FAISS
FAISS (Facebook AI Similarity Search) is Meta's open-source library for efficient similarity search and clustering of dense vectors at massive scale. Powers Meta's production systems handling billions of images, embeddings, and recommendations. Key strengths: (1) Speed—10-100× faster than naive search through optimized algorithms (IVF, HNSW, PQ), (2) Memory efficiency—Product Quantization compresses vectors 32×, (3) GPU support—leverage GPU parallelism for 100× speedup, (4) Scale—proven at billions of vectors. Used by Meta, OpenAI, Anthropic, and thousands of AI applications. C++ core with Python bindings.
Overview
FAISS is the industry standard for high-performance vector search. Unlike database-first solutions (Pinecone, Weaviate), FAISS is a library you embed in applications for maximum performance. Use cases: image search (Meta uses for billions of photos), recommendation systems (YouTube-scale), RAG systems (Anthropic's research), nearest neighbor search in ML pipelines. Key innovation: combines multiple indexing strategies (IVF for speed, PQ for memory, HNSW for accuracy) to achieve optimal speed/memory/accuracy tradeoffs at any scale.
Key Features
- **Multiple Index Types**: IVF, HNSW, PQ, LSH—choose based on speed/memory/accuracy needs
- **GPU Acceleration**: 100× faster on GPU, handles billion-vector datasets
- **Product Quantization**: Compress 768-dim float32 vectors 32× with <5% accuracy loss
- **Exact + Approximate**: Switch between exact (slow, perfect) and approximate (fast, 99% accurate)
- **Battle-Tested**: Powers Meta's production systems with billions of vectors
- **Python + C++**: Easy Python API, C++ core for maximum performance
Business Integration
FAISS enables billion-scale AI features with minimal infrastructure. E-commerce visual search: index 100M product images, find similar in <10ms. Content platforms: index 1B user-generated images, detect duplicates and recommend similar content. RAG systems: index entire company knowledge base (millions of documents), retrieve relevant context in milliseconds. Security applications: facial recognition across millions of faces with real-time matching. The key advantage: library approach means no database servers, no API costs—embed directly in your application for maximum performance and minimum latency.
Implementation Example
Technical Specifications
- **Scale**: Tested on billions of vectors, no theoretical limit
- **Speed**: 1M queries/second on GPU (IVF+PQ), 100K queries/second on CPU
- **Memory**: PQ compresses vectors 8-64×, enables billion-vector search on single machine
- **Accuracy**: HNSW achieves 99%+ recall, IVF 95%+, PQ 90%+ (configurable)
- **GPU**: Supports NVIDIA GPUs, 100× speedup for large-scale search
- **Languages**: Python (primary), C++, Java bindings
Best Practices
- Use Flat index for <10K vectors, IVF for 10K-10M, IVFFlat+PQ for >10M
- Train IVF on representative sample (100K-1M vectors sufficient)
- Normalize vectors for cosine similarity (use IndexFlatIP)
- Use GPU for >1M vectors—dramatically faster for large-scale
- Tune nprobe (IVF) and ef_search (HNSW) for speed/accuracy tradeoff
- Save trained indexes to disk—training is expensive, reuse indexes