Improve reliability of AI questions (#48)

- Improve reliability of AI questions (86d8725c407e90b05749218da63c124a439ee008)

functions/chat_functions.py

@@ -13,7 +13,7 @@ def example_question_message(data_source, name, titles, schema):
                          f"""We have a SQLite database with the following {titles}. 
                         We also have an AI agent with access to the same database that will be performing data analysis.
                         Please return an array of seven strings, each one being a question for our data analysis agent
-                        that we can suggest that you believe will be insightful or helpful to a data analysis looking for
+                        that we can suggest that you believe will be insightful or helpful to a data analyst looking for
                         data insights. Return nothing more than the array of questions because I need that specific data structure
                         to process your response. No other response type or data structure will work."""],
 
@@ -21,7 +21,7 @@ def example_question_message(data_source, name, titles, schema):
                  f"""We have a PostgreSQL database with the following tables: {titles}. 
                         We also have an AI agent with access to the same database that will be performing data analysis.
                         Please return an array of seven strings, each one being a question for our data analysis agent
-                        that we can suggest that you believe will be insightful or helpful to a data analysis looking for
+                        that we can suggest that you believe will be insightful or helpful to a data analyst looking for
                         data insights. Return nothing more than the array of questions because I need that specific data structure
                         to process your response. No other response type or data structure will work."""],
 
@@ -30,7 +30,7 @@ def example_question_message(data_source, name, titles, schema):
                         The schema of these collections is: {schema}. 
                         We also have an AI agent with access to the same database that will be performing data analysis.
                         Please return an array of seven strings, each one being a question for our data analysis agent
-                        that we can suggest that you believe will be insightful or helpful to a data analysis looking for
+                        that we can suggest that you believe will be insightful or helpful to a data analyst looking for
                         data insights. Return nothing more than the array of questions because I need that specific data structure
                         to process your response. No other response type or data structure will work."""],
 
@@ -38,7 +38,7 @@ def example_question_message(data_source, name, titles, schema):
                      f"""We have a GraphQL API endpoint with the following types: {titles}.
                         We also have an AI agent with access to the same GraphQL API endpoint that will be performing data analysis.
                         Please return an array of seven strings, each one being a question for our data analysis agent
-                        that we can suggest that you believe will be insightful or helpful to a data analysis looking for
+                        that we can suggest that you believe will be insightful or helpful to a data analyst looking for
                         data insights. Return nothing more than the array of questions because I need that specific data structure
                         to process your response. No other response type or data structure will work."""]         
 
@@ -59,7 +59,14 @@ def example_question_generator(session_hash, data_source, name, titles, schema):
 
     example_response = chat_generator.run(messages=example_messages)
 
-    return example_response["replies"][0].text
+    response_text = example_response["replies"][0].text
+    start = response_text.index("[") + 1
+    end = response_text.index("]")
+    response_content = response_text[start:end]
+    response_list = '[' + response_content + ']'
+    print(response_list)
+
+    return response_list
 
 def system_message(data_source, titles, schema=""):
     print("TITLES")