LEANN AI技能包

📦 Prerequisites: Install uv

Install uv first if you don't have it. Typically, you can install it with:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install C++ dependencies via vcpkg

vcpkg install zeromq:x64-windows openblas:x64-windows lapack:x64-windows ` boost-program-options:x64-windows protobuf:x64-windows

Install dependencies

uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn brew install poppler # macOS only, for PDF processing

#### Build Index

#### Search

#### Models - ColQwen2 (colqwen2): Latest vision-language model with improved performance - ColPali (colpali): Proven multimodal retriever

For detailed usage, see the ColQwen Guide.

</details>

Basic usage (requires Full Disk Access)

python -m apps.imessage_rag

- Requires confirmation for cross-project removal

Installation

🚀 Quick Install

Clone the repository to access all examples and try amazing applications,

git clone https://github.com/yichuan-w/LEANN.git leann
cd leann

and install LEANN from PyPI to run them immediately:

```bash uv venv source .venv/bin/activate uv pip install leann

Install toolchain (if not already present)

choco install cmake swig pkgconfiglite nuget.commandline -y

Build an index

builder = LeannBuilder(backend_name="hnsw") builder.add_text("LEANN saves 97% storage compared to traditional vector databases.") builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back") builder.build_index(INDEX_PATH)

Generation Model Setup

LLM Backend

LEANN supports many LLM providers for text generation (HuggingFace, Ollama, Anthropic, and Any OpenAI compatible API).

<details> <summary><strong>🔑 OpenAI API Setup (Default)</strong></summary>

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY="your-api-key-here"

Make sure to use --llm openai flag when using the CLI. You can also specify the model name with --llm-model <model-name> flag.

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<details> <summary><strong>🛠️ Supported LLM & Embedding Providers (via OpenAI Compatibility)</strong></summary>

Thanks to the widespread adoption of the OpenAI API format, LEANN is compatible out-of-the-box with a vast array of LLM and embedding providers. Simply set the OPENAI_BASE_URL and OPENAI_API_KEY environment variables to connect to your preferred service.

export OPENAI_API_KEY="xxx"
export OPENAI_BASE_URL="http://localhost:1234/v1" # base url of the provider

To use OpenAI compatible endpoint with the CLI interface:

If you are using it for text generation, make sure to use --llm openai flag and specify the model name with --llm-model <model-name> flag.

If you are using it for embedding, set the --embedding-mode openai flag and specify the model name with --embedding-model <MODEL>.

-----

Below is a list of base URLs for common providers to get you started.

Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Index Building Parameters

--backend-name NAME # Backend to use: hnsw or diskann (default: hnsw) --graph-degree N # Graph degree for index construction (default: 32) --build-complexity N # Build complexity for index construction (default: 64) --compact / --no-compact # Use compact storage (default: true). Must be no-compact for no-recompute build. --recompute / --no-recompute # Enable/disable embedding recomputation (default: enabled). Should not do a no-recompute search in a recompute build. ```

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Build index from PDFs

python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers

Install LEANN globally for MCP integration

uv tool install leann-core --with leann claude mcp add --scope user leann-server -- leann_mcp

Setup is automatic - just start using Claude Code!

``` Try our fully agentic pipeline with auto query rewriting, semantic search planning, and more:

🔥 Ready to supercharge your coding? Complete Setup Guide →

Installation

If you followed the Quick Start, leann is already installed in your virtual environment:

source .venv/bin/activate
leann --help

To make it globally available: ```bash

Install the LEANN CLI globally using uv tool

uv tool install leann-core --with leann

build from a specific directory, and my_docs is the index name(Here you can also build from multiple dict or multiple files)

leann build my-docs --docs ./your_documents

Detect file changes since last build/watch checkpoint

leann watch my-docs

Quick Start

Our declarative API makes RAG as easy as writing a config file.

Check out demo.ipynb or

```python from leann import LeannBuilder, LeannSearcher, LeannChat from pathlib import Path INDEX_PATH = str(Path("./").resolve() / "demo.leann")

Core Parameters (General preprocessing for all examples)

--index-dir DIR # Directory to store the index (default: current directory) --query "YOUR QUESTION" # Single query mode. Omit for interactive chat (type 'quit' to exit), and now you can play with your index interactively --max-items N # Limit data preprocessing (default: -1, process all data) --force-rebuild # Force rebuild index even if it exists

Basic usage with HTML export

python -m apps.chatgpt_rag --export-path conversations.html

Basic usage with JSON export

python -m apps.claude_rag --export-path my_claude_conversations.json

Usage Examples

```bash

Example output:

Set environment variables (adjust VCPKG_ROOT to your vcpkg path)

$env:CMAKE_PREFIX_PATH = "$env:VCPKG_ROOT\installed\x64-windows" $env:PKG_CONFIG_PATH = "$env:VCPKG_ROOT\installed\x64-windows\lib\pkgconfig" $env:PKG_CONFIG_EXECUTABLE = "C:\ProgramData\chocolatey\bin\pkg-config.exe" $env:OPENBLAS_LIB = "$env:VCPKG_ROOT\installed\x64-windows\lib\openblas.lib" $env:PATH += ";$env:VCPKG_ROOT\installed\x64-windows\bin" $env:PATH += ";$env:VCPKG_ROOT\installed\x64-windows\tools\protobuf"

uv sync --extra diskann ```

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⭐ Flexible Configuration

LEANN provides flexible parameters for embedding models, search strategies, and data processing to fit your specific needs.

📚 Need configuration best practices? Check our Configuration Guide for detailed optimization tips, model selection advice, and solutions to common issues like slow embeddings or poor search quality.

<details> <summary><strong>📋 Click to expand: Common Parameters (Available in All Examples)</strong></summary>

All RAG examples share these common parameters. Interactive mode is available in all examples - simply run without --query to start a continuous Q&A session where you can ask multiple questions. Type 'quit' to exit.

```bash

Environment Variables (GPU Device Selection)

LEANN_EMBEDDING_DEVICE # GPU for embedding model (e.g., cuda:0, cuda:1, cpu) LEANN_LLM_DEVICE # GPU for HFChat LLM (e.g., cuda:1, or "cuda" for multi-GPU auto)

Now you can use leann from anywhere without activating venv

leann --help ```

Note: Global installation is required for Claude Code integration. The leann_mcp server depends on the globally available leann command.

Command Line Interface

LEANN includes a powerful CLI for document processing and search. Perfect for quick document indexing and interactive chat.

- Supports both CLI and app-created indexes

```

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MCP Integration: RAG on Live Data from Any Platform

Connect to live data sources through the Model Context Protocol (MCP). LEANN now supports real-time RAG on platforms like Slack, Twitter, and more through standardized MCP servers.

Key Benefits: - Live Data Access: Fetch real-time data without manual exports - Standardized Protocol: Use any MCP-compatible server - Easy Extension: Add new platforms with minimal code - Secure Access: MCP servers handle authentication

💬 Slack Messages: Search Your Team Conversations

Transform your Slack workspace into a searchable knowledge base! Find discussions, decisions, and shared knowledge across all your channels.

```bash

🚀 Claude Code Integration: Transform Your Development Workflow!

<details> <summary><strong>AST‑Aware Code Chunking</strong></summary>

LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.

📖 Read the AST Chunking Guide →

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The future of code assistance is here. Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE.

Key features: - 🔍 Semantic code search across your entire project, fully local index and lightweight - 🧠 AST-aware chunking preserves code structure (functions, classes) - 📚 Context-aware assistance for debugging and development - 🚀 Zero-config setup with automatic language detection

```bash

Compares the current file system state against the last checkpoint (Merkle tree snapshot)

- Each subsequent run compares against the most recent checkpoint

📊 Storage Comparison

Interactive Q&A

python -m apps.colqwen_rag ask research_papers --interactive

<details>
<summary><strong>📋 Click to expand: ColQwen Setup & Usage</strong></summary>

#### Prerequisites

Ask a single question (non-interactive)

leann ask my-docs "Where are prompts configured?"

❓ [FAQ →](docs/faq.md)