@autodev/codebase
╭─ ~/workspace/autodev-codebase
╰─❯ codebase --demo --search="user manage"
Found 3 results in 2 files for: "user manage"
==================================================
File: "hello.js"
==================================================
< class UserManager > (L7-20)
class UserManager {
constructor() {
this.users = [];
}
addUser(user) {
this.users.push(user);
console.log('User added:', user.name);
}
getUsers() {
return this.users;
}
}
==================================================
File: "README.md" | 2 snippets
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< md_h1 Demo Project > md_h2 Usage > md_h3 JavaScript Functions > (L16-20)
### JavaScript Functions
- greetUser(name) - Greets a user by name
- UserManager - Class for managing user data
─────
< md_h1 Demo Project > md_h2 Search Examples > (L27-38)
## Search Examples
Try searching for:
- "greet user"
- "process data"
- "user management"
- "batch processing"
- "YOLO model"
- "computer vision"
- "object detection"
- "model training"
A vector embedding-based code semantic search tool with MCP server and multi-model integration. Can be used as a pure CLI tool. Supports Ollama for fully local embedding and reranking, enabling complete offline operation and privacy protection for your code repository.
🚀 Features
🔍 Semantic Code Search: Vector-based search using advanced embedding models
🌐 MCP Server: HTTP-based MCP server with SSE and stdio adapters
💻 Pure CLI Tool: Standalone command-line interface without GUI dependencies
⚙️ Layered Configuration: CLI, project, and global config management
🎯 Advanced Path Filtering: Glob patterns with brace expansion and exclusions
🌲 Tree-sitter Parsing: Support for 40+ programming languages
💾 Qdrant Integration: High-performance vector database
🔄 Multiple Providers: OpenAI, Ollama, Jina, Gemini, Mistral, OpenRouter, Vercel
📊 Real-time Watching: Automatic index updates
⚡ Batch Processing: Efficient parallel processing
Related MCP server: code-index-mcp
📦 Installation
1. Dependencies
brew install ollama ripgrep
ollama serve
ollama pull nomic-embed-text
2. Qdrant
docker run -d -p 6333:6333 -p 6334:6334 --name qdrant qdrant/qdrant
3. Install
npm install -g @autodev/codebase
codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text
🛠️ Quick Start
# Demo mode (recommended for first-time)
# Creates a demo directory in current working directory for testing
# Index & search
codebase --demo --index
codebase --demo --search="user greet"
# MCP server
codebase --demo --serve
📋 Commands
Indexing & Search
# Index the codebase
codebase --index --path=/my/project --force
# Search with filters
codebase --search="error handling" --path-filters="src/**/*.ts"
# Search with custom limit and minimum score
codebase --search="authentication" --limit=20 --min-score=0.7
codebase --search="API" -l 30 -s 0.5
# Search in JSON format
codebase --search="authentication" --json
# Clear index data
codebase --clear --path=/my/project
MCP Server
# HTTP mode (recommended)
codebase --serve --port=3001 --path=/my/project
# Stdio adapter
codebase --stdio-adapter --server-url=http://localhost:3001/mcp
Configuration
# View config
codebase --get-config
codebase --get-config embedderProvider --json
# Set config
codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text
codebase --set-config --global qdrantUrl=http://localhost:6333
Advanced Features
🔍 LLM-Powered Search Reranking
Enable LLM reranking to dramatically improve search relevance:
# Enable reranking with Ollama (recommended)
codebase --set-config rerankerEnabled=true,rerankerProvider=ollama,rerankerOllamaModelId=qwen3-vl:4b-instruct
# Or use OpenAI-compatible providers
codebase --set-config rerankerEnabled=true,rerankerProvider=openai-compatible,rerankerOpenAiCompatibleModelId=deepseek-chat
# Search with automatic reranking
codebase --search="user authentication" # Results are automatically reranked by LLM
Benefits:
🎯 Higher precision: LLM understands semantic relevance beyond vector similarity
📊 Smart scoring: Results are reranked on a 0-10 scale based on query relevance
⚡ Batch processing: Efficiently handles large result sets with configurable batch sizes
🎛️ Threshold control: Filter results with rerankerMinScore to keep only high-quality matches
Path Filtering & Export
# Path filtering with brace expansion and exclusions
codebase --search="API" --path-filters="src/**/*.ts,lib/**/*.js"
codebase --search="utils" --path-filters="{src,test}/**/*.ts"
# Export results in JSON format for scripts
codebase --search="auth" --json
⚙️ Configuration
Config Layers (Priority Order)
CLI Arguments - Runtime parameters (--path, --config, --log-level, --force, etc.)
Project Config - ./autodev-config.json (or custom path via --config)
Global Config - ~/.autodev-cache/autodev-config.json
Built-in Defaults - Fallback values
Note: CLI arguments provide runtime override for paths, logging, and operational behavior. For persistent configuration (embedderProvider, API keys, search parameters), use --set-config to save to config files.
Common Config Examples
Ollama:
{
"embedderProvider": "ollama",
"embedderModelId": "nomic-embed-text",
"qdrantUrl": "http://localhost:6333"
}
OpenAI:
{
"embedderProvider": "openai",
"embedderModelId": "text-embedding-3-small",
"embedderOpenAiApiKey": "sk-your-key",
"qdrantUrl": "http://localhost:6333"
}
OpenAI-Compatible:
{
"embedderProvider": "openai-compatible",
"embedderModelId": "text-embedding-3-small",
"embedderOpenAiCompatibleApiKey": "sk-your-key",
"embedderOpenAiCompatibleBaseUrl": "https://api.openai.com/v1"
}
Key Configuration Options
Category | Options | Description |
Embedding | embedderProvider
, embedderModelId
, embedderModelDimension
| Provider and model settings |
API Keys | embedderOpenAiApiKey
, embedderOpenAiCompatibleApiKey
| Authentication |
Vector Store | qdrantUrl
, qdrantApiKey
| Qdrant connection |
Search | vectorSearchMinScore
, vectorSearchMaxResults
| Search behavior |
Reranker | rerankerEnabled
, rerankerProvider
| Result reranking |
Key CLI Arguments:
--serve / --index / --search - Core operations
--get-config / --set-config - Configuration management
--path, --demo, --force - Common options
--limit / -l <number> - Maximum number of search results (default: from config, max 50)
--min-score / -s <number> - Minimum similarity score for search results (0-1, default: from config)
--help - Show all available options
For complete CLI reference, see CONFIG.md.
Configuration Commands:
# View config
codebase --get-config
codebase --get-config --json
# Set config (saves to file)
codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text
codebase --set-config --global embedderProvider=openai,embedderOpenAiApiKey=sk-xxx
# Use custom config file
codebase --config=/path/to/config.json --get-config
codebase --config=/path/to/config.json --set-config embedderProvider=ollama
# Runtime override (paths, logging, etc.)
codebase --index --path=/my/project --log-level=info --force
For complete configuration reference, see CONFIG.md.
🔌 MCP Integration
HTTP Streamable Mode (Recommended)
codebase --serve --port=3001
IDE Config:
{
"mcpServers": {
"codebase": {
"url": "http://localhost:3001/mcp"
}
}
}
Stdio Adapter
# First start the MCP server in one terminal
codebase --serve --port=3001
# Then connect via stdio adapter in another terminal (for IDEs that require stdio)
codebase --stdio-adapter --server-url=http://localhost:3001/mcp
IDE Config:
{
"mcpServers": {
"codebase": {
"command": "codebase",
"args": ["stdio-adapter", "--server-url=http://localhost:3001/mcp"]
}
}
}
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request or open an Issue on GitHub.
📄 License
This project is licensed under the MIT License.
🙏 Acknowledgments
This project is a fork and derivative work based on Roo Code. We've built upon their excellent foundation to create this specialized codebase analysis tool with enhanced features and MCP server capabilities.
🌟 If you find this tool helpful, please give us a
Made with ❤️ for the developer community
