rag-faiss

Topics in the source

• FAISS - Efficient Similarity Search
• When to use FAISS
• Quick start
• Installation
• Basic usage
• Index types
• 1. Flat (exact search)
• 2. IVF (inverted file) - Fast approximate

Facebook AI's library for billion-scale vector similarity search. Use FAISS when: Need fast similarity search on large vector datasets (millions/billions) GPU acceleration required Pure vector similarity (no metadata filtering needed) High throughput, low latency critical Offline/batch processing of embeddings Metrics: 31,700+ GitHub stars Meta/Facebook AI Research Handles billions of vectors C++ with Python bindings Use alternatives instead: Chroma/Pinecone: Need metadata filtering Weaviate: Need full database features Annoy: Simpler, fewer features 1. Choose right index type - Flat for <10K, IVF for 10K-1M, HNSW for quality 2. Normalize for cosine - Use IndexFlatIP with normalized vectors 3. Use GPU for large datasets - 10-100× faster 4. Save trained indices - Training is expensive 5. Tune nprobe/efsearch - Balance speed/accuracy 6. Monitor memory - PQ for large datasets 7. Batch queries - Better GPU utilization | Index Type | Build Time | Search Time | Memory | Accuracy | |------------|------------|-------------|--------|----------| | Flat | Fast | Slow | High | 100% | | IVF | Medium | Fast | Medium | 95-99% | | HNSW | Slow | Fastest | High | 99% | | PQ | Medium | Fast | Low | 9

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