LEANN

LEANN is a revolutionary vector database that makes personal AI accessible to everyone. 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.

RAG your emails, browser history, WeChat, or 60M documents on your laptop, in nearly zero cost. No cloud, no API keys, completely private.

Why LEANN?

The numbers speak for themselves: Index 60 million Wikipedia articles in just 6GB instead of 201GB. Finally, your MacBook can handle enterprise-scale datasets. See detailed benchmarks below ↓

Why This Matters

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

🪶 Lightweight: Minimal resource requirements - runs smoothly on any laptop without specialized hardware.

📈 Scalability: Orgnize our messy personal data that would crash traditional vector DBs, with performance that gets better as your data grows more personalized.

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

Quick Start in 1 minute

git clone git@github.com:yichuan520030910320/LEANN-RAG.git leann
cd leann
git submodule update --init --recursive

macOS:

brew install llvm libomp boost protobuf
export CC=$(brew --prefix llvm)/bin/clang
export CXX=$(brew --prefix llvm)/bin/clang++
uv sync

Linux (Ubuntu/Debian):

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

Ollama Setup (Optional for Local LLM):

We support both hf-transformers and Ollama for local LLMs. Ollama is recommended for faster performance.

macOS:

First, download Ollama for macOS.

# Install Ollama
brew install ollama

# 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

You can also replace llama3.2:1b to deepseek-r1:1.5b or qwen3:4b for better performance but higher memory usage.

Dead Simple API

Just 3 lines of code. Our declarative API makes RAG as easy as writing a config file:

from leann.api import LeannBuilder, LeannSearcher

# Index your entire email history (90K emails = 14MB vs 305MB)
builder = LeannBuilder(backend_name="hnsw")
builder.add_from_mailbox("~/Library/Mail")  # Your actual emails
builder.build_index("my_life.leann")

# Ask questions about your own data
searcher = LeannSearcher("my_life.leann") 
searcher.search("What did my boss say about the deadline?")
searcher.search("Find emails about vacation requests")
searcher.search("Show me all conversations with John about the project")

That's it. No cloud setup, no API keys, no "fine-tuning". Just your data, your questions, your laptop.

Try the interactive demo →

Wild Things You Can Do

LEANN supports RAGing a lot of data sources, like .pdf, .txt, .docx, .md, and also supports RAGing your WeChat, Google Search History, and more.

🕵️ Search Your Entire Life

python examples/mail_reader_leann.py
# "What did my boss say about the Christmas party last year?"
# "Find all emails from my mom about birthday plans"

90K emails → 14MB. 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?"

🌐 Time Machine for the Web

python examples/google_history_reader_leann.py
# "What was that AI paper I read last month?"
# "Show me all the cooking videos I watched"

38K browser entries → 6MB. 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?"

💬 WeChat Detective

python examples/wechat_history_reader_leann.py  
# "我想买魔术师约翰逊的球衣，给我一些对应聊天记录"
# "Show me all group chats about weekend plans"

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

📋 Click to expand: Command Examples

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

# Run with custom export directory
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"

📚 Personal Wikipedia

# Index 60M Wikipedia articles in 6GB (not 201GB)
python examples/build_massive_index.py --source wikipedia
# "Explain quantum computing like I'm 5"
# "What are the connections between philosophy and AI?"

PDF RAG Demo (using LlamaIndex for document parsing and Leann for indexing/search)

This demo showcases how to build a RAG system for PDF/md documents using Leann.

Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the examples/data/ directory.
Ensure you have an OPENAI_API_KEY set in your environment variables or in a .env file for the LLM to function.

How It Works

LEANN doesn't store embeddings. Instead, it builds a lightweight graph and computes embeddings on-demand during search.

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

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

Performance: Real-time search on millions of documents.

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.

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.

Storage Usage Comparison

System	DPR (2.1M chunks)	RPJ-wiki (60M chunks)	Chat history (400K messages)	Apple emails (90K messages chunks)	Google Search History (38K entries)
Traditional Vector DB(FAISS)	3.8 GB	201 GB	1.8G	305.8 MB	130.4 MB
LEANN	324 MB	6 GB	64 MB	14.8 MB	6.4MB
Reduction	91% smaller	97% smaller	97% smaller	95% smaller	95% smaller

Benchmarks run on Apple M3 Pro 36 GB

🏗️ Architecture

🔬 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}, 
}

🤝 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

📈 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

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

Made with ❤️ by the Leann team

Description

RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

ai faiss gpt-oss langchain llama-index llm localstorage offline-first ollama privacy python rag retrieval-augmented-generation vector-database vectors vector-search

Readme MIT 80 MiB