@autodev/codebase

A platform-agnostic code analysis library with semantic search capabilities and MCP (Model Context Protocol) server support. This library provides intelligent code indexing, vector-based semantic search, and can be integrated into various development tools and IDEs.

🚀 Features

• Semantic Code Search: Vector-based code search using embeddings
• MCP Server Support: HTTP-based MCP server for IDE integration
• Terminal UI: Interactive CLI with rich terminal interface
• Tree-sitter Parsing: Advanced code parsing and analysis
• Vector Storage: Qdrant vector database integration
• Flexible Embedding: Support for various embedding models via Ollama

📦 Installation

1. Install and Start Ollama

1# Install Ollama (macOS)
2brew install ollama
3
4# Start Ollama service
5ollama serve
6
7# In a new terminal, pull the embedding model
8ollama pull dengcao/Qwen3-Embedding-0.6B:Q8_0

2. Install ripgrep

ripgrep is required for fast codebase indexing. Install it with:

1# Install ripgrep (macOS)
2brew install ripgrep
3
4# Or on Ubuntu/Debian
5sudo apt-get install ripgrep
6
7# Or on Arch Linux
8sudo pacman -S ripgrep

3. Install and Start Qdrant

Start Qdrant using Docker:

1# Start Qdrant container
2docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

Or download and run Qdrant directly:

1# Download and run Qdrant
2wget https://github.com/qdrant/qdrant/releases/latest/download/qdrant-x86_64-unknown-linux-gnu.tar.gz
3tar -xzf qdrant-x86_64-unknown-linux-gnu.tar.gz
4./qdrant

4. Verify Services Are Running

1# Check Ollama
2curl http://localhost:11434/api/tags
3
4# Check Qdrant
5curl http://localhost:6333/collections

5. Install Autodev-codebase

1npm install -g @autodev/codebase

Alternatively, you can install it locally:

git clone https://github.com/anrgct/autodev-codebase
cd autodev-codebase
npm install
npm run build
npm link

🛠️ Usage

Command Line Interface

The CLI provides two main modes:

1. Interactive TUI Mode (Default)

1# Basic usage: index your current folder as the codebase.
2# Be cautious when running this command if you have a large number of files.
3codebase
4
5
6# With custom options
7codebase --demo # Create a local demo directory and test the indexing service, recommend for setup
8codebase --path=/my/project
9codebase --path=/my/project --log-level=info

2. MCP Server Mode (Recommended for IDE Integration)

1# Start long-running MCP server
2cd /my/project
3codebase mcp-server
4
5# With custom configuration
6codebase mcp-server --port=3001 --host=localhost
7codebase mcp-server --path=/workspace --port=3002

⚙️ Configuration

Configuration Files & Priority

The library uses a layered configuration system, allowing you to customize settings at different levels. The priority order (highest to lowest) is:

1. CLI Parameters (e.g., --model, --ollama-url, --qdrant-url, --config, etc.)
2. Project Config File (./autodev-config.json)
3. Global Config File (~/.autodev-cache/autodev-config.json)
4. Built-in Defaults

Settings specified at a higher level override those at lower levels. This lets you tailor the behavior for your environment or project as needed.

Config file locations:

• Global: ~/.autodev-cache/autodev-config.json
• Project: ./autodev-config.json
• CLI: Pass parameters directly when running commands

Global Configuration

Create a global configuration file at ~/.autodev-cache/autodev-config.json:

1{
2  "isEnabled": true,
3  "embedder": {
4    "provider": "ollama",
5    "model": "dengcao/Qwen3-Embedding-0.6B:Q8_0",
6    "dimension": 1024,
7    "baseUrl": "http://localhost:11434"
8  },
9  "qdrantUrl": "http://localhost:6333",
10  "qdrantApiKey": "your-api-key-if-needed",
11  "searchMinScore": 0.4
12}

Project Configuration

Create a project-specific configuration file at ./autodev-config.json:

1{
2  "embedder": {
3    "provider": "openai-compatible",
4    "apiKey": "sk-xxxxx",
5    "baseUrl": "http://localhost:2302/v1",
6    "model": "openai/text-embedding-3-smallnpm",
7    "dimension": 1536,
8  },
9  "qdrantUrl": "http://localhost:6334"
10}

Configuration Options

Option	Type	Description	Default
isEnabled	boolean	Enable/disable code indexing feature	true
embedder.provider	string	Embedding provider (ollama, openai, openai-compatible)	ollama
embedder.model	string	Embedding model name	dengcao/Qwen3-Embedding-0.6B:Q8_0
embedder.dimension	number	Vector dimension size	1024
embedder.baseUrl	string	Provider API base URL	http://localhost:11434
embedder.apiKey	string	API key (for OpenAI/compatible providers)	-
qdrantUrl	string	Qdrant vector database URL	http://localhost:6333
qdrantApiKey	string	Qdrant API key (if authentication enabled)	-
searchMinScore	number	Minimum similarity score for search results	0.4

Note: The isConfigured field is automatically calculated based on the completeness of your configuration and should not be set manually. The system will determine if the configuration is valid based on the required fields for your chosen provider.

Configuration Priority Examples

1# Use global config defaults
2codebase
3
4# Override model via CLI (highest priority)
5codebase --model="custom-model"
6
7# Use project config with CLI overrides
8codebase --config=./my-config.json --qdrant-url=http://remote:6333

🔧 CLI Options

Global Options

• --path=<path> - Workspace path (default: current directory)
• --demo - Create demo files in workspace
• --force - ignore cache force re-index
• --ollama-url=<url> - Ollama API URL (default: http://localhost:11434)
• --qdrant-url=<url> - Qdrant vector DB URL (default: http://localhost:6333)
• --model=<model> - Embedding model (default: nomic-embed-text)
• --config=<path> - Config file path
• --storage=<path> - Storage directory path
• --cache=<path> - Cache directory path
• --log-level=<level> - Log level: error|warn|info|debug (default: error)
• --log-level=<level> - Log level: error|warn|info|debug (default: error)
• --help, -h - Show help

MCP Server Options

• --port=<port> - HTTP server port (default: 3001)
• --host=<host> - HTTP server host (default: localhost)

IDE Integration (Cursor/Claude)

Configure your IDE to connect to the MCP server:

1{
2  "mcpServers": {
3    "codebase": {
4      "url": "http://localhost:3001/sse"
5    }
6  }
7}

For clients that do not support SSE MCP, you can use the following configuration:

1{
2  "mcpServers": {
3    "codebase": {
4      "command": "codebase",
5      "args": [
6        "stdio-adapter",
7        "--server-url=http://localhost:3001/sse"
8      ]
9    }
10  }
11}

🌐 MCP Server Features

Web Interface

• Home Page: http://localhost:3001 - Server status and configuration
• Health Check: http://localhost:3001/health - JSON status endpoint
• MCP Endpoint: http://localhost:3001/sse - SSE/HTTP MCP protocol endpoint

Available MCP Tools

• search_codebase - Semantic search through your codebase
  • Parameters: query (string), limit (number), filters (object)
  • Returns: Formatted search results with file paths, scores, and code blocks

Scripts

1# Development mode with demo files
2npm run dev
3
4# Build for production
5npm run build
6
7# Type checking
8npm run type-check
9
10# Run TUI demo
11npm run demo-tui
12
13# Start MCP server demo
14npm run mcp-server

Embedding Models PK

Mainstream Embedding Models Performance

Model	Dimension	Avg Precision@3	Avg Precision@5	Good Queries (≥66.7%)	Failed Queries (0%)
siliconflow/Qwen/Qwen3-Embedding-8B	4096	76.7%	66.0%	5/10	0/10
siliconflow/Qwen/Qwen3-Embedding-4B	2560	73.3%	54.0%	5/10	1/10
voyage/voyage-code-3	1024	73.3%	52.0%	6/10	1/10
siliconflow/Qwen/Qwen3-Embedding-0.6B	1024	63.3%	42.0%	4/10	1/10
morph-embedding-v2	1536	56.7%	44.0%	3/10	1/10
openai/text-embedding-ada-002	1536	53.3%	38.0%	2/10	1/10
voyage/voyage-3-large	1024	53.3%	42.0%	3/10	2/10
openai/text-embedding-3-large	3072	46.7%	38.0%	1/10	3/10
voyage/voyage-3.5	1024	43.3%	38.0%	1/10	2/10
voyage/voyage-3.5-lite	1024	36.7%	28.0%	1/10	2/10
openai/text-embedding-3-small	1536	33.3%	28.0%	1/10	4/10
siliconflow/BAAI/bge-large-en-v1.5	1024	30.0%	28.0%	0/10	3/10
siliconflow/Pro/BAAI/bge-m3	1024	26.7%	24.0%	0/10	2/10
ollama/nomic-embed-text	768	16.7%	18.0%	0/10	6/10
siliconflow/netease-youdao/bce-embedding-base_v1	1024	13.3%	16.0%	0/10	6/10

---

Ollama-based Embedding Models Performance

Model	Dimension	Precision@3	Precision@5	Good Queries (≥66.7%)	Failed Queries (0%)
ollama/dengcao/Qwen3-Embedding-4B:Q4_K_M	2560	66.7%	48.0%	4/10	1/10
ollama/dengcao/Qwen3-Embedding-0.6B:f16	1024	63.3%	44.0%	3/10	0/10
ollama/dengcao/Qwen3-Embedding-0.6B:Q8_0	1024	63.3%	44.0%	3/10	0/10
ollama/dengcao/Qwen3-Embedding-4B:Q8_0	2560	60.0%	48.0%	3/10	1/10
lmstudio/taylor-jones/bge-code-v1-Q8_0-GGUF	1536	60.0%	54.0%	4/10	1/10
ollama/dengcao/Qwen3-Embedding-8B:Q4_K_M	4096	56.7%	42.0%	2/10	2/10
ollama/hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M	3584	53.3%	44.0%	2/10	0/10
ollama/bge-m3:f16	1024	26.7%	24.0%	0/10	2/10
ollama/hf.co/nomic-ai/nomic-embed-text-v2-moe-GGUF:f16	768	26.7%	20.0%	0/10	2/10
ollama/granite-embedding:278m-fp16	768	23.3%	18.0%	0/10	4/10
ollama/unclemusclez/jina-embeddings-v2-base-code:f16	768	23.3%	16.0%	0/10	5/10
lmstudio/awhiteside/CodeRankEmbed-Q8_0-GGUF	768	23.3%	16.0%	0/10	5/10
lmstudio/wsxiaoys/jina-embeddings-v2-base-code-Q8_0-GGUF	768	23.3%	16.0%	0/10	5/10
ollama/dengcao/Dmeta-embedding-zh:F16	768	20.0%	20.0%	0/10	6/10
ollama/znbang/bge:small-en-v1.5-q8_0	384	16.7%	16.0%	0/10	6/10
lmstudio/nomic-ai/nomic-embed-text-v1.5-GGUF@Q4_K_M	768	16.7%	14.0%	0/10	6/10
ollama/nomic-embed-text:f16	768	16.7%	18.0%	0/10	6/10
ollama/snowflake-arctic-embed2:568m:f16	1024	16.7%	18.0%	0/10	5/10