Update main.py

main.py

@@ -94,7 +94,7 @@ except Exception as e:
     raise RuntimeError("Something went wrong with the Together API setup. Please verify your API key and configuration.")
 
 # ==========================
-# Conversational Retrieval Chain (RAG Implementation)
+# Conversational Retrieval Chain
 # ==========================
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 qa = ConversationalRetrievalChain.from_llm(
@@ -102,9 +102,8 @@ qa = ConversationalRetrievalChain.from_llm(
     memory=memory,
     retriever=db_retriever,
     combine_docs_chain_kwargs={"prompt": prompt},
-    return_source_documents=True  # This enables logging of retrieved content
 )
-logger.info("Conversational Retrieval Chain initialized with RAG capabilities.")
+logger.info("Conversational Retrieval Chain initialized.")
 
 # ==========================
 # FastAPI Backend
@@ -125,15 +124,23 @@ async def root():
 async def chat(request: ChatRequest):
     try:
         logger.debug(f"Received user question: {request.question}")
-        result = qa.invoke(input=request.question)
         
-        # Log the retrieved source documents for debugging purposes
-        source_docs = result.get("source_documents")
-        logger.debug(f"Retrieved source documents: {source_docs}")
+        # Retrieve documents and log them
+        retrieved_docs = db_retriever.get_relevant_documents(request.question)
+        logger.debug("Retrieved Documents:")
+        for i, doc in enumerate(retrieved_docs, start=1):
+            logger.debug(f"Document {i}: {doc.page_content[:500]}...")  # Log first 500 characters of each document
         
+        # Invoke the QA chain with the user question
+        result = qa.invoke(input=request.question)
         answer = result.get("answer")
+        
         if not answer or "The information is not available in the provided context" in answer:
             answer = "I'm sorry, I couldn't find relevant information for your query. Please try rephrasing or providing more details."
+        
+        # Log the final answer
+        logger.debug(f"Chatbot Answer: {answer}")
+        
         return ChatResponse(answer=answer)
     except Exception as e:
         logger.error(f"Error during chat invocation: {e}")