Why doesn't the OpenAI assistants API return a reliable answer in my Python script?

Below is some guidance on troubleshooting your code:

1. Returning a Value

Your function currently prints the results but does not return any value. For example, at the end of your function you could gather the final message text and return it:

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def get_prompt(question):
    assistant = client.beta.assistants.create(
        name="Math tutor",
        instructions="I am a math tutor. I can help you with your math homework.",
        tools=[{"type": "code_interpreter"}],
        model="gpt-4-1106-preview"
    )

    thread = client.beta.threads.create()
    print("Thread created:", thread)

    message = client.beta.threads.messages.create(
        thread_id=thread.id,
        role="user",
        content=question
    )
    print("User message:", message)

    run = client.beta.threads.runs.create(
        thread_id=thread.id,
        assistant_id=assistant.id,
    )

    run = client.beta.threads.runs.retrieve(
        thread_id=thread.id,
        run_id=run.id,
    )
    
    messages = client.beta.threads.messages.list(
        thread_id=thread.id,
    )

    # Extract the awaited assistant reply from the messages.
    # (Adjust the extraction depending on how the message object is structured)
    for msg in reversed(messages.data):
        if msg.role == "assistant":
            assistant_reply = msg.content[0].text.value
            print("Assistant:", assistant_reply)
            return assistant_reply
    return None

2. Delay for Completion

Sometimes these API operations are asynchronous. It might be that the assistant’s reply isn't ready by the time you retrieve messages. Consider adding a polling mechanism or a slight delay before retrieving the run result:

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import time

# Right after creating the run, consider waiting a few seconds
time.sleep(2)  # Adjust the sleep duration as needed.

3. Environment Differences

Your experience of varied behavior between debug mode and execution in a notebook may relate to timing differences. In notebooks or debug mode, execution might be slower, giving the API enough time to process the assistant's run before you query messages. In a script, consider using explicit waits or checking the run status before retrieving messages.

4. Quotes in the Code

Ensure you’re using standard quotation marks (") for strings. Sometimes curly quotes (“ ”) can cause issues in Python.

Summary

• Return a value: Make sure your function returns the assistant's reply instead of only printing it.
• Handle asynchronous processing: Introduce a waiting mechanism or polling to ensure the assistant's message is ready.
• Check your environment: The differences in execution between environments might be affecting timing.

These changes should help you get a reliable return from the API call. Let me know if you have any further questions on using the OpenAI API or if you'd like additional details on handling asynchronous responses.