faiss

**FAISS** (Facebook AI Similarity Search) is a **library for efficient similarity search and clustering of dense vectors** — providing the foundational technology underlying many modern vector databases with optimized algorithms for fast nearest neighbor search at scale on CPU and GPU hardware. **What Is FAISS?** - **Definition**: C++ library with Python bindings for vector similarity search - **Type**: Library, not a database (no CRUD operations) - **Creator**: Facebook AI Research (Meta) - **Optimization**: CPU and GPU implementations, highly optimized **Why FAISS Matters** - **Speed**: State-of-the-art performance, especially on GPU (10× faster) - **Foundation**: Powers many vector databases (Milvus, Pinecone) - **Flexibility**: Multiple index types for different accuracy/speed tradeoffs - **Memory Efficiency**: Advanced quantization and compression techniques - **Battle-Tested**: Used in production at Meta and thousands of companies **Core Functionality**: Searches vector database for those most similar to query vector, optimized for speed, memory, and GPU acceleration **Key Index Types**: IndexFlatL2 (brute force, 100% accurate), IndexIVFFlat (fast approximate), IndexHNSW (fastest CPU), IndexIVFPQ (compressed, memory-efficient) **GPU Acceleration**: 10× speedup on NVIDIA GPUs with standard interface **Advanced Features**: Quantization (Scalar, Product), Index Composition, Persistence **Limitations**: Not a database (no CRUD), No metadata filtering, Manual persistence, No updates **Use Cases**: Custom Search Engines, Static Datasets, Research, Embedding Search **Best Practices**: Choose Right Index, Normalize Vectors, Tune Parameters, Use GPU, Batch Queries FAISS is **the foundation** of modern vector search — providing core algorithms powering vector databases, ideal for maximum performance on local hardware or custom search solutions from scratch.