Update main.py

main.py

@@ -368,9 +368,11 @@ def get_desc_llm(desc):
    
 # find my description:
     check_desc=f'''
-    Description: I am part of a group which can be described as '{desc}'
+    Description: you are part of a group which can be described as '{desc}'
     Thought:  What do i know about myself?
-    Show observations and Action. Don't entire show verbose, don't mention anything additional
+    Show detailed observations and Action.
+    Thought:  What i don't know about myself?
+    Don't entire show verbose, don't mention anything additional
                 '''
     
     completion = openai.ChatCompletion.create(
@@ -379,7 +381,10 @@ def get_desc_llm(desc):
     messages=[
         {"role": "system", "content": "You are an expert at finding the context fromt the description"},
         {"role": "user", "content": "You are part of a group which can be described as 'Married women with 2 children'"},
-        {"role": "assistant", "content": "i am a women hence my gender is female, i have 2 children not sure about their age and gender, my relationship status is married. I know about my gender, children, relationship status only"},
+        {"role": "assistant", "content": '''i am a women hence my gender is female, i have 2 children not sure about their age and gender, my relationship status is married. 
+         I know about:
+           my gender, children, relationship status only
+         '''},
         {"role": "user", "content": check_desc }
     ]
     )
@@ -430,6 +435,20 @@ def filter_questions_chatgpt(questions: dict, decs: str):
     """
     # Set up the chat conversation with OpenAI Chat API
 
+    # prompt = f'''You are Tan. You are described as "{decs}".
+    # Find the relavent questions to you from below list of questions based on your description
+    # {questions}
+    # Return their IDs in below format:
+    # ```["53","39", ...]```
+    # Don't add anything to the response other than above format.
+    # '''
+
+
+    # Return their IDs in below format:
+    # ```["53","39", ...]```
+
+    # Don't add anything to the response other than above format.
+
     prompt = f'''
     Questions:
     {questions}
@@ -441,7 +460,7 @@ def filter_questions_chatgpt(questions: dict, decs: str):
         model="gpt-3.5-turbo",
         temperature=0.3,
         messages=[
-            {"role": "system", "content": "You are an expert at selecting relavent questions from the list of survey qestions for which answers are available in description and returning in list format"},
+            {"role": "system", "content": "You are an expert at selecting relavent questions from the list of qestions for which answers are available in description and returning in list format"},
             {"role": "user", "content": prompt}
         ]
     )
@@ -454,18 +473,22 @@ def filter_questions_chatgpt(questions: dict, decs: str):
 
 def get_answers_llm(cont:str,q_json:dict):
     prompt=f'''
-      You will be provided with a list of survey questions in JSON format and should respond to all questions based on the context provided.
-      If the answer is not available, please respond with "NaN" in the JSON format for that question.
+      You will be provided with a list of questions in JSON format and should respond to all questions based on the observation provided.
       If you are not sure about the answer, please respond with "NeC" in the JSON format for that question.
-      Context:{cont}
-      Questions:{q_json}
+      If the answer is not available, please respond with "NaN" in the JSON format for that question.
+      Context:
+      {cont}
+      Questions:
+      {q_json}
+      Before answering the questions, please make sure you have read the context and questions carefully.
+    check if the answer is available in the context, if not, please remove that question from the JSON response.       
     '''
     print(prompt)
     completion = openai.ChatCompletion.create(
-    model="gpt-3.5-turbo-0613", 
+    model="gpt-3.5-turbo", 
     temperature=0.4,
     messages=[
-        {"role": "system", "content": "You are an expert at answering survey questions based on the context, observation and action"},
+        {"role": "system", "content": "You are an expert at answering questions based on the context, observation and action"},
         {"role": "user", "content": "what is your age?"},
         {"role": "assistant", "content": "50"},
         {"role": "user", "content": prompt}
@@ -480,9 +503,9 @@ def revalidate_final_response_gpt(context:str,
                                   )->dict:
     
     prompt=f'''
-    You will be provided with a list of survey questions in JSON format and should select the most relavent answer from the answer_options based on the context provided.
-    If the options are not available, please answer based on the observations and actions
-    If the answer is not available, remove that question from the JSON response
+    You will be provided with a list of questions in JSON format and should select right answer from the answer_options strictly based on the context provided. 
+    If the answer is not available, remove that question from the JSON response.
+    If answer is similar to "No", "None", "None of the above","I don't" remove that question from the JSON response.
 
     Context:{context}
     Questions:{json_response}
@@ -495,14 +518,16 @@ def revalidate_final_response_gpt(context:str,
             {{"id": "632", "question": "What is your relationship status?", "answer": "Single, never married"}},
         ]
         }}
-
+    
+    Before answering the questions, please make sure you have read the context and questions carefully.
+    check if the answer is available in the context, if not, please remove that question from the JSON response.       
     '''
     print(prompt)
 
     response = openai.Completion.create(
         model="text-davinci-003",
         prompt=prompt,
-        temperature=0.2,
+        temperature=0.0,
         max_tokens=2000,
         top_p=1,
         frequency_penalty=0,
@@ -512,7 +537,6 @@ def revalidate_final_response_gpt(context:str,
     print("revalidate_final_response_gpt > Total tokens used:", response["usage"]["total_tokens"])
     return json_response
 
-
 # indxing methods
 
 def data_cleanup(file_path):