Update app.py

app.py

@@ -206,8 +206,8 @@ def loadKB(fileprovided, urlProvided, uploads_dir, request):
 def getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,llmID):
 
     # Retrieve conversation history if available
-    memory = ConversationBufferWindowMemory(k=3, memory_key="history", input_key="question")
-    
+    #memory = ConversationBufferWindowMemory(k=3, memory_key="history", input_key="question")
+    memory = ConversationBufferWindowMemory(k=3, memory_key="history", input_key="question", initial_memory=conversation_history)
     
     # chain = RetrievalQA.from_chain_type(
     #     llm=getLLMModel(llmID),
@@ -233,7 +233,7 @@ def getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,llm
         chain_type_kwargs={
             "verbose": False,
             "prompt": createPrompt(customerName, customerDistrict, custDetailsPresent),
-            "memory": ConversationBufferWindowMemory(k=3, memory_key="history", input_key="question", initial_memory=conversation_history),
+            "memory": memory
         }
     )
     return chain
@@ -362,7 +362,7 @@ def process_json():
 
         for index, query in enumerate(requestQuery['message']):
             # message = answering(query)
-
+            memory.chat_memory.add_user_message(query)
             relevantDoc = vectordb.similarity_search_with_score(query, distance_metric="cos", k=3)
             conversation_history.append(query)
             print("Printing Retriever Docs")
@@ -383,6 +383,7 @@ def process_json():
             print("Chain Run Completed >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
             print("query:", query)
             print("Response:", message)
+            memory.chat_memory.add_ai_message(message)
             if "I don't know" in message:
                 message = "Dear Sir/ Ma'am, Could you please ask questions relevant to Jio?"
             responseJSON = {"message": message, "id": index}