Update utils/database.py

utils/database.py

@@ -3,13 +3,14 @@ import streamlit as st
 import sqlite3
 from sqlite3 import Error
 from datetime import datetime
-from langchain.chains.conversational_retrieval.base import (
-    ConversationalRetrievalChain,
-)
-from langchain.memory import ConversationBufferMemory
+#from langchain.memory import ConversationBufferMemory
+from langchain.chat_models import ChatOpenAI
+import os
+from langchain.memory import ConversationBufferWindowMemory
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
 import os
-
 
 def create_connection(db_file):
     try:
@@ -90,38 +91,35 @@ def insert_document(conn, doc_name, doc_content):
     return False
 
 
+# utils/database.py
+from langchain.memory import ConversationBufferWindowMemory
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+import os
+
 def initialize_qa_system(vector_store):
     """Initialize QA system with proper chat handling"""
     try:
-        from langchain.prompts import ChatPromptTemplate
-        from langchain.prompts import MessagesPlaceholder
-
         llm = ChatOpenAI(
             temperature=0,
             model_name="gpt-4",
             api_key=os.environ.get("OPENAI_API_KEY"),
         )
 
-        # Create prompt template
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", "You are a helpful assistant analyzing RFP documents."),
-            MessagesPlaceholder(variable_name="chat_history"),
-            ("human", "{input}"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ])
-
-        memory = ConversationBufferMemory(
+        # Initialize memory with proper configuration
+        memory = ConversationBufferWindowMemory(
             memory_key="chat_history",
-            return_messages=True
+            return_messages=True,
+            k=5  # Keep last 5 interactions
         )
 
         qa_chain = ConversationalRetrievalChain.from_llm(
             llm=llm,
             retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
             memory=memory,
-            combine_docs_chain_kwargs={"prompt": prompt},
-            return_source_documents=True,
-            verbose=True
+            verbose=True,
+            return_source_documents=True
         )
 
         return qa_chain