Introduction to the Instructor Library

The Instructor library is a powerful tool designed for developers looking to integrate various Large Language Model (LLM) providers into their applications. With a well-structured architecture, it allows for easy addition of new providers, making it a versatile choice for AI enthusiasts.

Key Features of the Instructor Library

• Provider Flexibility: Supports multiple LLM providers with a consistent interface.
• Custom Utility Functions: Allows for tailored response handling and message formatting.
• Easy Integration: Simple setup process for adding new providers.
• Community Contributions: Open-source model encourages collaboration and improvement.

Understanding the Technical Architecture

The architecture of the Instructor library is designed to maintain a clean separation between core functionality and provider-specific implementations. Each provider is organized in its own subdirectory within the providers/ directory, following a structured pattern:

providers/
├── provider_name/
│   ├── __init__.py
│   ├── client.py      # Provider-specific client factory (optional)
│   └── utils.py       # Provider-specific utilities (optional)

This organization allows developers to easily navigate and extend the library.

Setting Up the Instructor Library

To get started with the Instructor library, follow these steps:

Installation

Clone the repository and install the necessary dependencies:

git clone https://github.com/YOUR-USERNAME/instructor.git
cd instructor
uv pip install -e ".[dev,docs]"

Alternatively, you can use Poetry for dependency management:

curl -sSL https://install.python-poetry.org | python3 -
cd instructor
poetry install --with dev,docs

Usage Examples and API Overview

Once installed, you can start using the library to interact with various LLM providers. Here’s a simple example of how to use the OpenAI provider:

import instructor
from openai import OpenAI

# Create the patched client
client = instructor.from_openai(OpenAI())

# Use the model
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Tell me a joke."}
    ]
)
print(response)

This example demonstrates how to create a client and make a request to the OpenAI model.

Contributing to the Instructor Library

The Instructor library thrives on community contributions. Here are some ways you can help:

• Evaluation Tests: Create new evals to monitor model performance.
• Report Issues: If you find a bug, file an issue on GitHub with detailed information.
• Submit Pull Requests: For small changes, submit a PR directly; for larger changes, start with an issue.

For more details, refer to the contributing guidelines.

License and Legal Considerations

The Instructor library is licensed under the MIT License, allowing for free use, modification, and distribution. Ensure to include the original copyright notice in any copies or substantial portions of the software.

Conclusion

The Instructor library provides a robust framework for integrating various LLM providers into your applications. With its flexible architecture and community-driven development, it is an excellent choice for developers looking to enhance their AI capabilities.

For more information, visit the Instructor GitHub Repository.

FAQ

Here are some frequently asked questions about the Instructor library: