addition of intro_msg

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 get_system_msg, retreive_results, rerank_with_cross_encoder, generate_response
+from helpmate_ai import get_system_msg, retreive_results, rerank_with_cross_encoder, generate_response, intro_message
 import google.generativeai as genai
 import os
 from dotenv import load_dotenv
@@ -11,6 +11,7 @@ import re
 import speech_recognition as sr
 from io import BytesIO
 import wave
+
 import google.generativeai as genai
 
 
@@ -77,11 +78,11 @@ def get_gemini_completions(conversation: str) -> str:
 async def initialize_chat():
     global conversation_bot
 
-    conversation = "Hi" 
-    introduction = get_gemini_completions(conversation)
-    conversation_bot = [Message(role="bot", content=introduction)]
+    # conversation = "Hi" 
+    # introduction = get_gemini_completions(conversation)
+    conversation_bot = [Message(role="bot", content=intro_message)]
     return ChatResponse(
-        response=introduction,
+        response=intro_message,
         conversation=conversation_bot
     )
 
@@ -109,6 +110,7 @@ async def chat(request: ChatRequest):
         response=response_assistant,
         conversation=conversation_bot
     )
+
 # Voice processing endpoint
 @app.post("/process-voice")
 async def process_voice(audio_file: UploadFile = File(...), dependencies=[Depends(verify_api_key)]):
@@ -161,9 +163,9 @@ async def handle_feedback(
 async def reset_conversation():
     global conversation_bot, conversation
     conversation_bot = []
-    conversation = "Hi"
-    introduction = get_gemini_completions(conversation)
-    conversation_bot.append(Message(role="bot", content=introduction))
+    # conversation = "Hi"
+    # introduction = get_gemini_completions(conversation)
+    conversation_bot.append(Message(role="bot", content=intro_message))
     return {"status": "success", "message": "Conversation reset"}
 
 if __name__ == "__main__":

helpmate_ai.py

@@ -2,6 +2,8 @@
 import pandas as pd
 import chromadb
 
+intro_message = 'Hello! 😊 ask me questions about Inurance Policy. Sample questions you can ask are \'What is the name of policy?\', \'What are the different insurance offered?\', \'What are the premium rates for different types of insurance under this policy?\' etc.'
+
 def get_system_msg():
     """
     Generate a response using GPT-3.5's ChatCompletion based on the user query and retrieved information.
@@ -40,13 +42,12 @@ def get_system_msg():
 
             Guidelines:
             1. Extract information that directly answers the user's query from the document excerpts.
-            3. Provide the final response as a well-formatted HTML test and easily readable text along with the citation.
+            3. Provide the final response well-formatted and easily readable text along with the citation.
             4. Provide your complete response using the relevant parts in the documents.
             5. The generated response should answer the query directly addressing the user and avoiding additional information about how you work.
             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.'
-            
-            <When user says 'Hi' respond with a short welcome message which also has policy name and a smiley.>
+
             """
     ] 
                 
@@ -118,7 +119,7 @@ def retreive_results(query):
         # Query the collection against the user query and return the top 10 results
     results = insurance_collection.query(
         query_texts=query,
-        n_results=10
+        n_results=50
     )
 
     # Store the query in cache_collection as document w.r.t to ChromaDB so that it can be embedded and searched against later