Update main.py

main.py

@@ -59,7 +59,7 @@ except Exception as e:
 # ==========================
 try:
     db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
-    db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3, "max-length": 512})
+    db_retriever = db.as_retriever(search_type="mmr", search_kwargs={"k": 5, "max-length": 512})
     logger.info("Vectorstore successfully loaded.")
 except Exception as e:
     logger.error(f"Error loading FAISS vectorstore: {e}")
@@ -125,11 +125,12 @@ async def chat(request: ChatRequest):
     try:
         logger.debug(f"Received user question: {request.question}")
         
-        # 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
+        # Retrieve documents and log them with similarity scores
+        retrieved_docs = db_retriever.invoke({"query": request.question})
+        logger.debug("Retrieved Documents and Scores:")
+        for i, doc in enumerate(retrieved_docs["documents"], start=1):
+            logger.debug(f"Document {i}: {doc.page_content[:500]}...")
+            logger.debug(f"Score: {retrieved_docs['scores'][i-1]}")
         
         # Invoke the QA chain with the user question
         result = qa.invoke(input=request.question)