-879af6000000 Time:2025-02-25T02:05:56.7922166Z ErrorCode:BlobNotFound Content: BlobNotFoundThe specified blob does not exist. RequestId:566977cc-801e-0032-6129-879af6000000 Time:2025-02-25T02:05:56.7922166Z 2025-02-25 08:01:16 INFO Date: 2025-02-25 ======================================== Time: 08:01:16 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 08:01:20 INFO not logined 2025-02-25 08:01:20 INFO Rendering unauthenticated menu. 2025-02-25 08:03:09 INFO Login button clicked. 2025-02-25 08:03:14 INFO Login successful for user: maheshsr 2025-02-25 08:04:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:05:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:05:23 ERROR Exception while retrieving blob content: The specified blob does not exist. RequestId:66dcc16d-101e-0042-702d-872301000000 Time:2025-02-25T02:35:24.9334365Z ErrorCode:BlobNotFound Content: BlobNotFoundThe specified blob does not exist. RequestId:66dcc16d-101e-0042-702d-872301000000 Time:2025-02-25T02:35:24.9334365Z 2025-02-25 08:10:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:10:25 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:10:27 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:10:27 INFO Query executed successfully. 2025-02-25 08:10:27 INFO Dataset columns displayed using AG Grid. 2025-02-25 08:48:27 INFO Date: 2025-02-25 ======================================== Time: 08:48:27 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 08:48:31 INFO not logined 2025-02-25 08:48:31 INFO Rendering unauthenticated menu. 2025-02-25 08:48:55 INFO Login button clicked. 2025-02-25 08:48:59 INFO Login successful for user: maheshsr 2025-02-25 08:50:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:55:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:56:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:56:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 08:56:05 INFO Query executed successfully. 2025-02-25 09:02:25 INFO Date: 2025-02-25 ======================================== Time: 09:02:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 09:02:30 INFO not logined 2025-02-25 09:02:30 INFO Rendering unauthenticated menu. 2025-02-25 09:02:55 INFO Login button clicked. 2025-02-25 09:03:02 INFO Login successful for user: maheshsr 2025-02-25 09:16:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:16:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:17:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:17:00 INFO Query executed successfully. 2025-02-25 09:17:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:17:54 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:02 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:02 INFO Query executed successfully. 2025-02-25 09:18:02 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:18:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:18:43 INFO Query executed successfully. 2025-02-25 09:18:43 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:18:44 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 09:18:55 INFO Tokens consumed: 1021 2025-02-25 09:19:00 INFO Existing token_consumed found for month: 2025-02 2025-02-25 09:19:27 INFO token updated successfully: 2025-02 2025-02-25 09:19:27 INFO token updated successfully. 2025-02-25 09:19:39 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 26 2025-02-25 09:20:05 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 09:20:11 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/27.py 2025-02-25 09:20:11 INFO Code generated and written in generated_method//26.py 2025-02-25 09:20:11 ERROR Error executing the query: %s 2025-02-25 09:20:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:20:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:20:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:20:46 INFO Query executed successfully. 2025-02-25 09:20:46 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:20:46 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 09:21:03 INFO Tokens consumed: 1021 2025-02-25 09:21:26 INFO Existing token_consumed found for month: 2025-02 2025-02-25 09:21:54 INFO token updated successfully: 2025-02 2025-02-25 09:21:54 INFO token updated successfully. 2025-02-25 09:22:00 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 27 2025-02-25 09:22:23 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 09:22:29 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/28.py 2025-02-25 09:22:29 INFO Code generated and written in generated_method//27.py 2025-02-25 09:22:29 ERROR Error executing the query: %s 2025-02-25 09:22:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:22:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:23:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:23:09 INFO Query executed successfully. 2025-02-25 09:23:09 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:23:09 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 09:23:21 INFO Tokens consumed: 1025 2025-02-25 09:23:25 INFO Existing token_consumed found for month: 2025-02 2025-02-25 09:23:31 INFO token updated successfully: 2025-02 2025-02-25 09:23:31 INFO token updated successfully. 2025-02-25 09:23:33 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 28 2025-02-25 09:24:01 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 09:24:02 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/29.py 2025-02-25 09:24:02 INFO Code generated and written in generated_method//28.py 2025-02-25 09:24:02 ERROR Error executing the query: %s 2025-02-25 09:32:31 INFO Date: 2025-02-25 ======================================== Time: 09:32:31 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 09:32:36 INFO not logined 2025-02-25 09:32:36 INFO Rendering unauthenticated menu. 2025-02-25 09:33:02 INFO Login button clicked. 2025-02-25 09:33:14 INFO Login successful for user: maheshsr 2025-02-25 09:34:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:03 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:05 INFO Query executed successfully. 2025-02-25 09:35:05 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:35:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:35:57 INFO Query executed successfully. 2025-02-25 09:35:57 INFO Dataset columns displayed using AG Grid. 2025-02-25 09:35:57 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 09:36:06 INFO Tokens consumed: 1025 2025-02-25 09:36:17 INFO Existing token_consumed found for month: 2025-02 2025-02-25 09:36:20 INFO token updated successfully: 2025-02 2025-02-25 09:36:20 INFO token updated successfully. 2025-02-25 09:36:46 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 29 2025-02-25 09:36:51 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 09:37:08 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/30.py 2025-02-25 09:37:08 INFO Code generated and written in generated_method//29.py 2025-02-25 09:37:08 INFO Insight generated and displayed using AG Grid. 2025-02-25 09:38:10 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 09:38:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:21:25 INFO Date: 2025-02-25 ======================================== Time: 10:21:25 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 10:21:29 INFO not logined 2025-02-25 10:21:29 INFO Rendering unauthenticated menu. 2025-02-25 10:23:17 INFO Login button clicked. 2025-02-25 10:23:26 INFO Login successful for user: maheshsr 2025-02-25 10:51:10 INFO Date: 2025-02-25 ======================================== Time: 10:51:10 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 10:51:14 INFO not logined 2025-02-25 10:51:14 INFO Rendering unauthenticated menu. 2025-02-25 10:51:47 INFO Login button clicked. 2025-02-25 10:51:55 INFO Login successful for user: maheshsr 2025-02-25 10:53:20 INFO Generating SQL query with prompt: You are an expert in understanding an English language healthcare data query and translating it into an SQL Query that can be executed on a SQLite database. I am providing you the table names and their purposes that you need to use as a dictionary within double backticks. There may be more than one table. Table descriptions: ``{'Patient': 'The table stores the healthcare encounter information about patients. Each row has an unique patient information. The table contains the key information by distilling and flattening the FHIR encounter schema.', 'Encounter': 'Table that stores all encounters of each patient with the healthcare providers. Every row indicate a single encounter.', 'EpisodeOfCare': 'contains continuous period of engagement by a care manager and/or a care management organization with the patient. Every row indicates a unique episide of care for a patient. One patient may have multiple episodes of care ', 'RiskScore': 'Contains the health risk scores of each of the patients. Only the latest risk score is stored. Every row has risk score of an unique patient', 'patient_sdoh_scores': 'table stores the various social determinants of quality scores about a patient obtained through assessment. Each row indicate score about one patient and about one type of assessment'}`` I am providing you the table structure as a dictionary. For this dictionary, table names are the keys. Values within this dictionary are other dictionaries (nested dictionaries). In each nested dictionary, the keys are the field names and the values are dictionaries where each key is the column name and each value is the datatype. There may be multiple table structures described here. The table structure is enclosed in triple backticks. Table Structures: ```{'Patient': {'identifier_value': ['patient identifier that uniquely identifies patient and links a patient from this to other tables', 'varchar'], 'identifier_use': ['if the identifier is used for any specific purpose', 'varchar'], 'identifier_type': ["type of identifier, ususally means the source, MR' stands for medical record", 'varchar'], 'identifier_start_date': ['date on since when the identifier was valid', 'date'], 'identifier_assigner': ['Identification value assignment authority', 'varchar'], 'active': ['if he patient is active or not', 'boolean'], 'official_name_family': ['family name of the patient', 'varchar'], 'official_name_given': ['given name of the patient', 'varchar'], 'usual_name_given': ['Short form of the given name', 'varchar'], 'gender': ["patient's gender, male or female", 'varchar'], 'birth_date': ['date of birth of the patient', 'date'], 'Age': ['patient age', 'integer'], 'home_address_line': ["patient's home address street", 'varchar'], 'home_address_city': ["patient's home address city", 'varchar'], 'home_address_district': ["patient's home county", 'varchar'], 'home_address_state': ["patient's home state", 'varchar'], 'home_address_postalCode': ["patient's home address zip code", 'varchar'], 'home_address_period_start': ["start date of the patient's home address", 'date']}, 'Encounter': {'id': ['encounter id that identifies an encounter uniquely', 'varchar'], 'status': ["encounter status, can be one of 'planned', ''completed', 'discharged', 'in-progress' ", 'varchar'], 'class': ["indicates location setting of the encounter, valid values are: 'IMP' as inpatient, 'EMER' as emergency, 'AMB' as ambulatory, 'HH' as home health ", 'varchar'], 'priority': ["indicates priority of the encounter, valid values are: 'UR' as urgent, 'A' as As soon as, 'S' as stat, 'R' as routine ", 'varchar'], 'subject_id': ['indicates id of the patient associated with the encounter, should match with identifier_value of the Patient table', 'varchar'], 'service_provider_id': ['contains the id of the care delivery organization where the patient had the encounter', 'varchar'], 'participant_actor_id': ['contains the id of the provider associated with the care delivery organization who rendered the encounter', 'varchar'], 'diagnosis_condition_id': ['contains list of diagnosis codes relevant to the patient of the encounter', 'varchar'], 'location_id': ['location where the encounter happend or is happening or will be happening', 'varchar'], 'discharge_disposition': ['how the patient was discharged at the end of the encounter', 'varchar'], 'diagnosis_condition_text': ['clinical description of the diagnosis codes', 'varchar'], 'condition_class': ['condition of the patient classified into specific broad classe., may contain multiple coditions. All lower case.', 'varchar']}, 'EpisodeOfCare': {'identifier_value': ['unique identifier of the episode', 'varchar'], 'type': ['type of episode, can be disease management, post acute care or specialist referral', 'varchar'], 'diagnosis_condition_id': ['ICD-10 diagnosis code assiciated with the episode of care', 'varchar'], 'subject_id': ["id of the patient associated with episode, should have a corresponding 'identifier_value' in the Patient table", 'varchar'], 'managing_organization_id': ['contains the id of the organization managing the episode', 'varchar'], 'care_manager_id': ['contains the id of the care manager managing the episode', 'varchar'], 'care_team_id': ['contains the id of the care team managing the episode. Care manager is part of the care team', 'varchar']}, 'RiskScore': {'patient_id': ['identifier that uniquely identifies a patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'risk_score': ['decimal number between 0 and 1 indicating the risk score', 'decimal number']}, 'patient_sdoh_scores': {'Patient_Id': ['unique identifier of the patient. Matches with at least one identifier_value of Patient table.', 'varchar'], 'Assessment_Id': ['name of the assessment', 'varchar'], 'Answer': ['The actual answer provided in the assessment', 'integer'], 'Assessment_Type': ["type of the assessment, can be 'Financial', 'Home', 'Food' and 'Physical'", 'varchar'], 'score': ['Derived standardized score based on the answer provided', 'decimal number']}}``` Pay special attention to the field names. Some field names have an underscore ('_') and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. This is the English language query that needs to be converted into an SQL Query within four backticks. English language query: ````get all the patients```` Your task is to generate an SQL query that can be executed on a SQLite database. Only produce the SQL query as a string. Do NOT produce any backticks before or after. Do NOT produce any JSON tags. Do NOT produce any additional text that is not part of the query itself. 2025-02-25 10:53:25 INFO Tokens consumed: 1487 2025-02-25 10:54:06 INFO Existing token_consumed found for month: 2025-02 2025-02-25 10:54:10 INFO token updated successfully: 2025-02 2025-02-25 10:54:10 INFO token updated successfully. 2025-02-25 10:54:10 INFO Query executed successfully. 2025-02-25 10:54:12 INFO Latest file number in generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: 5 2025-02-25 10:54:17 INFO Blob exists check for generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 10:54:22 INFO SQL query blob saved successfully: generated_sql/b4189428-c0e1-70b5-967d-898b0d807f03/6.json 2025-02-25 10:57:06 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:14 INFO Query executed successfully. 2025-02-25 10:57:14 INFO Dataset columns displayed using AG Grid. 2025-02-25 10:57:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:57:52 INFO Query executed successfully. 2025-02-25 10:57:52 INFO Dataset columns displayed using AG Grid. 2025-02-25 10:57:52 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 10:57:59 INFO Tokens consumed: 1025 2025-02-25 10:58:25 INFO Existing token_consumed found for month: 2025-02 2025-02-25 10:58:27 INFO token updated successfully: 2025-02 2025-02-25 10:58:27 INFO token updated successfully. 2025-02-25 10:58:29 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 30 2025-02-25 10:58:36 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 10:58:37 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/31.py 2025-02-25 10:58:37 INFO Code generated and written in generated_method//30.py 2025-02-25 10:58:37 INFO Insight generated and displayed using AG Grid. 2025-02-25 10:58:56 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:59:00 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:59:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 10:59:01 INFO Query executed successfully. 2025-02-25 10:59:01 INFO Dataset columns displayed using AG Grid. 2025-02-25 10:59:01 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 10:59:08 INFO Tokens consumed: 1024 2025-02-25 10:59:10 INFO Existing token_consumed found for month: 2025-02 2025-02-25 10:59:11 INFO token updated successfully: 2025-02 2025-02-25 10:59:11 INFO token updated successfully. 2025-02-25 10:59:27 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 31 2025-02-25 10:59:29 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 10:59:30 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/32.py 2025-02-25 10:59:30 INFO Code generated and written in generated_method//31.py 2025-02-25 10:59:30 INFO Insight generated and displayed using AG Grid. 2025-02-25 11:00:41 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:00:41 INFO Insight list generated successfully. 2025-02-25 11:00:46 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:00:46 INFO Insight list generated successfully. 2025-02-25 11:01:00 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:01:00 INFO Query executed successfully. 2025-02-25 11:01:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:01:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:01:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:01:57 INFO Query executed successfully. 2025-02-25 11:01:57 INFO Dataset columns displayed using AG Grid. 2025-02-25 11:02:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:02:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:02:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:02:39 INFO Query executed successfully. 2025-02-25 11:02:39 INFO Dataset columns displayed using AG Grid. 2025-02-25 11:02:40 INFO Existing insight found for base code: %s 2025-02-25 11:02:44 INFO Insight updated successfully: %s 2025-02-25 11:02:44 INFO Insight updated successfully. 2025-02-25 11:14:08 INFO Date: 2025-02-25 ======================================== Time: 11:14:08 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 11:14:13 INFO not logined 2025-02-25 11:14:13 INFO Rendering unauthenticated menu. 2025-02-25 11:14:29 INFO Login button clicked. 2025-02-25 11:14:34 INFO Login successful for user: maheshsr 2025-02-25 11:18:36 INFO Date: 2025-02-25 ======================================== Time: 11:18:36 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 11:18:42 INFO not logined 2025-02-25 11:18:42 INFO Rendering unauthenticated menu. 2025-02-25 11:20:41 INFO Login button clicked. 2025-02-25 11:20:46 INFO Login successful for user: maheshsr 2025-02-25 11:22:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:23:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:23:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:23:35 INFO Query executed successfully. 2025-02-25 11:25:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:26:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:28:12 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:28:17 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:28:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:28:18 INFO Query executed successfully. 2025-02-25 11:30:44 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:30:44 INFO Insight list generated successfully. 2025-02-25 11:30:50 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:30:50 INFO Insight list generated successfully. 2025-02-25 11:30:51 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 11:30:51 INFO Query executed successfully. 2025-02-25 15:34:09 INFO Date: 2025-02-25 ======================================== Time: 15:34:09 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 15:34:14 INFO not logined 2025-02-25 15:34:14 INFO Rendering unauthenticated menu. 2025-02-25 15:39:50 INFO Login button clicked. 2025-02-25 15:39:54 INFO Login successful for user: maheshsr 2025-02-25 15:40:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:43:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:43:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:43:35 INFO Query executed successfully. 2025-02-25 15:43:35 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:44:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:44:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:44:58 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:44:58 INFO Query executed successfully. 2025-02-25 15:44:58 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:44:59 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 15:45:04 INFO Tokens consumed: 1024 2025-02-25 15:45:06 INFO Existing token_consumed found for month: 2025-02 2025-02-25 15:45:08 INFO token updated successfully: 2025-02 2025-02-25 15:45:08 INFO token updated successfully. 2025-02-25 15:45:10 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 32 2025-02-25 15:45:11 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 15:45:13 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/33.py 2025-02-25 15:45:13 INFO Code generated and written in generated_method//32.py 2025-02-25 15:45:13 ERROR Error executing the query: %s 2025-02-25 15:47:08 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:47:09 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:47:09 INFO Query executed successfully. 2025-02-25 15:47:09 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:47:09 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create an insight of patient whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 15:47:13 INFO Tokens consumed: 1024 2025-02-25 15:47:17 INFO Existing token_consumed found for month: 2025-02 2025-02-25 15:47:18 INFO token updated successfully: 2025-02 2025-02-25 15:47:18 INFO token updated successfully. 2025-02-25 15:47:20 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 33 2025-02-25 15:47:22 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 15:47:23 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/34.py 2025-02-25 15:47:23 INFO Code generated and written in generated_method//33.py 2025-02-25 15:47:23 INFO Insight generated and displayed using AG Grid. 2025-02-25 15:49:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:49:21 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:49:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:49:22 INFO Query executed successfully. 2025-02-25 15:49:22 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:49:22 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph the patient with age groups``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-25 15:49:32 INFO Tokens consumed: 2107 2025-02-25 15:49:35 INFO Existing token_consumed found for month: 2025-02 2025-02-25 15:49:36 INFO token updated successfully: 2025-02 2025-02-25 15:49:36 INFO token updated successfully. 2025-02-25 15:49:40 INFO Plotly chart object created successfully. 2025-02-25 15:49:40 INFO Graph generated and displayed using Plotly. 2025-02-25 15:54:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:54:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:54:41 INFO Query executed successfully. 2025-02-25 15:54:41 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:54:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:54:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:54:49 INFO Query executed successfully. 2025-02-25 15:54:49 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:54:50 INFO Existing insight found for base code: %s 2025-02-25 15:54:52 INFO Insight updated successfully: %s 2025-02-25 15:54:52 INFO Insight updated successfully. 2025-02-25 15:55:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:55:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:55:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:55:15 INFO Query executed successfully. 2025-02-25 15:55:15 INFO Dataset columns displayed using AG Grid. 2025-02-25 15:55:15 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a scattered graph the patient with age groups``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-25 15:55:27 INFO Tokens consumed: 2009 2025-02-25 15:55:30 INFO Existing token_consumed found for month: 2025-02 2025-02-25 15:55:32 INFO token updated successfully: 2025-02 2025-02-25 15:55:32 INFO token updated successfully. 2025-02-25 15:55:32 INFO Plotly chart object created successfully. 2025-02-25 15:55:32 INFO Graph generated and displayed using Plotly. 2025-02-25 15:56:25 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:25 INFO Insight list generated successfully. 2025-02-25 15:56:30 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:30 INFO Insight list generated successfully. 2025-02-25 15:56:32 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:32 INFO Query executed successfully. 2025-02-25 15:56:45 INFO Insight list generated successfully. 2025-02-25 15:56:48 INFO Insight list generated successfully. 2025-02-25 15:56:53 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:53 INFO Insight list generated successfully. 2025-02-25 15:56:58 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:58 INFO Insight list generated successfully. 2025-02-25 15:56:59 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 15:56:59 INFO Query executed successfully. 2025-02-25 16:01:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:22 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:23 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:23 INFO Query executed successfully. 2025-02-25 16:01:23 INFO Dataset columns displayed using AG Grid. 2025-02-25 16:01:33 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:34 INFO Query executed successfully. 2025-02-25 16:01:34 INFO Dataset columns displayed using AG Grid. 2025-02-25 16:01:42 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 16:01:44 INFO Query executed successfully. 2025-02-25 16:01:44 INFO Dataset columns displayed using AG Grid. 2025-02-25 16:01:45 INFO Existing insight found for base code: %s 2025-02-25 16:01:47 INFO Insight updated successfully: %s 2025-02-25 16:01:47 INFO Insight updated successfully. 2025-02-25 16:08:55 INFO Date: 2025-02-25 ======================================== Time: 16:08:55 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 16:09:00 INFO not logined 2025-02-25 16:09:00 INFO Rendering unauthenticated menu. 2025-02-25 16:09:37 INFO Login button clicked. 2025-02-25 16:09:41 INFO Login successful for user: maheshsr 2025-02-25 16:10:43 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:10:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:10:32 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:10:32 INFO Query executed successfully. 2025-02-25 17:10:32 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:13:41 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:13:41 INFO Query executed successfully. 2025-02-25 17:13:41 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:13:41 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 60`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:13:45 INFO Tokens consumed: 1027 2025-02-25 17:13:47 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:13:49 INFO token updated successfully: 2025-02 2025-02-25 17:13:49 INFO token updated successfully. 2025-02-25 17:13:51 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 34 2025-02-25 17:13:53 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:13:54 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/35.py 2025-02-25 17:13:54 INFO Code generated and written in generated_method//34.py 2025-02-25 17:13:54 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:15:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:15:44 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:15:45 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:15:45 INFO Query executed successfully. 2025-02-25 17:15:45 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:15:45 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient based on the age groups``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-25 17:15:55 INFO Tokens consumed: 2124 2025-02-25 17:15:58 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:15:59 INFO token updated successfully: 2025-02 2025-02-25 17:15:59 INFO token updated successfully. 2025-02-25 17:16:03 INFO Plotly chart object created successfully. 2025-02-25 17:16:03 INFO Graph generated and displayed using Plotly. 2025-02-25 17:16:30 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:16:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:16:31 INFO Query executed successfully. 2025-02-25 17:16:31 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:16:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:16:37 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:16:37 INFO Query executed successfully. 2025-02-25 17:16:37 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:16:38 INFO Existing insight found for base code: %s 2025-02-25 17:16:40 INFO Insight updated successfully: %s 2025-02-25 17:16:40 INFO Insight updated successfully. 2025-02-25 17:17:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:04 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:05 INFO Query executed successfully. 2025-02-25 17:17:05 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:17:05 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:17:12 INFO Tokens consumed: 1027 2025-02-25 17:17:14 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:17:15 INFO token updated successfully: 2025-02 2025-02-25 17:17:15 INFO token updated successfully. 2025-02-25 17:17:18 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 35 2025-02-25 17:17:20 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:17:21 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/36.py 2025-02-25 17:17:21 INFO Code generated and written in generated_method//35.py 2025-02-25 17:17:21 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:17:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:35 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:35 INFO Query executed successfully. 2025-02-25 17:17:35 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:17:35 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a data set of patients whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:17:40 INFO Tokens consumed: 1026 2025-02-25 17:17:42 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:17:44 INFO token updated successfully: 2025-02 2025-02-25 17:17:44 INFO token updated successfully. 2025-02-25 17:17:49 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:49 INFO Insight list generated successfully. 2025-02-25 17:17:56 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:56 INFO Insight list generated successfully. 2025-02-25 17:17:57 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:17:57 INFO Query executed successfully. 2025-02-25 17:17:57 ERROR Error executing generated insight code: AttributeError("'NoneType' object has no attribute 'to_df'") 2025-02-25 17:26:27 INFO Date: 2025-02-25 ======================================== Time: 17:26:27 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 17:26:31 INFO not logined 2025-02-25 17:26:31 INFO Rendering unauthenticated menu. 2025-02-25 17:26:49 INFO Login button clicked. 2025-02-25 17:26:53 INFO Login successful for user: maheshsr 2025-02-25 17:27:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:28:26 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:28:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:28:28 INFO Query executed successfully. 2025-02-25 17:28:28 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:29:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:29:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:29:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:29:15 INFO Query executed successfully. 2025-02-25 17:29:15 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:29:15 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 65`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:29:20 INFO Tokens consumed: 1024 2025-02-25 17:29:23 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:29:24 INFO token updated successfully: 2025-02 2025-02-25 17:29:24 INFO token updated successfully. 2025-02-25 17:29:26 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 36 2025-02-25 17:29:28 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:29:30 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/37.py 2025-02-25 17:29:30 INFO Code generated and written in generated_method//36.py 2025-02-25 17:29:30 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:30:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:30:11 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:30:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:30:13 INFO Query executed successfully. 2025-02-25 17:30:13 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:30:13 INFO Generating graph with prompt: You are an expert in understanding English language instructions to generate a graph based on a given dataframe. I am providing you the dataframe structure as a dictionary in double backticks. Dataframe structure: `` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string`` I am also providing you a summary of the dataframe as a dictionary in double backticks. Dataframe summary: ``{'columns': ['id', 'identifier_value', 'identifier_use', 'identifier_type', 'identifier_start_date', 'identifier_assigner', 'active', 'official_name_family', 'official_name_given', 'usual_name_given', 'gender', 'birth_date', 'Age', 'home_address_line', 'home_address_city', 'home_address_district', 'home_address_state', 'home_address_postalCode', 'home_address_period_start'], 'dtypes': {'id': 'object', 'identifier_value': 'object', 'identifier_use': 'object', 'identifier_type': 'object', 'identifier_start_date': 'object', 'identifier_assigner': 'object', 'active': 'int64', 'official_name_family': 'object', 'official_name_given': 'object', 'usual_name_given': 'object', 'gender': 'object', 'birth_date': 'object', 'Age': 'int64', 'home_address_line': 'object', 'home_address_city': 'object', 'home_address_district': 'object', 'home_address_state': 'object', 'home_address_postalCode': 'int64', 'home_address_period_start': 'object'}, 'describe': {'active': {'count': 40.0, 'mean': 1.0, 'std': 0.0, 'min': 1.0, '25%': 1.0, '50%': 1.0, '75%': 1.0, 'max': 1.0}, 'Age': {'count': 40.0, 'mean': 65.0, 'std': 6.084869844593311, 'min': 54.0, '25%': 61.25, '50%': 66.0, '75%': 70.0, 'max': 74.0}, 'home_address_postalCode': {'count': 40.0, 'mean': 12521.8, 'std': 1568.5528394849855, 'min': 10001.0, '25%': 10701.75, '50%': 12751.5, '75%': 13901.25, 'max': 14605.0}}}`` I have provided the dataframe structure and its summary. I can't provide the entire dataframe. I am also giving you the intent instruction in triple backticks. Instruction for generating the graph: ```generate a bar graph of patient based on age group``` Your task is to write the code that will generate a Plotly chart. You should be able to derive the chart type from the instruction. Graphs may need calculations, such as aggregating or calculating averages for some of the numeric columns. You should generate the code that will allow me to create the Plotly chart object that can then be used as the parameter in Streamlit's `st.plotly_chart()` method. Pay special attention to the field names. Some field names have an underscore (_) and some do not. You need to be accurate while generating the query. Pay special attention when you need to group by based on two categorical columns to create things like bubble charts. For example, the sample code within four backticks below is the correct way to prepare a dataframe with procedure code, a categorical variable in one axis, and diagnosis code, another categorical variable in another axis, and the size of the bubble would be based on the sum of 'Total Paid' values for each procedure and diagnosis code combination. Sample code: ````grouped_df = df_ma.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index()```` If you need to add a filter criterion, then you need to add a second step as indicated in five backticks below. This shows it is filtering the dataframe for all groups with a sum of 'Total Paid' more than 1000. You can feed the last dataframe to the Plotly chart. Sample code: `````grouped_df = df.groupby(['Procedure Code', 'Diagnosis Codes'])['Total Paid'].sum().reset_index() \n\nfiltered_df = grouped_df[grouped_df['Total Paid'] > 1000]````` If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. While creating the Plotly chart, you need to get the top 5000 rows since Plotly chart cannot handle more than 5000 rows. Pay special attention to grouped bar charts. For grouped bar charts, there should be at least two x-axis columns. One can be the actual x-axis and the other can be used in the 'column' parameter of the Plotly Chart object. For example, the following code in four backticks shows a grouped bar chart with the x-axis showing 'year' and each 'site' for each year. Grouped bar chart sample code: ````alt.Chart(source).mark_bar().encode( x='year:O', y='sum(yield):Q', column='site:N' )```` A grouped bar chart will be explicitly asked for in the instructions. Only produce the Python code. Do NOT produce any backticks or double quotes or single quotes before or after the code. Do generate the Plotly import statement as part of the code. Do NOT justify your code. Do not generate any narrative or comments in the code. Do NOT produce any JSON tags. Do not print or return the chart object at the end. Do NOT produce any additional text that is not part of the query itself. Always name the final Plotly chart object as 'chart'. Go back and check if the generated code can be used in the `st.plotly_chart()` method. 2025-02-25 17:30:18 INFO Tokens consumed: 1694 2025-02-25 17:30:20 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:30:23 INFO token updated successfully: 2025-02 2025-02-25 17:30:23 INFO token updated successfully. 2025-02-25 17:30:26 INFO Plotly chart object created successfully. 2025-02-25 17:30:27 INFO Graph generated and displayed using Plotly. 2025-02-25 17:32:13 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:15 INFO Query executed successfully. 2025-02-25 17:32:15 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:32:16 INFO Existing insight found for base code: %s 2025-02-25 17:32:18 INFO Insight updated successfully: %s 2025-02-25 17:32:18 INFO Insight updated successfully. 2025-02-25 17:32:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:28 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:29 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:29 INFO Query executed successfully. 2025-02-25 17:32:29 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:32:29 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 70`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:32:33 INFO Tokens consumed: 1025 2025-02-25 17:32:36 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:32:38 INFO token updated successfully: 2025-02 2025-02-25 17:32:38 INFO token updated successfully. 2025-02-25 17:32:39 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 37 2025-02-25 17:32:42 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:32:43 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/38.py 2025-02-25 17:32:43 INFO Code generated and written in generated_method//37.py 2025-02-25 17:32:43 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:32:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:53 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:32:53 INFO Query executed successfully. 2025-02-25 17:32:53 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:32:53 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 55`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:32:57 INFO Tokens consumed: 1024 2025-02-25 17:32:59 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:33:00 INFO token updated successfully: 2025-02 2025-02-25 17:33:00 INFO token updated successfully. 2025-02-25 17:33:02 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 38 2025-02-25 17:33:04 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:33:06 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/39.py 2025-02-25 17:33:06 INFO Code generated and written in generated_method//38.py 2025-02-25 17:33:06 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:33:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:14 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:15 INFO Query executed successfully. 2025-02-25 17:33:15 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:33:15 INFO Generating insight with prompt: You are an expert in understanding an english langauge task and write python script that, when executed, provide correect answer by analyzing a python dataframe. I am providing the english language task in double backticks Task: ``create a dataset of patient whose age is above 65`` I am providing you the dataframe structure as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe structure is enclosed in triple backticks. Dataframe Structures: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` I am providing you the dataframe as a dictionary. For this dictionary, column names are the keys and values are the datatypes of each column. The dataframe is enclosed in triple backticks. Dataframe: ``` Column Dtype 0 id string 1 identifier_value string 2 identifier_use string 3 identifier_type string 4 identifier_start_date string 5 identifier_assigner string 6 active int64 7 official_name_family string 8 official_name_given string 9 usual_name_given string 10 gender string 11 birth_date string 12 Age int64 13 home_address_line string 14 home_address_city string 15 home_address_district string 16 home_address_state string 17 home_address_postalCode int64 18 home_address_period_start string``` You are required to create a python script that will manipulate a dataframe named 'mydf' and generate output that satisfies the task. Put the final result in a dictionary called output. The output dictionary should have only one key called 'result_df' and the value of that key will be output dataframe. Do not define an empty output dictionary as it will be already defined outside the generated code. Only keep the relevant columns in the final output df, do not put unnecessary columns that are not needed for the task. Pay special attention to the field names. Some field names have an '_' and some do not. You need to be accurate while generating the query. If there is a space in the column name, then you need to fully enclose each occurrence of the column name with double quotes in the query. Put the given task as a comment line in the first line of the code generated. Do not generate a method, but generate only script. Your task is to generate python code that can be executed. Do NOT produce any backticks before or after. Do NOT produce any narrative or justification before or after the code Do NOT produce any additional text that is not part of the python code of the method itself. You must give a new line character before every actual line of code. The script you produced must be able to run on a Python runtime. Go back and check if the generated code can be run within a python runtime. Go back and check to make sure you have not produced any narrative or justification before or after the code. Go back and check to make sure you have not enclosed the code in triple backticks.this is the prompt i want to the prompt to generate the duck db query for the df for example duckdb.query("select * from mydf").to_df() like this i want run that perticular query 2025-02-25 17:33:19 INFO Tokens consumed: 1024 2025-02-25 17:33:21 INFO Existing token_consumed found for month: 2025-02 2025-02-25 17:33:23 INFO token updated successfully: 2025-02 2025-02-25 17:33:23 INFO token updated successfully. 2025-02-25 17:33:25 INFO Latest file number in generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: 39 2025-02-25 17:33:27 INFO Blob exists check for generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/: True 2025-02-25 17:33:29 INFO Python method blob saved successfully: generated_method/b4189428-c0e1-70b5-967d-898b0d807f03/40.py 2025-02-25 17:33:29 INFO Code generated and written in generated_method//39.py 2025-02-25 17:33:29 INFO Insight generated and displayed using AG Grid. 2025-02-25 17:33:45 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:45 INFO Insight list generated successfully. 2025-02-25 17:33:53 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:53 INFO Insight list generated successfully. 2025-02-25 17:33:54 INFO Blob content retrieved successfully from: insight_library/SDoH Specialist/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:33:54 INFO Query executed successfully. 2025-02-25 17:33:55 ERROR Error executing generated insight code: AttributeError("'NoneType' object has no attribute 'to_df'") 2025-02-25 17:34:55 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:34:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:35:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:35:01 INFO Query executed successfully. 2025-02-25 17:35:01 INFO Dataset columns displayed using AG Grid. 2025-02-25 17:36:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:37:39 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:37:47 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:37:52 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:37:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:38:01 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:38:04 INFO Date: 2025-02-25 ======================================== Time: 17:38:04 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 17:38:04 INFO Date: 2025-02-25 ======================================== Time: 17:38:04 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 17:38:04 INFO not logined 2025-02-25 17:38:04 INFO Rendering unauthenticated menu. 2025-02-25 17:38:04 INFO Rendering unauthenticated menu. 2025-02-25 17:38:27 INFO Login button clicked. 2025-02-25 17:38:27 INFO Login button clicked. 2025-02-25 17:38:31 INFO Login successful for user: maheshsr 2025-02-25 17:38:31 INFO Login successful for user: maheshsr 2025-02-25 17:40:29 INFO Date: 2025-02-25 ======================================== Time: 17:40:29 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-25 17:40:33 INFO not logined 2025-02-25 17:40:33 INFO Rendering unauthenticated menu. 2025-02-25 17:40:51 INFO Login button clicked. 2025-02-25 17:40:55 INFO Login successful for user: maheshsr 2025-02-25 17:42:05 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:43:18 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:43:19 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-25 17:43:19 INFO Query executed successfully. 2025-02-26 07:25:05 INFO Date: 2025-02-26 ======================================== Time: 07:25:05 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:25:10 INFO not logined 2025-02-26 07:25:10 INFO Rendering unauthenticated menu. 2025-02-26 07:25:24 INFO Login button clicked. 2025-02-26 07:25:29 INFO Login successful for user: mahesh 2025-02-26 07:26:31 INFO User logged out. 2025-02-26 07:26:31 INFO Date: 2025-02-26 ======================================== Time: 07:26:31 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:26:31 INFO Date: 2025-02-26 ======================================== Time: 07:26:31 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:26:31 INFO not logined 2025-02-26 07:26:31 INFO not logined 2025-02-26 07:26:31 INFO Rendering unauthenticated menu. 2025-02-26 07:26:31 INFO Rendering unauthenticated menu. 2025-02-26 07:26:46 INFO Login button clicked. 2025-02-26 07:26:46 INFO Login button clicked. 2025-02-26 07:26:50 INFO Login successful for user: mahesh 2025-02-26 07:26:50 INFO Login successful for user: mahesh 2025-02-26 07:27:52 INFO Date: 2025-02-26 ======================================== Time: 07:27:52 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:27:52 INFO Date: 2025-02-26 ======================================== Time: 07:27:52 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:27:52 INFO Date: 2025-02-26 ======================================== Time: 07:27:52 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:27:52 INFO not logined 2025-02-26 07:27:52 INFO not logined 2025-02-26 07:27:52 INFO not logined 2025-02-26 07:27:52 INFO Rendering unauthenticated menu. 2025-02-26 07:27:52 INFO Rendering unauthenticated menu. 2025-02-26 07:27:52 INFO Rendering unauthenticated menu. 2025-02-26 07:28:11 INFO Login button clicked. 2025-02-26 07:28:11 INFO Login button clicked. 2025-02-26 07:28:17 INFO Login successful for user: Mahesh 2025-02-26 07:28:17 INFO Login successful for user: Mahesh 2025-02-26 07:28:17 INFO Login successful for user: Mahesh 2025-02-26 07:35:44 INFO Date: 2025-02-26 ======================================== Time: 07:35:44 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:35:49 INFO not logined 2025-02-26 07:35:49 INFO Rendering unauthenticated menu. 2025-02-26 07:36:14 INFO Login button clicked. 2025-02-26 07:36:19 INFO Login successful for user: maheshsr 2025-02-26 07:38:20 INFO User logged out. 2025-02-26 07:38:21 INFO Date: 2025-02-26 ======================================== Time: 07:38:21 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:38:21 INFO Date: 2025-02-26 ======================================== Time: 07:38:21 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:38:21 INFO not logined 2025-02-26 07:38:21 INFO not logined 2025-02-26 07:38:21 INFO Rendering unauthenticated menu. 2025-02-26 07:38:21 INFO Rendering unauthenticated menu. 2025-02-26 07:38:25 INFO Login button clicked. 2025-02-26 07:38:25 INFO Login button clicked. 2025-02-26 07:38:29 INFO Login successful for user: maheshsr 2025-02-26 07:38:29 INFO Login successful for user: maheshsr 2025-02-26 07:53:01 INFO Date: 2025-02-26 ======================================== Time: 07:53:01 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 07:53:09 INFO not logined 2025-02-26 07:53:09 INFO Rendering unauthenticated menu. 2025-02-26 08:04:21 INFO Date: 2025-02-26 ======================================== Time: 08:04:21 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:04:26 INFO not logined 2025-02-26 08:04:26 INFO Rendering unauthenticated menu. 2025-02-26 08:06:16 INFO Login button clicked. 2025-02-26 08:06:20 INFO Login successful for user: maheshsr 2025-02-26 08:07:40 INFO User logged out. 2025-02-26 08:07:41 INFO Date: 2025-02-26 ======================================== Time: 08:07:41 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:07:41 INFO Date: 2025-02-26 ======================================== Time: 08:07:41 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:07:41 INFO not logined 2025-02-26 08:07:41 INFO not logined 2025-02-26 08:07:41 INFO Rendering unauthenticated menu. 2025-02-26 08:07:46 INFO Login button clicked. 2025-02-26 08:07:46 INFO Login button clicked. 2025-02-26 08:07:50 INFO Login successful for user: maheshsr 2025-02-26 08:07:50 INFO Login successful for user: maheshsr 2025-02-26 08:08:11 INFO User logged out. 2025-02-26 08:08:11 INFO User logged out. 2025-02-26 08:08:11 INFO Date: 2025-02-26 ======================================== Time: 08:08:11 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:08:11 INFO Date: 2025-02-26 ======================================== Time: 08:08:11 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:08:11 INFO Date: 2025-02-26 ======================================== Time: 08:08:11 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:08:11 INFO not logined 2025-02-26 08:08:11 INFO not logined 2025-02-26 08:08:11 INFO not logined 2025-02-26 08:08:11 INFO Rendering unauthenticated menu. 2025-02-26 08:08:11 INFO Rendering unauthenticated menu. 2025-02-26 08:08:11 INFO Rendering unauthenticated menu. 2025-02-26 08:08:25 INFO Login button clicked. 2025-02-26 08:08:25 INFO Login button clicked. 2025-02-26 08:08:25 INFO Login button clicked. 2025-02-26 08:08:28 INFO Login successful for user: maheshsr 2025-02-26 08:08:28 INFO Login successful for user: maheshsr 2025-02-26 08:08:28 INFO Login successful for user: maheshsr 2025-02-26 08:09:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:09:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:09:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:49 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:51 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:12:51 INFO Query executed successfully. 2025-02-26 08:12:51 INFO Query executed successfully. 2025-02-26 08:12:51 INFO Query executed successfully. 2025-02-26 08:13:23 INFO User logged out. 2025-02-26 08:13:23 INFO User logged out. 2025-02-26 08:13:23 INFO User logged out. 2025-02-26 08:13:23 INFO Date: 2025-02-26 ======================================== Time: 08:13:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:13:23 INFO Date: 2025-02-26 ======================================== Time: 08:13:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:13:23 INFO Date: 2025-02-26 ======================================== Time: 08:13:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:13:23 INFO Date: 2025-02-26 ======================================== Time: 08:13:23 Logger Data: This is Insight's lab log data. ---------------------------------------- 2025-02-26 08:13:23 INFO not logined 2025-02-26 08:13:23 INFO not logined 2025-02-26 08:13:23 INFO not logined 2025-02-26 08:13:23 INFO not logined 2025-02-26 08:13:23 INFO Rendering unauthenticated menu. 2025-02-26 08:13:23 INFO Rendering unauthenticated menu. 2025-02-26 08:13:23 INFO Rendering unauthenticated menu. 2025-02-26 08:13:23 INFO Rendering unauthenticated menu. 2025-02-26 08:13:28 INFO Login button clicked. 2025-02-26 08:13:28 INFO Login button clicked. 2025-02-26 08:13:28 INFO Login button clicked. 2025-02-26 08:13:28 INFO Login button clicked. 2025-02-26 08:13:32 INFO Login successful for user: maheshsr 2025-02-26 08:13:32 INFO Login successful for user: maheshsr 2025-02-26 08:13:32 INFO Login successful for user: maheshsr 2025-02-26 08:13:32 INFO Login successful for user: maheshsr 2025-02-26 08:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:40 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:57 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:59 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:13:59 INFO Query executed successfully. 2025-02-26 08:13:59 INFO Query executed successfully. 2025-02-26 08:13:59 INFO Query executed successfully. 2025-02-26 08:13:59 INFO Query executed successfully. 2025-02-26 08:14:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:15 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:31 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:34 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:36 INFO Blob content retrieved successfully from: query_library/b4189428-c0e1-70b5-967d-898b0d807f03/1.json 2025-02-26 08:14:36 INFO Query executed successfully. 2025-02-26 08:14:36 INFO Query executed successfully. 2025-02-26 08:14:36 INFO Query executed successfully. 2025-02-26 08:14:36 INFO Query executed successfully.