Package Exports

github-llm-context-generator
github-llm-context-generator/src/index.js

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Readme

GitHub Context Generator

Generate LLM-ready context from GitHub repositories for AI-assisted coding. Extract code patterns, implementations, and documentation to provide rich context for Large Language Models like Gemini, GPT-4, and Claude.

Features

🔍 Smart Code Extraction - Parse JavaScript, TypeScript, Python, Java, and more
🎯 Targeted Path Loading - Load specific directories or files from repositories
📊 Knowledge Graph Generation - Build a queryable graph of code relationships
🤖 LLM-Optimized Output - Generate markdown context perfect for AI consumption
🚀 Pattern Recognition - Find implementation patterns and code examples
💾 Intelligent Caching - Cache repository data for faster subsequent queries
🔗 Multi-Repository Support - Combine context from multiple sources

Installation

npm install github-llm-context-generator

Quick Start

const GitHubContextGenerator = require('github-llm-context-generator');

// Initialize the generator
const generator = new GitHubContextGenerator({
  cacheEnabled: true,
  localStoragePath: './repos'
});

// Load a repository
const repos = await generator.loadRepositories([
  'https://github.com/user/repo'
]);

// Generate context for your LLM
const context = await generator.getContext('authentication implementation', {
  format: 'markdown',
  maxFiles: 10,
  includeFullFiles: true
});

console.log(context); // Markdown-formatted context ready for LLM

Advanced Usage

Loading Specific Paths

Target specific directories or files within repositories:

// Load only specific paths from a repository
const repos = await generator.loadRepositories([{
  url: 'https://github.com/google/adk-samples',
  branch: 'main',
  paths: [
    'python/agents/data-science',
    'python/agents/RAG',
    'README.md'
  ]
}]);

Natural Language Queries

Find relevant code using natural language:

// Query for specific patterns
const results = await generator.query('how to implement async API calls');

// Get context for a specific coding task
const context = await generator.getContext('websocket event handling', {
  format: 'markdown',
  maxNodes: 100,
  maxFiles: 20,
  maxCodeLength: 50000
});

Vibe Coding Support

Perfect for "vibe coding" - giving LLMs the context they need to generate code that matches your codebase style:

// Get comprehensive context about your codebase patterns
const vibeContext = await generator.getContext('', {
  format: 'markdown',
  maxNodes: 200,
  maxFiles: 30,
  includeFullFiles: false // Smart extraction of relevant sections
});

// Save context for LLM consumption
const fs = require('fs').promises;
await fs.writeFile('context.md', vibeContext);

API Reference

Constructor Options

new GitHubContextGenerator({
  cacheEnabled: true,        // Enable caching (default: true)
  cacheTTL: 3600000,         // Cache TTL in ms (default: 1 hour)
  rateLimitDelay: 1000,      // Delay between API calls in ms
  localStoragePath: './repos' // Where to clone repositories
})

Main Methods

`loadRepositories(repositories)`

Load one or more repositories into the knowledge graph.

// Simple format
await generator.loadRepositories(['https://github.com/user/repo']);

// Advanced format with specific paths
await generator.loadRepositories([{
  url: 'https://github.com/user/repo',
  branch: 'main',
  paths: ['src', 'docs/api.md']
}]);

`query(naturalLanguageQuery)`

Search the knowledge graph using natural language.

const results = await generator.query('find all React components');

`getContext(query, options)`

Generate LLM-ready context based on a query.

Options:

format: 'markdown' | 'text' | 'structured' (default: 'structured')
maxNodes: Maximum number of graph nodes to include (default: 20)
maxFiles: Maximum number of files to include (default: 10)
maxCodeLength: Maximum characters of code per file (default: 5000)
includeFullFiles: Whether to include complete file contents (default: true)

const context = await generator.getContext('authentication', {
  format: 'markdown',
  maxFiles: 15,
  includeFullFiles: true
});

`getStats()`

Get statistics about the loaded knowledge graph.

const stats = generator.getStats();
console.log(`Nodes: ${stats.nodes}, Edges: ${stats.edges}`);

Examples

Example 1: Finding Implementation Patterns

const generator = new GitHubContextGenerator();

// Load a repository
await generator.loadRepositories(['https://github.com/expressjs/express']);

// Find middleware patterns
const middlewareContext = await generator.getContext('middleware implementation', {
  format: 'markdown',
  maxFiles: 10
});

// Use with your LLM
const prompt = `Based on this context:\n${middlewareContext}\n\nCreate a new middleware that logs request duration.`;

Example 2: Multi-Repository Context

// Load examples from multiple repositories
await generator.loadRepositories([
  {
    url: 'https://github.com/vercel/next.js',
    paths: ['examples/with-typescript', 'docs']
  },
  {
    url: 'https://github.com/facebook/react',
    paths: ['packages/react/src']
  }
]);

// Generate comprehensive context
const context = await generator.getContext('component lifecycle hooks', {
  format: 'markdown',
  maxFiles: 20
});

Example 3: Targeted Documentation Context

// Load only documentation and examples
await generator.loadRepositories([{
  url: 'https://github.com/user/project',
  paths: ['README.md', 'docs/', 'examples/']
}]);

// Get documentation-focused context
const docsContext = await generator.getContext('API usage examples', {
  format: 'markdown',
  includeFullFiles: true
});

Use Cases

AI-Assisted Development: Provide context to LLMs for generating compatible code
Code Analysis: Understand patterns and implementations across repositories
Documentation Generation: Extract and organize code documentation
Learning: Study how specific features are implemented in real projects
Migration: Understand existing code patterns when migrating or refactoring
Code Review: Generate context for understanding large pull requests

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Built with simple-git for repository management
Uses natural for NLP processing
Inspired by the need for better LLM context in "vibe coding"

Support

Made with ❤️ for the AI-assisted development community