feature: check associated deliverable, and revise prompts for better consistancy

src/chatbot/app_interface.py

@@ -59,7 +59,7 @@ def update_total_payment_amount_input(value):
     return form_information
 
 def update_expense_justification_input(value):
-    set_form_info("Expense Justification", value)
+    set_form_info("Expense Description", value)
     form_information = get_form_info()
     return form_information
 
@@ -86,7 +86,7 @@ def update_form_information_handler(form_information):
     if total_payment_amount_input and total_payment_amount_input != "None":
         total_payment_amount_input = float(total_payment_amount_input)
 
-    expense_justification_input = form_information.get("Expense Justification", None)
+    expense_justification_input = form_information.get("Expense Description", None)
     expense_justification_input = None if expense_justification_input == "None" else expense_justification_input
 
     # form_amount_input = form_information.get("Total Payment Amount", None)
@@ -114,8 +114,7 @@ def chat_handler(message, history):
     if len(message['files']) > 0:
         logger.info(f"Received file: {message['files'][0]}")
         response = user_input_handler(str(message['files'][0]), CURRENT_SESSION_ID)
-    
-    if message['text']!="":
+    elif message['text']!="":
         try:
             logger.info(f"Received text: {message['text']}")
             response = user_input_handler(message['text'], CURRENT_SESSION_ID)
@@ -186,7 +185,7 @@ with gr.Blocks() as app:
                 gr.Markdown("บาท")
 
             gr.Markdown("**การพิสูจน์ค่าใช้จ่าย**")
-            expense_justification_input = gr.Textbox(value=ast.literal_eval(form_info["Expense Justification"]), label="", container=False, lines=4)
+            expense_justification_input = gr.Textbox(value=ast.literal_eval(form_info["Expense Description"]), label="", container=False, lines=4)
 
             submit_btn = gr.Button("Submit", variant="primary")
 

src/chatbot/form_management/acceptance_criteria.py

@@ -66,12 +66,21 @@ acceptance_criteria = {
         "exampleInput": "2000"
       },
       {
-        "fieldName": "Expense Justification",
+        "fieldName": "Associated Deliverable",
         "priority": 8,
+        "description": "The deliverable associated with the payment.",
+        "validationRules": [
+          "Cannot be none.",
+        ],
+        "exampleInput": "ค่าจ้างแรงงาน ทำแนวกันไฟในจุดพื้นที่เสี่ยงต่อครั้งที่ 2-8 ทุก 10 วัน"
+      },
+      {
+        "fieldName": "Expense Description",
+        "priority": 9,
         "description": "The expense description pertaining to the project's scope.",
         "validationRules": [
           "Cannot be none.",
-          "Must provide a description for how the expense contributes towards the project."
+          "Must provide a description for how the expense contributes towards the associated deliverable."
         ],
         "exampleInput": "ค่าจ้างแรงงาน ทำแนวกันไฟในจุดพื้นที่เสี่ยงต่อครั้งที่ 2-8 ทุก 10 วัน"
       }

src/chatbot/llm_engine.py

@@ -3,7 +3,11 @@
 import os
 from langchain_openai import ChatOpenAI
 import openai
-from prompts.chat_completion_prompts import feedback_generation_prompt, expense_description_feedback_prompt
+from prompts.chat_completion_prompts import (
+    feedback_generation_prompt, 
+    expense_description_feedback_prompt,
+    map_input_to_project_deliverables_prompt
+)
 from dotenv import load_dotenv
 from datetime import datetime
 
@@ -44,4 +48,16 @@ def generate_expense_description_feedback(project_tasks: dict, expense_descripti
         input=expense_description_feedback_prompt.format(project_tasks, expense_description)
     )
 
+    return response.output_text
+
+def map_input_to_project_deliverable(user_input: str, project_deliverables: str) -> str:
+    """
+    Map user input to project deliverables.
+    """
+    response = client.responses.create(
+        model="gpt-4.1-mini",
+        temperature=0,
+        input=map_input_to_project_deliverables_prompt.format(user_input, project_deliverables)
+    )
+
     return response.output_text

src/chatbot/nodes.py

@@ -8,7 +8,12 @@ from langgraph.prebuilt import create_react_agent, ToolNode
 from langgraph.checkpoint.memory import MemorySaver
 
 # from chatbot.llm_engine import llm_overall_agent, generate_expense_info_feedback
-from llm_engine import llm_overall_agent, generate_expense_info_feedback, generate_expense_description_feedback
+from llm_engine import (
+    llm_overall_agent, 
+    generate_expense_info_feedback, 
+    generate_expense_description_feedback, 
+    map_input_to_project_deliverable
+)
 from form_management.acceptance_criteria import acceptance_criteria
 from form_management.project_task_descriptions import project_tasks
 from prompts.agent_prompts import speaker_system_message
@@ -16,6 +21,10 @@ from form_management.form_management import Form
 from kie import receipt_kie
 import ast
 import json
+import sqlite3
+
+conn = sqlite3.connect(r'C:\Users\Gavin\Desktop\hello-earth\db\project_data.db')
+cursor = conn.cursor()
 
 # placeholder, init empty form info
 form_info = {
@@ -31,9 +40,16 @@ form_info = {
         #     "Row Entries": [],
         # },
         # "Incompleteness Description": 'None',
-        "Expense Justification": 'None',
+        "Associated Deliverable": 'None',
+        "Expense Description": 'None',
     }
 
+# define the project_deliverables from the db
+cursor.execute("SELECT title FROM deliverables")
+rows = cursor.fetchall()
+deliverable_titles = [row[0] for row in rows]
+print(f"deliverable_titles: {deliverable_titles}")
+
 def get_form_info():
     return form_info
 
@@ -76,16 +92,16 @@ def apply_ocr(image_path: str) -> dict:
  
     feedback = auditor_feedback(form_info)
 
-    print("feedback: {}".format(feedback))
+    print("feedback: {} end of feedback".format(feedback))
 
     return f"""
-    <OCR_Extraction>
+    <form_status>
     {form_info}
-    </OCR_Extraction>
+    </form_status>
 
-    <Feedback>
+    <feedback>
     {feedback}
-    </Feedback>
+    </feedback>
     """
 
 @tool
@@ -103,19 +119,29 @@ def edit_form(key: str, value: str) -> dict:
         form_info[key] = value
         print(form_info)
         print(f"Updated {key} to {value}")
-        if key == "Expense Justification":
-            feedback = exp_desc_feedback(value)
+        if key == "Expense Description":
+            feedback = expense_description_feedback(value)
+        elif key == "Associated Deliverable":
+            res = map_associated_deliverable(value, deliverable_titles) # llm function that maps the user text to a project deliverable
+            if res == "No Matching Deliverable": # if no matching deliverable, 
+                form_info[key] = "None" # set the associated deliverable to None
+                feedback = "A valid associated deliverable is required."
+            else:
+                form_info[key] = res
+                feedback = auditor_feedback(form_info)
         else:
             feedback = auditor_feedback(form_info)
+            
+        print("feedback: {} end of feedback".format(feedback))
         return f"""
-    <OCR_Extraction>
-    {form_info}
-    </OCR_Extraction>
+        <form_status>
+        {form_info}
+        </form_status>
 
-    <Feedback>
-    {feedback}
-    </Feedback>
-    """
+        <feedback>
+        {feedback}
+        </feedback>
+        """
     else:
         print(form_info)
         print(f"Invalid key: {key}. No changes made.")
@@ -128,7 +154,11 @@ def inspect_form() -> dict:
     Use this when you need to address any inquiries the user might have about the current state of the form.
     """
     print("using inspect_form tool")
-    return form_info
+    return """
+    <form_status>
+    {form_info}
+    </form_status>
+    """
 
 tools = [apply_ocr, edit_form, inspect_form]
 
@@ -163,11 +193,17 @@ def speaker_chatbot_node(state: State):
 
 # provides feedback based on form info
 def auditor_feedback(form_info):
+    print("in auditor_feedback...")
     response = generate_expense_info_feedback(acceptance_criteria, form_info)
     return response
 
-def exp_desc_feedback(expense_description):
-    response = generate_expense_description_feedback(project_tasks, expense_description)
+def expense_description_feedback(expense_description):
+    response = generate_expense_description_feedback(form_info["Associated Deliverable"], expense_description)
+    return response
+
+# assume static project deliverables, would need project id to get the deliverables in actual case
+def map_associated_deliverable(user_input, project_deliverables):
+    response = map_input_to_project_deliverable(user_input, str(project_deliverables))
     return response
 
 # # example usage

src/chatbot/prompts/agent_prompts.py

@@ -11,7 +11,8 @@ Buyer Name: The name of the person or entity making the payment.
 Buyer Address: The address of the buyer.
 Transaction Date: The date of the transaction.
 Total Payment Amount: The total amount paid.
-Expense Justification: The justification for the expense as it pertains to the project.
+Associated Deliverable: The deliverable associated with the payment.
+Expense Description: The description of the expense as it pertains to the project.
 
 Users cannot add new fields to the form, but they can edit the existing fields.
 Each time the form is revised, feedback will be provided, and your job to summarize current form information and convey the feedback to the user.

src/chatbot/prompts/chat_completion_prompts.py

@@ -1,5 +1,5 @@
 feedback_generation_prompt = """Provide feedback on the following form information based on the acceptance criteria.
-The acceptance critiria has the following format:
+The acceptance criteria has the following format:
 
 "fieldName": "<the field name>",
 "priority": <the priority number, where 1 is highest>,
@@ -10,9 +10,10 @@ The acceptance critiria has the following format:
 ],
 "exampleInput": "<the example input for the field>"
 
-Your feedback should follow the highest priority field that has violated its rule.
+Determine the highest priority field with a violated rule.
+Provide feedback of that field only.
 Adhere strictly to the rules and provide feedback only for the fields that have violated their rules.
-Only output the field name, the rule that was broken, and an explanation on why the value is invalid in the following format:
+Only output the one field name, the rule that was broken, and an explanation on why the value is invalid in the following format:
 
 Field Name: <Field Name in question>
 Broken Rule: <Rule that was broken>
@@ -22,23 +23,26 @@ If there are no violations, output "Form is complete and valid".
 
 The current date is: {}.
 
-<Acceptance Criteria>
-{}
-</Acceptance Criteria>
-
-<Form Information>
-{}
-</Form Information>
-
+acceptance_criteria: {}
 
+form_information: {}
 """
 
-expense_description_feedback_prompt = """Determine whether the expense description is valid based on the project tasks described.
-Validity is determined by whether the expense description is relevant to the project tasks.
+expense_description_feedback_prompt = """Determine whether the expense description is relevant to the associated project deliverable.
+Validity is determined by whether the expense description is relevant to the associated project deliverable.
 
 If the expense description is relevant, output "Form is complete and valid".
-If the expense description is not relevant, provide your rationale on why it is not relevant.
+If the expense description is not relevant to the associated deliverable, provide your rationale on why it is not relevant.
+If the associated deliverable is empty, output "A valid associated deliverable is required".
 
-Project Tasks: {}
+Associated Deliverable: {}
 Expense Description: {}
+"""
+
+map_input_to_project_deliverables_prompt = """Determine which, if any of the project deliverables the user input is related to.
+If the user input is related to a project deliverable, output the deliverable title.
+If the user input is not related to any project deliverable, output "No Matching Deliverable".
+
+User Input: {}
+Project Deliverables: {}
 """