faiss

name faiss description Fast vector similarity search at billion scale. version 1.0.0 author Orchestra Research license MIT dependencies ["faiss-cpu","faiss-gpu","numpy"] platforms ["linux","macos"] metadata {"hermes":{"tags":["RAG","FAISS","Similarity Search","Vector Search","Facebook AI","GPU Acceleration","Billion-Scale","K-NN","HNSW","High Performance","Large Scale"]}} FAISS - Efficient Similarity Search Facebook AI's library for billion-scale vector similarity search. When to use FAISS 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 Quick start Installation # CPU only pip install faiss-cpu # GPU support pip install faiss-gpu Basic usage import faiss import numpy as np # Create sample data (1000 vectors, 128 dimensions) d = 128 nb = 1000 vectors = np.random.random((nb, d)).astype( 'float32' ) # Create index index = faiss.IndexFlatL2(d) # L2 distance index.add(vectors) # Add vectors # Search k = 5 # Find 5 nearest neighbors query = np.random.random(( 1 , d)).astype( 'float32' ) distances, indices = index.search(query, k) print ( f"Nearest neighbors: {indices} " ) print ( f"Distances: {distances} " ) Index types 1. Flat (exact search) # L2 (Euclidean) distance index = faiss.IndexFlatL2(d) # Inner product (cosine similarity if normalized) index = faiss.IndexFlatIP(d) # Slowest, most accurate 2. IVF (inverted file) - Fast approximate # Create quantizer quantizer = faiss.IndexFlatL2(d) # IVF index with 100 clusters nlist = 100 index = faiss.IndexIVFFlat(quantizer, d, nlist) # Train on data index.train(vectors) # Add vectors index.add(vectors) # Search (nprobe = clusters to search) index.nprobe = 10 distances, indices = index.search(query, k) 3. HNSW (Hierarchical NSW) - Best quality/speed # HNSW index M = 32 # Number of connections per layer index = faiss.IndexHNSWFlat(d, M) # No training needed index.add(vectors) # Search distances, indices = index.search(query, k) 4. Product Quantization - Memory efficient # PQ reduces memory by 16-32× m = 8 # Number of subquantizers nbits = 8 index = faiss.IndexPQ(d, m, nbits) # Train and add index.train(vectors) index.add(vectors) Save and load # Save index faiss.write_index(index, "large.index" ) # Load index index = faiss.read_index( "large.index" ) # Continue using distances, indices = index.search(query, k) GPU acceleration # Single GPU res = faiss.StandardGpuResources() index_cpu = faiss.IndexFlatL2(d) index_gpu = faiss.index_cpu_to_gpu(res, 0 , index_cpu) # GPU 0 # Multi-GPU index_gpu = faiss.index_cpu_to_all_gpus(index_cpu) # 10-100× faster than CPU LangChain integration from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings # Create FAISS vector store vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings()) # Save vectorstore.save_local( "faiss_index" ) # Load vectorstore = FAISS.load_local( "faiss_index" , OpenAIEmbeddings(), allow_dangerous_deserialization= True ) # Search results = vectorstore.similarity_search( "query" , k= 5 ) LlamaIndex integration from llama_index.vector_stores.faiss import FaissVectorStore import faiss # Create FAISS index d = 1536 faiss_index = faiss.IndexFlatL2(d) vector_store = FaissVectorStore(faiss_index=faiss_index) Best practices Choose right index type - Flat for <10K, IVF for 10K-1M, HNSW for quality Normalize for cosine - Use IndexFlatIP with normalized vectors Use GPU for large datasets - 10-100× faster Save trained indices - Training is expensive Tune nprobe/ef_search - Balance speed/accuracy Monitor memory - PQ for large datasets Batch queries - Better GPU utilization Performance 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 90-95% Resources GitHub : https://github.com/facebookresearch/faiss ⭐ 31,700+ Wiki : https://github.com/facebookresearch/faiss/wiki License : MIT