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- ⚡ Storage Saving RAG sytem on Consumer Device + 💾 Extreme Storage Saving • 🔒 100% Private • 📚 RAG Everything • ⚡ Easy & Accurate
@@ -20,26 +20,44 @@ --- -## 🌟 What is Leann? +## 🌟 What is LEANN-RAG? -**Leann** revolutionizes Retrieval-Augmented Generation (RAG) by eliminating the storage bottleneck of traditional vector databases. Instead of pre-computing and storing billions of embeddings, Leann dynamically computes embeddings at query time using optimized graph-based search algorithms. +**LEANN-RAG** is a lightweight, locally deployable **Retrieval-Augmented Generation (RAG)** engine designed for personal devices. It combines **compact storage**, **clean usability**, and **privacy-by-design**, making it easy to build personalized retrieval systems over your own data — emails, notes, documents, chats, or anything else. -### 🎯 Why Leann? +Unlike traditional vector databases that rely on massive embedding storage, LEANN reduces storage needs dramatically by using **graph-based recomputation** and **pruned HNSW search**, while maintaining responsive and reliable performance — all without sending any data to the cloud. -Traditional RAG systems face a fundamental trade-off: +--- -- **💾 Storage**: Storing embeddings for millions of documents requires massive disk space -- **🔄 Memory overhead**: The indexes LlamaIndex uses usually face high memory overhead (e.g., in-memory vector databases) -- **💰 Cost**: Vector databases are expensive to scale +## 🔥 Key Highlights -**Leann revolutionizes this with Graph-based recomputation and cutting-edge system optimizations:** +### 💾 1. Extreme Storage Efficiency +LEANN reduces storage usage by **up to 97%** compared to conventional vector DBs (e.g., FAISS), by storing only pruned graph structures and computing embeddings at query time. +> For example: 60M chunks can be indexed in just **6GB**, compared to **200GB+** with dense storage. -- ✅ **Zero embedding storage** - Only graph structure is persisted, reducing storage by 94-97% -- ✅ **Real-time computation** - Embeddings computed on-demand with low latency -- ✅ **Memory efficient** - Runs on consumer hardware with theoretical zero memory overhead -- ✅ **Graph-based optimization** - Advanced pruning techniques for efficient search while keeping low storage cost, with batching and overlapping strategies using low-precision search to optimize latency -- ✅ **Pluggable backends** - Support for DiskANN, HNSW, and other ANN algorithms (welcome contributions!) +### 🔒 2. Fully Private, Cloud-Free +LEANN runs entirely locally. No cloud services, no API keys, and no risk of leaking sensitive data. +> Converse with your own files **without compromising privacy**. +### 🧠 3. RAG Everything +Build truly personalized assistants by querying over **your own** chat logs, email archives, browser history, or agent memory. +> LEANN makes it easy to integrate personal context into RAG workflows. + +### ⚡ 4. Easy, Accurate, and Fast +LEANN is designed to be **easy to install**, with a **clean API** and minimal setup. It runs efficiently on consumer hardware without sacrificing retrieval accuracy. +> One command to install, one click to run. + +--- + +## 🚀 Why Choose LEANN? + +Traditional RAG systems often require trade-offs between storage, privacy, and usability. **LEANN-RAG aims to simplify the stack** with a more practical design: + +- ✅ **No embedding storage** — compute on demand, save disk space +- ✅ **Low memory footprint** — lightweight and hardware-friendly +- ✅ **Privacy-first** — 100% local, no network dependency +- ✅ **Simple to use** — developer-friendly API and seamless setup + +> 📄 For more details, see our [academic paper](https://arxiv.org/abs/2506.08276) ## 🚀 Quick Start ### Installation