Related projects

This page references other projects and repositories that use Faiss or that are related to it. They are unaffiliated the main Faiss, so any issue with those should be redirected to the respective packages. If you did something interesting with Faiss and you want to add a link to your project, just ask us (via an issue).

Bindings to other languages and porting to other platforms

There are projects to interface Faiss with other languages, see

the Rust bindings at https://github.com/Enet4/faiss-rs
the .NET Core 3.0 bindings at https://gitlab.com/josetruyol/faisssharp
the Java interface at https://github.com/gameofdimension/jni-faiss
ruby wrapper here https://github.com/ankane/faiss
golang wrapper here: https://github.com/DataIntelligenceCrew/go-faiss/

Other platforms:

Faiss is reported to work on Raspberry Pi (see https://github.com/facebookresearch/faiss/issues/1071) update Faiss runs on ARM server machines with Linux.
Faiss compiles on PowerPC, see issues #529 and #1098
Faiss compiles on the Apple M1, see #2111. We will soon add this as a supported platform.

Other packages:

Faiss C++ on CPU is available via a Brew package: https://github.com/Homebrew/homebrew-core/pull/49235
Faiss is reported to work on Google collab: https://stackoverflow.com/questions/47967252/installing-faiss-on-google-colaboratory
Faiss on Alibaba cloud: https://www.alibabacloud.com/help/en/e-mapreduce/latest/faiss-server

Packages that use or extend Faiss

QuickerADC, an implementation of IVFPQ variants using SIMD with better precision/speed operating points: https://github.com/technicolor-research/faiss-quickeradc See also the paper Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD, André et al.
pgANN: Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database. https://github.com/netrasys/pgANN
milvus offers a uniform interface to Faiss + other packages and manages it like a database engine (client/server and the like): https://github.com/milvus-io/milvus
vearch: a search engine based on Faiss with good scalability and real-time updates: https://github.com/vearch/vearch
port of (an old version of) Faiss to Windows: https://github.com/bitsun/faiss-windows update: Faiss is now supported on windows.
EFANNA, a k-NN graph construction method that can use Faiss: https://github.com/ZJULearning/efanna_graph
Autofaiss, a library from Criteo that automatically selects a Faiss index: https://github.com/criteo/autofaiss
Managed Faiss, a service built on top of Faiss with higher level functionalities: https://www.pinecone.io/managed-faiss
Haystack retriever, that manages document stores: https://haystack.deepset.ai/
OpenSearch k-NN a nearest-neighbor search service built on top of Faiss (and used in AWS) https://opensearch.org/docs/latest/search-plugins/knn/index/

Alternative packages

There are many approximate nearest neighbor search libraries. You may be interested in:

NMSLib (An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces): https://github.com/nmslib/nmslib
an implementation of IVF-HNSW https://github.com/dbaranchuk/ivf-hnsw
Annoy (Approximate Nearest Neighbors in C++/Python): https://github.com/spotify/annoy
A promising GPU index here (vaporware as of 2020-01-20): https://github.com/cgtuebingen/ggnn
Google's SCANN package (very fast!): https://github.com/google-research/google-research/tree/master/scann
Poeem (by JD.com): joint training of recommendation embeddings and index: https://github.com/jdcomsearch/poeem
Pinecone: a fully fledged similarity search service (closed source, not based on Faiss) https://www.pinecone.io/
Weaviate: a text indexing service with a custom HNSW implementation https://www.semi.technology/developers/weaviate/current/vector-index-plugins/
Pyserini: a document indexing library that uses Lucene and Faiss https://github.com/castorini/pyserini
Qdrant: a vector search engine based on HNSW that supports filtering https://qdrant.tech/
Redis: a vector search engine that supports HNSW (k-nn and range search), https://redis.io/docs/stack/search/reference/vectors/
Usearch: a templatized HSNW implementation with many language bindings, https://github.com/unum-cloud/usearch
Epsilla: a vector search engine for low-dimensional vectors (?): https://github.com/epsilla-cloud/vectordb
Spotify's Voyager: a HNSW implementation with speed and memory usage improvements: https://spotify.github.io/voyager/
Puck is Baidu's search library, supports HNSW and PQ compression: https://github.com/baidu/puck

Benchmarks

Approximate nearest neighbor benchmark page: https://github.com/erikbern/ann-benchmarks (The benchmark is notoriously outdated even w.r.t. accepted PRs)
Another benchmark: https://github.com/matsui528/annbench
The billion-scale approximate nearest neighbor challenge: https://big-ann-benchmarks.com/ (Faiss was used as a baseline and is integrated into the winner solution of Track 1)