changes in prompt to address issue in welcome message of comment about not excerpts

app.py

@@ -2,7 +2,7 @@ from fastapi import FastAPI, Request, Depends, HTTPException, Header, File, Uplo
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from typing import List, Optional
-from helpmate_ai import initialize_conversation, retreive_results, rerank_with_cross_encoder, generate_response
+from helpmate_ai import get_system_msg, retreive_results, rerank_with_cross_encoder, generate_response
 import google.generativeai as genai
 import os
 from dotenv import load_dotenv
@@ -53,7 +53,7 @@ class Report(BaseModel):
 
 # Initialize conversation and model
 conversation_bot = []
-conversation = initialize_conversation()
+conversation = get_system_msg()
 model = genai.GenerativeModel("gemini-1.5-flash", system_instruction=conversation)
 
 # Initialize speech recognizer
@@ -76,6 +76,8 @@ def get_gemini_completions(conversation: str) -> str:
 @app.get("/init", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
 async def initialize_chat():
     global conversation_bot
+
+    conversation = "Hi" 
     introduction = get_gemini_completions(conversation)
     conversation_bot = [Message(role="bot", content=introduction)]
     return ChatResponse(
@@ -159,11 +161,11 @@ async def handle_feedback(
 async def reset_conversation():
     global conversation_bot, conversation
     conversation_bot = []
-    conversation = initialize_conversation()
+    conversation = "Hi"
     introduction = get_gemini_completions(conversation)
     conversation_bot.append(Message(role="bot", content=introduction))
     return {"status": "success", "message": "Conversation reset"}
 
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

helpmate_ai.py

@@ -2,11 +2,11 @@
 import pandas as pd
 import chromadb
 
-def initialize_conversation():
+def get_system_msg():
     """
     Generate a response using GPT-3.5's ChatCompletion based on the user query and retrieved information.
     """
-    conversation = [
+    system_msg = [
             f"""
             You are a helpful assistant in the insurance domain who can effectively answer user queries about insurance policies and documents.
             The document name is 'Group Life Insurance Policy' and it contais information about 3 different insurance policies 'Member Life Insurance', 'Member Accidental Death and Dismemberment Insurance' and 'Dependent Life Insurance'.
@@ -46,14 +46,22 @@ def initialize_conversation():
             6. If the provided excerpts do not fully answer the query, provide partial information and suggest which sections of the policy document the user should review for further details.
             7. If no relevant information is found in the provided excerpts, respond with 'No relevant information found in the provided excerpts.'
             
-            # Start the session with a short welcome message which also has policy name and a smiley and nothing else.
+            <When user says 'Hi' respond with a short welcome message which also has policy name and a smiley.>
             """
     ] 
                 
-    # conversation = [{"role": "user", "parts": system_message}] 
-    # conversation = [{"role": "system", "content": system_message}]
-
-    return conversation
+    return system_msg
+
+# def get_welcome_msg():
+#     """
+#     Generate a welcome msg.
+#     """
+#     messages = f"""
+#                 Start the session with a short welcome message which also has policy name and a smiley.
+#                 """
+#     introduction = [{"role": "user", "parts": messages}] 
+
+#     return introduction
 
 # Import the SentenceTransformer Embedding Function into chroma
 from chromadb.utils import embedding_functions