Welcome to the world of generative AI! In this guide, we’ll explore how to seamlessly integrate the LLM-Client-SDK for large language models. Whether you’re a novice or a seasoned developer, our step-by-step approach will make this process clear and manageable.

Understanding the LLM-Client-SDK

The LLM-Client-SDK is designed for easy integration with popular generative AI models like OpenAI, Google, Hugging Face, and more. Think of it as a universal remote control: instead of fumbling with multiple remotes for different devices, you have one tool that can access a variety of AI models without sacrificing flexibility.

Getting Started with Installation

Before diving into the code, let’s ensure the SDK is installed properly. You have several options depending on your needs:

• For all clients support: pip install llm-client[all]
• For only the base interface and light LLM clients (AI21 and Aleph Alpha): pip install llm-client
• For OpenAI support: pip install llm-client[openai]
• For Hugging Face support: pip install llm-client[huggingface]

Base Interface Explained

The SDK provides a base interface that allows you to interact with different LLMs effortlessly. Here’s a conceptual analogy: consider this interface as a restaurant menu where each dish represents a different AI service. You can select the dish (service) you want based on your appetite (requirements). Below is a simplified overview of how the code works:

class BaseLLMClient(ABC):
    @abstractmethod
    async def text_completion(self, prompt: str, **kwargs) -> list[str]:
        raise NotImplementedError()

Using the SDK: Sample Code

Let’s take a look at how to use the SDK effectively through some example codes.

1. Using OpenAI Directly

import os
from aiohttp import ClientSession
from llm_client import ChatMessage, Role, OpenAIClient, LLMAPIClientConfig

async def main():
    async with ClientSession() as session:
        llm_client = OpenAIClient(LLMAPIClientConfig(
            os.environ['API_KEY'], 
            session, 
            default_model='text-davinci-003'
        ))
        text = "This is indeed a test"
        messages = [
            ChatMessage(role=Role.USER, content="Hello!"),
            ChatMessage(role=Role.SYSTEM, content="Hi there! How can I assist you today?")
        ]
        print(await llm_client.text_completion(text))

Troubleshooting Common Issues

If you encounter any challenges during installation or usage, consider the following troubleshooting tips:

• Check Python Version: Ensure that your Python version is 3.9 or higher.
• Missing Dependencies: If you receive errors about missing packages, double-check your installation command.
• Permission Issues: Running commands with insufficient permissions can cause errors. Try using sudo or your terminal’s elevated permission option.
• API Key Issues: Ensure your API keys are set properly in your environment variables.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Contributing to LLM-Client-SDK

We welcome contributions! If you want to enhance the SDK, consider the following:

• Add support for more LLMs.
• Implement new functionalities such as model listing.
• Improve project documentation and guidelines.

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.

With the LLM-Client-SDK, you are now equipped to optimize your AI integration experience. Happy coding!