Issue with Azure OpenAI Integration: Response Returning None and Debugging Steps

Sanmeet Singh 0 Reputation points
2024-11-18T18:27:47.4533333+00:00

Hi everyone,

I’m working on an application that integrates with Azure OpenAI using the azure-openai Python SDK. My setup includes functionality for creating assistants, uploading files to a vector store, and requesting responses using threads and tools like file_search. However, I’m encountering an issue where the assistant response consistently returns None.

Some code snippet -

For Upload to vector -

def upload_files_to_vector_store(self, file_streams): try: print(“Uploading files to vector store…”) seen_files = set() unique_files =

`        for file_stream in file_streams:
            file_name = file_stream.name
            if file_name not in seen_files:
                seen_files.add(file_name)
                unique_files.append(file_stream)
                print(f"Adding file: {file_name}")
            else:
                print(f"Skipping duplicate file: {file_name}")

        vector_store = self.client.beta.vector_stores.create(name="My Vector Store new one")
        print(f"Vector store created with ID: {vector_store.id}")

        batch = self.client.beta.vector_stores.file_batches.upload_and_poll(
            vector_store_id=vector_store.id, files=unique_files
        )
        print("File batch upload completed.")

        self.client.beta.assistants.update(
            assistant_id=self.assistant_id,
            tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}
        )
        print("Assistant updated with new vector store.")
        self.vector_store_id = vector_store.id
        return self.vector_store_id`

For requesting message -

thread = self.client.beta.threads.create( messages=[{“role”: “user”, “content”: prompt}], tool_resources={“file_search”: {“vector_store_ids”: [self.vector_store_id]}} ) with self.client.beta.threads.runs.stream( thread_id=thread.id, assistant_id=self.assistant_id, instructions=“Provide a detailed response.”, event_handler=event_handler, ) as stream: stream.until_done()

Azure OpenAI Service
Azure OpenAI Service
An Azure service that provides access to OpenAI’s GPT-3 models with enterprise capabilities.
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  1. Sina Salam 13,371 Reputation points
    2024-11-18T22:53:05.92+00:00

    Hello Sanmeet Singh,

    Welcome to the Microsoft Q&A and thank you for posting your questions here.

    I understand that you are having the assistant response consistently returns None when using the Azure OpenAI Python SDK in a multi-step workflow involving vector store uploads and thread-based requests.

    I will provide you step-by-step improvement to your current implementation.

    1. Add detailed logging and exception handling during vector store creation and file upload to catch and log any issues.
         def upload_files_to_vector_store(self, file_streams):
             try:
                 print("Uploading files to vector store...")
                 seen_files = set()
                 unique_files = []
                 for file_stream in file_streams:
                     file_name = file_stream.name
                     if file_name not in seen_files:
                         seen_files.add(file_name)
                         unique_files.append(file_stream)
                         print(f"Adding file: {file_name}")
                     else:
                         print(f"Skipping duplicate file: {file_name}")
                 # Create the vector store
                 vector_store = self.client.beta.vector_stores.create(name="My Vector Store")
                 print(f"Vector store created with ID: {vector_store.id}")
                 # Upload files to vector store
                 batch = self.client.beta.vector_stores.file_batches.upload_and_poll(
                     vector_store_id=vector_store.id, files=unique_files
                 )
                 print("File batch upload completed. Batch ID:", batch.id)
                 # Update assistant with the vector store ID
                 self.client.beta.assistants.update(
                     assistant_id=self.assistant_id,
                     tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}
                 )
                 print("Assistant updated with vector store.")
                 
                 self.vector_store_id = vector_store.id
                 return self.vector_store_id
             except Exception as e:
                 print(f"Error in vector store upload: {e}")
                 return None
      

    This will make sure your Vector Store Creation and File Upload.

    Secondly, also make sure every step of the assistant response process has proper logging and that you capture all events.

    thread = self.client.beta.threads.create(
        messages=[{"role": "user", "content": prompt}],
        tool_resources={"file_search": {"vector_store_ids": [self.vector_store_id]}}
    )
    def event_handler(event):
        if event.data.get('type') == 'error':
            print(f"Error event: {event.data}")
        else:
            print(f"Event received: {event.data}")
    try:
        with self.client.beta.threads.runs.stream(
            thread_id=thread.id,
            assistant_id=self.assistant_id,
            instructions="Provide a detailed response.",
            event_handler=event_handler,
        ) as stream:
            response = stream.until_done()
            print("Stream response completed.")
    except Exception as e:
        print(f"Error during assistant streaming: {e}")
    

    And this will help Requesting Assistant Response with Proper Logging.

    1. Make sure that the uploaded files are indexed and accessible by querying the vector store directly and double-check your Azure OpenAI subscription limits and ensure all necessary configurations (e.g., assistant ID, vector store ID) are valid. Also, your event_handler should provide detailed information about what happens during the stream process. If errors occur, they will be captured here.

    Other things you can do next are:

    • Capture and review logs to understand what happens during each stage.
    • Use print or logging to inspect the raw responses and events.
    • Make sure you are using the latest version of the azure-openai SDK.

    I hope this is helpful! Do not hesitate to let me know if you have any other questions.


    Please don't forget to close up the thread here by upvoting and accept it as an answer if it is helpful.

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