- 🔄 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
- 🧠 AST-Aware Code Chunking - Intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript files
- 📈 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 - HNSW/FAISS (default), with optional DiskANN for large-scale deployments
- 🔄 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)
- Simple Python API - Get started in minutes
- Extensible backend system - Easy to add new algorithms
- Comprehensive examples - From basic usage to production deployment