Chatbot returns empty response after updating python pa...

Hello guys, I'm facing an issue on mu chatbot. It's being a long time i didn't run my chatbot because i was training my GPT AI, I updated my python package and run my query fonctions and after my query returns "empty response". It did never return this message. Can you help me ? here is the code of my query:

Plain Text

        # Récupération info model

        model = GetModel(requestDTO.Model)

        # Initialisation OpenAI

        llm = AzureOpenAI(
            model=model.ChatModel.Model,
            deployment_name=model.ChatModel.Name,
            temperature=requestDTO.Temperature,
            api_key=model.Key1,
            azure_endpoint=model.Server,
            api_version=model.ChatModel.ApiVersion,
        )

        embed_model = AzureOpenAIEmbedding(
            model=model.LearningModel.Model,
            deployment_name=model.LearningModel.Name,
            api_key=model.Key1,
            azure_endpoint=model.Server,
            api_version=model.LearningModel.ApiVersion,
        )

        Settings.llm = llm
        Settings.embed_model = embed_model
        Settings.context_window = _contextWindow
        Settings.num_output = _numOutput

         # Génération de l'historique

        messages = []

        if (requestDTO.History != None):
            for item in requestDTO.History:
                messages.append(ChatMessage(role=item.Type.lower(), content=item.Prompt))
      
        # Initialisation des paramètres pour les requètes sur MongoDB Atlas

        mongodb_client = pymongo.MongoClient(_mongoURI)
        store = MongoDBAtlasVectorSearch(mongodb_client, db_name=requestDTO.Index)

        # Initialisation de l'index via les index sur MongoDB Atlas
        # Et inversement, commenter/décommenter si on veut juste query l'index existant
        # build index
        
        vector_index = VectorStoreIndex.from_vector_store(store)

        # configure retriever
        
        retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=requestDTO.LinkNumber)
        
        # configure response synthesizer, ici on peut chosir le mode réponse pour la query(ici on peut influer sur la maniere dont le contexte et le prompt sont itéré sur la query)
        
        response_synthesizer = get_response_synthesizer(response_mode=GetResponseMode(requestDTO.Mode), text_qa_template=qa_template)
        
        # assemble query engine, o recupere l'index grace au retriver et aussi le mode de reponse du "ResponseMode.TREE_SUMMARIZE" via response_synthesizer 
        
        query_retreiver = RetrieverQueryEngine(retriever=retriever, response_synthesizer=response_synthesizer)

        # Partie permettant de créer la réponse

        #query_engine = vector_index.as_query_engine(text_qa_template=qa_template)
        #gpt_result = query_engine.query(requestDTO.Prompt)

        gpt_result = query_retreiver.query(requestDTO.Prompt)

        resultDTO = ServiceQueryResponse.ServiceQueryResponseResultDTO(gpt_result.response, [])
        
        for item in gpt_result.source_nodes:
            node = ServiceQueryResponse.ServiceQueryResponseNodeDTO(item.node.extra_info.get("file_name"), item.node.extra_info.get("page_label"), item.node.text, item.score)
            resultDTO.Nodes.append(node)

        # Construction de la réponse

        responseDTO = ServiceQueryResponse.ServiceQueryResponseDTO(False, None, resultDTO)

        # Terminée, on envoi la réponse définitive

        return GenerateQueryResponse(requestDTO, responseDTO), 200
    
    except Exception as error:

        return str(error), 400

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Chatbot returns empty response after updating python packages