FAISS (Facebook AI Similarity Search) is a high-performance library for efficient similarity search and clustering of dense vectors. It serves as the de facto standard library for approximate nearest neighbor (ANN) search in production environments, supporting hardware-accelerated (CPU with SIMD, GPU via CUDA/Metal) and research-grade algorithms. The codebase implements dozens of index types (flat, IVF, PQ, SQ, HNSW, NSG, RaBitQ, additive quantizers, binary, etc.), each with multiple encoding and scanning strategies. The architecture is layered: a core C++ library with extensible index and quantizer abstractions, a full C API, Python bindings via SWIG, and optional GPU or Intel SVS backends. Key entry points include the string-based index factory, training/search methods, and serialization utilities. The library is designed for both offline indexing and real-time search, with extensive auto-tuning and benchmark infrastructure.
VexDB-Lite is a vector similarity search library providing Approximate Nearest Neighbor (ANN) capabilities as extensions for PostgreSQL, DuckDB, and SQLite databases. The system implements Hierarchical Navigable Small World (HNSW) graphs for efficient similarity search, with optional Product Quantization and RaBitQ compression for memory efficiency. It features hardware-accelerated distance computation using SIMD instructions (SSE, AVX, AVX512, NEON) and provides multiple distance metrics (L2, inner product, cosine). The architecture separates core algorithms from database-specific adapters, enabling consistent functionality across different storage engines. The codebase demonstrates strong engineering practices with reusable components like the SIMD dispatch system, vector buffer manager, and product quantization implementation.