How to properly integrate an openai client into a Dash app?

Hi there,
I would like to develop an app which implements openai api functionality. I would like to use Python and the Dash library to design it as a website.

Dash works with callback functions that detect and execute whenever a designated input element (like a button, slider, text…) is changed. Dash is a stateless framework, meaning, any callback function should not directly modify variables that were defined outside their scope. This is for two reasons: 1. To ensure that multiple independent user sessions can run simultaneously. 2. To make parallel callbacks possible using multiple ‘workers’ which do not share memory.

So my question: When I initialize the openai client
client = OpenAI()
do I have to do this inside each callback function whenever I want to generate responses? Or is it possible to do this once, ‘globally’, and then use this client inside the callbacks?

How to properly integrate an openai client into a Dash app?

Answer from kapa.ai

Based on the information provided in the extracts, it is recommended to instantiate the OpenAI client for each session or use case, rather than relying on a global client. This is particularly important in a stateless framework like Dash, where multiple independent user sessions can run simultaneously and parallel callbacks are possible using multiple ‘workers’ which do not share memory.

You can initialize the OpenAI client within each callback function whenever you want to generate responses. Here’s an example of how you might do this:

from openai import OpenAI

def my_callback_function():
    client = OpenAI()
    # Your code here

Based on the information provided in the extracts, it is recommended to instantiate the OpenAI client for each session or use case, rather than relying on a global client. This is particularly important in a stateless framework like Dash, where multiple independent user sessions can run simultaneously and parallel callbacks are possible using multiple ‘workers’ which do not share memory.

You can initialize the OpenAI client within each callback function whenever you want to generate responses. Here’s an example of how you might do this:

from openai import OpenAI

def get_openai_client():
    return OpenAI()

def my_callback_function():
    client = get_openai_client()
    # Your code here

This approach can help keep your code DRY (Don’t Repeat Yourself) and make it easier to manage the OpenAI client configuration in one place. Remember to replace OpenAI() with OpenAI(api_key=os.environ['OPENAI_API_KEY']) if you have saved the key under a different environment variable name.

Sources:

Thanks for the idea and the Github sources. I don’t really think the AI produced a well founded answer yet, as it didn’t have sources on Dash other than what I said.

I have looked at older Dash examples that have some of the functionality I want. The problem is, they are “older” as in still using the API before openai.ChatCompletions() was deprecated. I cannot post links yet, but a search for “Dash openai examples” should yield results.

From your sources I take it that was a global instance of the client, right? So it may still be appropriate to call a global instance and then re-use it?

I think of clients as sockets - they are stateless although through them you can maintain state based on other things. For the assistants API (as example) the statefullness of a user is maintained through the thread_id and you can use that thread_id (which holds the entire conversation of messages) in any client connected through the same app with same key and same assistant_id. So I just create clients and close them pretty casually (I don’t try to preserve them) because I don’t want leftover clients hanging around after a user finishes. Any time my app reaches a point where the user might ‘think’ or walk away I close the client. Then I open a new one before I need to use the thread again. In a flask app I keep the thread_id in the user session and recall it from there.

Thank you for the idea. I have tried it and it works to an extend - Since I need to reach the OpenAI API through a proxy, I explicitly create an httpx-Client to call with OpenAI(http_client=…). Naturally, this also gets destroyed whenever I call client.close().

There is a developer post on GitHub by Robert Craigie which also makes me think. I can’t include links yet, but the topic is “Unclosed client session”. In particular this part:

It should be noted that we highly recommend using one client instance and re-using it between requests as we can then re-use HTTP connections leading to a massive performance boost.