If you’ve ever found yourself overwhelmed by the amount of data you need to process using the OpenAI API, fear not! The openai-multi-client Python library is here to help you handle multiple requests simultaneously, all while keeping your application code easy to manage. In this article, we’ll guide you step-by-step on how to get started with the OpenAI Multi-Client.

Table of Contents

• Motivation
• Features
• Installation
• Usage Example
• Troubleshooting

Motivation

Imagine you are at the helm of a ship navigating through a vast ocean filled with articles, eager to discover valuable insights. If you send requests to the OpenAI API one by one, your ship barely moves, taking an eternity to reach its destination. The OpenAI Multi-Client acts like a fleet of ships, allowing you to sail multiple paths at once, significantly speeding up your analysis!

Features

• Concurrent requests to the OpenAI API
• Support for ordered and unordered requests
• Built-in retries for failed requests
• Customizable API client for testing and mocking
• User-friendly interface

Installation

To equip your system with the power of the OpenAI Multi-Client, simply run the following command in your terminal:

pip install openai-multi-client

Usage Example

To illustrate the magic of the OpenAI Multi-Client, let’s dig into a straightforward example:

from openai_multi_client import OpenAIMultiClient

# Set the OPENAI_API_KEY environment variable to your API key
api = OpenAIMultiClient(endpoint='chats', data_template='model: gpt-3.5-turbo')

def make_requests():
    for num in range(1, 10):
        api.request(data={
            'messages': [
                {'role': 'user', 'content': f'Can you tell me what is {num} * {num}?'}
            ]
        }, metadata={'num': num})

api.run_request_function(make_requests)

for result in api:
    num = result.metadata['num']
    response = result.response['choices'][0]['message']['content']
    print(f"{num} * {num}: {response}")

Here’s a breakdown of what this code is doing:

• Imagine your code as a well-orchestrated symphony, where each musician (in this case, your requests) plays together harmoniously. Instead of a single musician performing solo (serial requests), the OpenAI Multi-Client allows multiple musicians to play at once (parallel requests), creating a performance that is both captivating and efficient.
• You set up the OpenAIMultiClient with the desired endpoint and data template, then define a function to make your requests.
• Requests are sent in a loop, and once all are completed, results are processed and displayed.

Troubleshooting

If you run into issues during installation or usage, consider these troubleshooting ideas:

• Ensure you have installed the library properly using the command provided in the Installation section.
• Check that your OPENAI_API_KEY is correctly set in your environment variables.
• Adjust the concurrency parameter in your client setup if you’re experiencing rate limits.
• Review error messages carefully; they often provide hints about what went wrong.

For additional assistance, or if you’re interested in collaborating on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.