concurrent-openai 0.2.1

Python library for asynchronous interactions with the OpenAI API, enabling concurrent request handling. It simplifies building scalable, AI-powered applications by offering efficient, rate-limited access to OpenAI services. Perfect for developers seeking to integrate OpenAI's capabilities with minimal overhead.

Concurrent OpenAI Manager

The Concurrent OpenAI Manager is a pure Python library meticulously designed for developers seeking an optimal integration with OpenAI's APIs. This library is engineered to handle API requests with efficiency, ensuring compliance with rate limits and managing system resources effectively, all while providing transparent cost estimations for OpenAI services.

Key features

Rate limiting

Central to the library is a carefully crafted rate limiter, capable of managing the number of requests and tokens per minute. This ensures your application stays within OpenAI's usage policies, avoiding rate limit violations and potential service disruptions.

Throttled Request Dispatching

The throttling mechanism is designed to prevent sudden surges of requests, spreading them evenly over time. This ensures a steady and predictable load on OpenAI's endpoints, contributing to a responsible utilization of API resources and avoiding the 429 errors that might occur if we simply do all the requests at once.

Semaphore for Concurrency Control

To manage local system resources or limit parallelism, the library incorporates a semaphore mechanism. This allows developers to specify the maximum number of concurrent operations, ensuring balanced resource utilization and a responsive application performance. Useful when you want tot manage local resources (such as database connections or memory usage) or wish to limit parallelism to ensure a responsive user experience. By fine-tuning the semaphore value, you have control on the amount of coroutines that are on the Event Loop.

Cost Estimation

A notable feature of the Concurrent OpenAI Manager is its built-in cost estimation. This functionality provides users with detailed insights into the cost implications of their API requests, including a breakdown of prompt and completion tokens used. Such transparency empowers users to manage their budget effectively and optimize their use of OpenAI's APIs.

Getting started

Integrating the Concurrent OpenAI Manager into your project is straightforward:

$ pip install concurrent-openai

Usage

1. Create a .env file in your project directory.
2. Add an env variable named OPENAI_API_KEY.
3. Test it out:

from concurrent_openai import process_completion_requests

results = await process_completion_requests(
    prompts=[{"role": "user", "content": "Knock, knock!"}],
    model="gpt-4-0613",
    temperature=0.7,
    max_tokens=150,
    max_concurrent_requests=5,
    token_safety_margin=10,
)

for result in results:
    if result:
        print(result)
    else:
        print("Error processing request.")