Update utils/database.py

utils/database.py

@@ -10,8 +10,9 @@ from langchain.memory import ConversationBufferWindowMemory
 from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage
 from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, 
 from langchain.agents import initialize_agent
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
 
 def create_connection(db_file):
     try:
@@ -106,6 +107,8 @@ def format_chat_history(messages: list[BaseMessage]) -> list[dict]:
 
 
 
+
+
 def initialize_qa_system(vector_store):
     """Initialize QA system with proper chat handling"""
     try:
@@ -122,36 +125,48 @@ def initialize_qa_system(vector_store):
             k=5
         )
 
-        # Create retrieval QA chain
+        # Create the base QA chain
         qa = ConversationalRetrievalChain.from_llm(
             llm=llm,
             retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
             chain_type="stuff",
         )
 
-        # Create tool for the agent
-        qa_tool = Tool(
-            name='Knowledge Base',
-            func=qa.run,
-            description='use this tool when answering questions about the RFP documents'
-        )
-
-        # Initialize agent
-        agent = initialize_agent(
-            agent='chat-conversational-react-description',
-            tools=[qa_tool],
-            llm=llm,
-            verbose=True,
-            max_iterations=3,
-            early_stopping_method='generate',
+        # Define the tools
+        tools = [
+            Tool(
+                name="RFP_Knowledge_Base",
+                func=qa.run,
+                description="Use this tool to analyze RFP documents and answer questions about their content."
+            )
+        ]
+
+        # Create the 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"),
+        ])
+
+        # Create the agent
+        agent = create_openai_tools_agent(llm, tools, prompt)
+
+        # Create the agent executor
+        agent_executor = AgentExecutor(
+            agent=agent,
+            tools=tools,
             memory=memory,
+            verbose=True,
+            handle_parsing_errors=True
         )
 
-        return agent
+        return agent_executor
 
     except Exception as e:
         st.error(f"Error initializing QA system: {e}")
         return None
+        
 def initialize_faiss(embeddings, documents, document_names):
     """Initialize FAISS vector store"""
     try: