Llama-github is a Python library designed to enable LLM Chatbots, AI Agents, and Auto-dev Solutions to perform Agentic Retrieval Augmented Generation (RAG) on public GitHub repositories. It retrieves relevant code snippets, issues, and repository information to generate context for coding questions, aiming to streamline AI-driven development.

How It Works

The library employs an LLM-powered question analysis to break down complex queries and generate effective search strategies. It then retrieves highly relevant information from GitHub using advanced techniques and caches repositories across threads to improve efficiency and reduce API token consumption. Finally, it generates comprehensive, contextually relevant answers by combining retrieved GitHub data with LLM reasoning. The architecture is built with asynchronous programming for concurrent request handling and offers flexible LLM integration.

Quick Start & Requirements

• Install via pip: pip install llama-github
• Requires GitHub Personal Access Token or GitHub App authentication.
• Requires OpenAI API key (optional in simple mode).
• Jina API key is optional but recommended for high concurrency production deployment.
• See documentation for advanced usage: https://deepwiki.com/JetXu-LLM/llama-github

Highlighted Details

• Intelligent GitHub retrieval for code snippets, issues, and repo info.
• Repository pool caching for accelerated retrieval and reduced API token usage.
• LLM-powered question analysis for optimized search strategies.
• Asynchronous processing for concurrent request handling.
• Flexible LLM and embedding model integration.

Maintenance & Community

• Project roadmap available at: https://deepwiki.com/JetXu-LLM/llama-github
• Mentions contributions from LangChain and Jina.ai.
• Open to contributions via contributing guidelines.

Licensing & Compatibility

• Licensed under the Apache 2.0 license.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

In professional mode, context generation for a single query can take approximately one minute. The library relies on external API keys for OpenAI and optionally Jina.