The smallest vector index in the world. RAG Everything with LEANN!

LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using 97% less storage than traditional solutions without accuracy loss.

LEANN achieves this through graph-based selective recomputation with high-degree preserving pruning, computing embeddings on-demand instead of storing them all. Illustration Fig → | Paper →

Ready to RAG Everything? Transform your laptop into a personal AI assistant that can search your file system, emails, browser history, chat history, or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.

Why LEANN?

The numbers speak for themselves: Index 60 million Wikipedia chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. See detailed benchmarks for different applications below ↓

🔒 Privacy: Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".

🪶 Lightweight: Graph-based recomputation eliminates heavy embedding storage, while smart graph pruning and CSR format minimize graph storage overhead. Always less storage, less memory usage!

📈 Scalability: Handle messy personal data that would crash traditional vector DBs, easily managing your growing personalized data and agent generated memory!

✨ No Accuracy Loss: Maintain the same search quality as heavyweight solutions while using 97% less storage.

Installation

git clone git@github.com:yichuan-w/LEANN.git leann
cd leann
git submodule update --init --recursive

macOS:

brew install llvm libomp boost protobuf zeromq pkgconf

# Install with HNSW backend (default, recommended for most users)
# Install uv first if you don't have it:
# curl -LsSf https://astral.sh/uv/install.sh | sh
# See: https://docs.astral.sh/uv/getting-started/installation/#installation-methods
CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ uv sync

Linux:

sudo apt-get install libomp-dev libboost-all-dev protobuf-compiler libabsl-dev libmkl-full-dev libaio-dev libzmq3-dev

# Install with HNSW backend (default, recommended for most users)
uv sync

Ollama Setup (Recommended for full privacy):

You can skip this installation if you only want to use OpenAI API for generation.

macOS:

First, download Ollama for macOS.

# Pull a lightweight model (recommended for consumer hardware)
ollama pull llama3.2:1b

Linux:

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

# Start Ollama service manually
ollama serve &

# Pull a lightweight model (recommended for consumer hardware)
ollama pull llama3.2:1b

Quick Start in 30s

Our declarative API makes RAG as easy as writing a config file. Try in this ipynb file →

from leann.api import LeannBuilder, LeannSearcher, LeannChat

# 1. Build the index (no embeddings stored!)
builder = LeannBuilder(backend_name="hnsw")
builder.add_text("C# is a powerful programming language")
builder.add_text("Python is a powerful programming language and it is very popular")
builder.add_text("Machine learning transforms industries")
builder.add_text("Neural networks process complex data")
builder.add_text("Leann is a great storage saving engine for RAG on your MacBook")
builder.build_index("knowledge.leann")

# 2. Search with real-time embeddings
searcher = LeannSearcher("knowledge.leann")
results = searcher.search("programming languages", top_k=2)

# 3. Chat with LEANN using retrieved results
llm_config = {
    "type": "ollama",
    "model": "llama3.2:1b"
}

chat = LeannChat(index_path="knowledge.leann", llm_config=llm_config)
response = chat.ask(
    "Compare the two retrieved programming languages and say which one is more popular today.",
    top_k=2,
)

RAG on Everything!

LEANN supports RAG on various data sources including documents (.pdf, .txt, .md), Apple Mail, Google Search History, WeChat, and more.

📄 Personal Data Manager: Process Any Documents (.pdf, .txt, .md)!

Ask questions directly about your personal PDFs, documents, and any directory containing your files!

The example below asks a question about summarizing two papers (uses default data in examples/data):

# Drop your PDFs, .txt, .md files into examples/data/
uv run ./examples/main_cli_example.py

# Or use python directly
source .venv/bin/activate
python ./examples/main_cli_example.py

📧 Your Personal Email Secretary: RAG on Apple Mail!

Note: You need to grant full disk access to your terminal/VS Code in System Preferences → Privacy & Security → Full Disk Access.

python examples/mail_reader_leann.py --query "What's the food I ordered by doordash or Uber eat mostly?"

780K email chunks → 78MB storage Finally, search your email like you search Google.

📋 Click to expand: Command Examples

# Use default mail path (works for most macOS setups)
python examples/mail_reader_leann.py

# Run with custom index directory
python examples/mail_reader_leann.py --index-dir "./my_mail_index"

# Process all emails (may take time but indexes everything)
python examples/mail_reader_leann.py --max-emails -1

# Limit number of emails processed (useful for testing)
python examples/mail_reader_leann.py --max-emails 1000

# Run a single query
python examples/mail_reader_leann.py --query "What did my boss say about deadlines?"

📋 Click to expand: Example queries you can try

Once the index is built, you can ask questions like:

• "Find emails from my boss about deadlines"
• "What did John say about the project timeline?"
• "Show me emails about travel expenses"

🔍 Time Machine for the Web: RAG Your Entire Google Browser History!

python examples/google_history_reader_leann.py --query "Tell me my browser history about machine learning?"

38K browser entries → 6MB storage. Your browser history becomes your personal search engine.

📋 Click to expand: Command Examples

# Use default Chrome profile (auto-finds all profiles)
python examples/google_history_reader_leann.py

# Run with custom index directory
python examples/google_history_reader_leann.py --index-dir "./my_chrome_index"

# Limit number of history entries processed (useful for testing)
python examples/google_history_reader_leann.py --max-entries 500

# Run a single query
python examples/google_history_reader_leann.py --query "What websites did I visit about machine learning?"

📋 Click to expand: How to find your Chrome profile

The default Chrome profile path is configured for a typical macOS setup. If you need to find your specific Chrome profile:

1. Open Terminal
2. Run: ls ~/Library/Application\ Support/Google/Chrome/
3. Look for folders like "Default", "Profile 1", "Profile 2", etc.
4. Use the full path as your --chrome-profile argument

Common Chrome profile locations:

• macOS: ~/Library/Application Support/Google/Chrome/Default
• Linux: ~/.config/google-chrome/Default

💬 Click to expand: Example queries you can try

Once the index is built, you can ask questions like:

• "What websites did I visit about machine learning?"
• "Find my search history about programming"
• "What YouTube videos did I watch recently?"
• "Show me websites I visited about travel planning"

💬 WeChat Detective: Unlock Your Golden Memories!

python examples/wechat_history_reader_leann.py --query "Show me all group chats about weekend plans"

400K messages → 64MB storage Search years of chat history in any language.

🔧 Click to expand: Installation Requirements

First, you need to install the WeChat exporter:

sudo packages/wechat-exporter/wechattweak-cli install

Troubleshooting:

• Installation issues: Check the WeChatTweak-CLI issues page
• Export errors: If you encounter the error below, try restarting WeChat

Failed to export WeChat data. Please ensure WeChat is running and WeChatTweak is installed.
Failed to find or export WeChat data. Exiting.

📋 Click to expand: Command Examples

# Use default settings (recommended for first run)
python examples/wechat_history_reader_leann.py

# Run with custom export directory and wehn we run the first time, LEANN will export all chat history automatically for you
python examples/wechat_history_reader_leann.py --export-dir "./my_wechat_exports"

# Run with custom index directory
python examples/wechat_history_reader_leann.py --index-dir "./my_wechat_index"

# Limit number of chat entries processed (useful for testing)
python examples/wechat_history_reader_leann.py --max-entries 1000

# Run a single query
python examples/wechat_history_reader_leann.py --query "Show me conversations about travel plans"

💬 Click to expand: Example queries you can try

Once the index is built, you can ask questions like:

• "我想买魔术师约翰逊的球衣，给我一些对应聊天记录?" (Chinese: Show me chat records about buying Magic Johnson's jersey)

🖥️ Command Line Interface

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

# Build an index from documents
leann build my-docs --docs ./documents

# Search your documents  
leann search my-docs "machine learning concepts"

# Interactive chat with your documents
leann ask my-docs --interactive

# List all your indexes
leann list

Key CLI features:

• Auto-detects document formats (PDF, TXT, MD, DOCX)
• Smart text chunking with overlap
• Multiple LLM providers (Ollama, OpenAI, HuggingFace)
• Organized index storage in ~/.leann/indexes/
• Support for advanced search parameters

📋 Click to expand: Complete CLI Reference

Build Command:

leann build INDEX_NAME --docs DIRECTORY [OPTIONS]

Options:
  --backend {hnsw,diskann}     Backend to use (default: hnsw)
  --embedding-model MODEL      Embedding model (default: facebook/contriever)
  --graph-degree N            Graph degree (default: 32)
  --complexity N              Build complexity (default: 64)
  --force                     Force rebuild existing index
  --compact                   Use compact storage (default: true)
  --recompute                 Enable recomputation (default: true)

Search Command:

leann search INDEX_NAME QUERY [OPTIONS]

Options:
  --top-k N                   Number of results (default: 5)
  --complexity N              Search complexity (default: 64)
  --recompute-embeddings      Use recomputation for highest accuracy
  --pruning-strategy {global,local,proportional}

Ask Command:

leann ask INDEX_NAME [OPTIONS]

Options:
  --llm {ollama,openai,hf}    LLM provider (default: ollama)
  --model MODEL               Model name (default: qwen3:8b)
  --interactive              Interactive chat mode
  --top-k N                  Retrieval count (default: 20)

🏗️ Architecture & How It Works

The magic: Most vector DBs store every single embedding (expensive). LEANN stores a pruned graph structure (cheap) and recomputes embeddings only when needed (fast).

Core techniques:

• Graph-based selective recomputation: Only compute embeddings for nodes in the search path
• High-degree preserving pruning: Keep important "hub" nodes while removing redundant connections
• Dynamic batching: Efficiently batch embedding computations for GPU utilization
• Two-level search: Smart graph traversal that prioritizes promising nodes

Backends: DiskANN or HNSW - pick what works for your data size.

Benchmarks

Run the comparison yourself:

python examples/compare_faiss_vs_leann.py

System	Storage
FAISS HNSW	5.5 MB
LEANN	0.5 MB
Savings	91%

Same dataset, same hardware, same embedding model. LEANN just works better.

Storage Usage Comparison

System	DPR (2.1M chunks)	RPJ-wiki (60M chunks)	Chat history (400K messages)	Apple emails (780K messages chunks)	Google Search History (38K entries)
Traditional Vector DB(FAISS)	3.8 GB	201 GB	1.8G	2.4G	130.4 MB
LEANN	324 MB	6 GB	64 MB	79 MB	6.4MB
Reduction	91% smaller	97% smaller	97% smaller	97% smaller	95% smaller

Benchmarks run on Apple M3 Pro 36 GB

Reproduce Our Results

uv pip install -e ".[dev]"  # Install dev dependencies
python examples/run_evaluation.py data/indices/dpr/dpr_diskann      # DPR dataset
python examples/run_evaluation.py data/indices/rpj_wiki/rpj_wiki.index  # Wikipedia

The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!

🔬 Paper

If you find Leann useful, please cite:

LEANN: A Low-Storage Vector Index

@misc{wang2025leannlowstoragevectorindex,
      title={LEANN: A Low-Storage Vector Index}, 
      author={Yichuan Wang and Shu Liu and Zhifei Li and Yongji Wu and Ziming Mao and Yilong Zhao and Xiao Yan and Zhiying Xu and Yang Zhou and Ion Stoica and Sewon Min and Matei Zaharia and Joseph E. Gonzalez},
      year={2025},
      eprint={2506.08276},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2506.08276}, 
}

✨ Features

🔥 Core Features

• 🔄 Real-time Embeddings - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
• 📈 Scalable Architecture - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
• 🎯 Graph Pruning - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
• 🏗️ Pluggable Backends - DiskANN, HNSW/FAISS with unified API

🛠️ Technical Highlights

• 🔄 Recompute Mode - Highest accuracy scenarios while eliminating vector storage overhead
• ⚡ Zero-copy Operations - Minimize IPC overhead by transferring distances instead of embeddings
• 🚀 High-throughput Embedding Pipeline - Optimized batched processing for maximum efficiency
• 🎯 Two-level Search - Novel coarse-to-fine search overlap for accelerated query processing (optional)
• 💾 Memory-mapped Indices - Fast startup with raw text mapping to reduce memory overhead
• 🚀 MLX Support - Ultra-fast recompute/build with quantized embedding models, accelerating building and search (minimal example)

🎨 Developer Experience

• Simple Python API - Get started in minutes
• Extensible backend system - Easy to add new algorithms
• Comprehensive examples - From basic usage to production deployment

🤝 Contributing

We welcome contributions! Leann is built by the community, for the community.

Ways to Contribute

• 🐛 Bug Reports: Found an issue? Let us know!
• 💡 Feature Requests: Have an idea? We'd love to hear it!
• 🔧 Code Contributions: PRs welcome for all skill levels
• 📖 Documentation: Help make Leann more accessible
• 🧪 Benchmarks: Share your performance results

FAQ

1. My building time seems long

You can speed up the process by using a lightweight embedding model. Add this to your arguments:

--embedding-model sentence-transformers/all-MiniLM-L6-v2

Model sizes: all-MiniLM-L6-v2 (30M parameters), facebook/contriever (~100M parameters), Qwen3-0.6B (600M parameters)

📈 Roadmap

🎯 Q2 2025

• DiskANN backend with MIPS/L2/Cosine support
• HNSW backend integration
• Real-time embedding pipeline
• Memory-efficient graph pruning

🚀 Q3 2025

• Advanced caching strategies
• Add contextual-retrieval https://www.anthropic.com/news/contextual-retrieval
• Add sleep-time-compute and summarize agent! to summarilze the file on computer!
• Add OpenAI recompute API

🌟 Q4 2025

• Integration with LangChain/LlamaIndex
• Visual similarity search
• Query rewrtiting, rerank and expansion

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

• Microsoft Research for the DiskANN algorithm
• Meta AI for FAISS and optimization insights
• HuggingFace for the transformer ecosystem
• Our amazing contributors who make this possible

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⭐ Star us on GitHub if Leann is useful for your research or applications!

Made with ❤️ by the Leann team