Update app.py

app.py

@@ -59,6 +59,9 @@ uploads_dir = os.path.join(app.root_path,'static', 'uploads')
 
 os.makedirs(uploads_dir, exist_ok=True)
 
+# Initialize global variables for conversation history
+conversation_history = []
+
 defaultEmbeddingModelID = 3
 defaultLLMID=0
 
@@ -201,6 +204,26 @@ 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.load_history(conversation_history)
+    
+    # chain = RetrievalQA.from_chain_type(
+    #     llm=getLLMModel(llmID),
+    #     chain_type='stuff',
+    #     retriever=getRetriever(vectordb),
+    #     #retriever=vectordb.as_retriever(),
+    #     verbose=False,
+    #     chain_type_kwargs={
+    #         "verbose": False,
+    #         "prompt": createPrompt(customerName, customerDistrict, custDetailsPresent),
+    #         "memory": ConversationBufferWindowMemory(
+    #             k=3,
+    #             memory_key="history",
+    #             input_key="question"),
+    #     }
+    # )
     chain = RetrievalQA.from_chain_type(
         llm=getLLMModel(llmID),
         chain_type='stuff',
@@ -210,10 +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"),
+            "memory": memory,
         }
     )
     return chain
@@ -307,6 +327,10 @@ def aisearch():
 def process_json():
     print(f"\n{'*' * 100}\n")
     print("Request Received >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
+
+    # Retrieve conversation ID from the request (use any suitable ID)
+    conversation_id = request.json.get('conversation_id', None)
+    
     content_type = request.headers.get('Content-Type')
     if content_type == 'application/json':
         requestQuery = request.get_json()
@@ -322,6 +346,14 @@ def process_json():
         selectedLLMID=defaultLLMID
         if "llmID" in requestQuery:
             selectedLLMID=(int) (requestQuery['llmID'])
+
+        # Create a conversation ID-specific history list if not exists
+        conversation_history_id = f"{conversation_id}_history"
+        if conversation_history_id not in globals():
+            globals()[conversation_history_id] = []
+        conversation_history = globals()[conversation_history_id]
+
+        
         print("chain initiation")
         chainRAG = getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,selectedLLMID)
         print("chain created")
@@ -332,6 +364,7 @@ def process_json():
             # message = answering(query)
 
             relevantDoc = vectordb.similarity_search_with_score(query, distance_metric="cos", k=3)
+            conversation_history.append(query)
             print("Printing Retriever Docs")
             for doc in getRetriever(vectordb).get_relevant_documents(query):
                 searchResult = {}