# What is CodeRAG?
CodeRAG is a lightning-fast semantic code search library designed for RAG (Retrieval-Augmented Generation) applications. It combines traditional keyword search (TF-IDF/BM25) with optional vector embeddings to provide accurate, context-aware code search results.
## Key Features
### AST-Based Chunking
Unlike traditional search that returns entire files, CodeRAG uses Abstract Syntax Tree (AST) parsing to split code at semantic boundaries:
- **Functions**: Find specific function implementations
- **Classes**: Locate class definitions and methods
- **Imports**: Track module dependencies
- **Comments**: Search documentation blocks
This means search results are more precise and consume fewer tokens when used with LLMs.
### Hybrid Search
CodeRAG supports three search modes:
1. **Keyword Search (TF-IDF/BM25)**: Fast, precise matching using StarCoder2 tokenization
2. **Semantic Search (Vector)**: Meaning-based search using embeddings (requires OpenAI API)
3. **Hybrid Search**: Weighted combination of both for best results
### Performance
| Metric | Value |
|--------|-------|
| Indexing Speed | 1000-2000 files/sec |
| Startup Time | <100ms (cached) |
| Search Latency | <50ms |
| Memory per 1000 files | ~1-2 MB |
### Language Support
CodeRAG supports 15+ programming languages out of the box:
- **JavaScript/TypeScript**: JS, JSX, TS, TSX, MJS, CJS
- **Systems**: Python, Go, Rust, Java, C, C++, Ruby, PHP
- **Markup**: Markdown, HTML, XML
- **Data**: JSON, YAML, TOML, Protobuf
## Use Cases
### AI Assistants
CodeRAG powers AI coding assistants by providing relevant code context:
```typescript
// User asks: "How does authentication work?"
const results = await indexer.search('authentication logic')
// Returns: auth.ts:15-45 (login function), middleware/auth.ts:10-30 (JWT validation)
```
### Code Navigation
Build IDE-like "Go to Definition" features:
```typescript
const results = await indexer.search('function getUserById', {
limit: 1,
fileExtensions: ['.ts'],
})
```
### Documentation Search
Find relevant code examples for documentation:
```typescript
const results = await indexer.search('database connection pool', {
includeContent: true,
contextLines: 5,
})
```
## Architecture
```
┌─────────────────────────────────────────────────┐
│ CodebaseIndexer │
├─────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ │
│ │ TF-IDF │ │ Vector │ (optional) │
│ │ (BM25) │ │ (OpenAI) │ │
│ └──────┬──────┘ └──────┬──────┘ │
│ │ │ │
│ └───────┬────────┘ │
│ ▼ │
│ ┌─────────────────────────────────┐ │
│ │ AST Chunking │ │
│ │ (tree-sitter + synth) │ │
│ └─────────────────────────────────┘ │
├─────────────────────────────────────────────────┤
│ ┌─────────────────────────────────┐ │
│ │ PersistentStorage │ │
│ │ (SQLite) │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────────────────┘
```
## Next Steps
- [Installation](/guide/installation) - Install CodeRAG in your project
- [Quick Start](/guide/quick-start) - Build your first search index
- [MCP Server](/mcp/overview) - Use with AI assistants
