# facebookresearch/faiss

A library for efficient similarity search and clustering of dense vectors.

Repository: https://github.com/facebookresearch/faiss
Canonical: https://ross.abutalabs.com/products/faiss
Homepage: https://faiss.ai
Language: C++
License: MIT
License Family: permissive
Last push: 2026-08-26T22:57:23+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 3494, "days_push": 7, "days_rel": 30, "gap_med": 73, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 40809, forks 4505 (observed 2026-08-28T04:12:08.366610+00:00)

## What it is
Faiss is a C++ library (with Python wrappers) for efficient similarity search and clustering of dense vectors, supporting indexes that scale to billions of vectors including GPU-accelerated implementations. It provides approximate and exact nearest-neighbor search with L2, inner product, and cosine similarity metrics.

## Use cases
- find nearest neighbors of embeddings
- build semantic search over dense vectors
- similarity search at billion-vector scale
- gpu-accelerated vector search
- cluster high-dimensional vectors
- knn search for recommendation systems
- vector index for RAG retrieval

## When to choose
- you need fast approximate or exact nearest-neighbor search over dense embeddings
- your dataset is too large to fit in RAM or needs GPU acceleration
- you want a mature, battle-tested library with Python bindings

## When to avoid
- you need a full managed vector database with CRUD, filtering, and replication
- your vectors are sparse or textual rather than dense embeddings
- you need real-time updates and deletions, which Faiss indexes support only in limited ways

## Facets
- artifact type: library
- maturity: stable
- function: vector-database, search-engine, machine-learning, gpu-computing
- domain: machine-learning, databases, artificial-intelligence
- platform: cpp, python, windows, cross-platform
- tags: nearest-neighbor-search, ann, similarity-search, dense-vectors, embeddings, hnsw, clustering, cuda, faiss, search, retrieval-augmented-generation, gpu, linux, macos

## Member repositories
- facebookresearch/faiss (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:08.366610+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:23:16.968153+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/faiss (fetched 2026-08-28T04:12:08.366610+00:00, sha 7174d9ef7460)
  - homepage: https://faiss.ai (fetched 2026-08-29T07:46:23.371925+00:00, sha 00d76f42a79e)
- Data as of 2026-08-30T08:39:29.467469+00:00.