FAISS. FAISS (Facebook AI Similarity Search) is an open-source library for similarity search and clustering of vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. 

FAISS is written in C++ with complete wrappers for Python and C. Some of the most useful algorithms are implemented on the GPU using CUDA.

Features

FAISS is organized as a toolbox that contains a variety of indexing methods that commonly involve a chain of components (preprocessing, compression, non-exhaustive search, etc.). The scope of the library is intentionally limited to focus on ANNS algorithmic implementation and to avoid facilities related to database functionality, distributed computing or feature extraction algorithms.

FAISS is designed with the following assumptions:
 Primary data type for vector representation is FP32. The support of other floating-point formats, such as BF16 and FP16, is provided.
 Prefer batches of input queries over a single input query for the search.
 Emphasize on allowing users to write a fast prototyping code using its Python wrappers.
 The code should be as open as possible, so that users can access all the implementation details of the indexes.

The following major categories of indexing methods are supported:
 Brute-force search
 Inverted-lists based indices
 Graph indices, including  (Hierarchical navigable small world) HNSW and Navigating Spread-out Graph (NSG)
 Locality-sensitive hashing (LSH)

The following families of vector quantization methods are supported:
 Binary Quantization
 Scalar Quantization (SQ)
 Product Quantization (PQ), including Polysemous Codes, Optimized Product Quantization (OPQ) and Quicker ADC (PQFastScan)  
 Additive Quantization (AQ), including Residual Quantization (RQ) and Local Search Quantization (LSQ) 
 Neural Quantization, including QINCO

FAISS focuses on euclidean distance and inner product distance for floating-point data. The limited support of other distances (manhattan distance, Lp distance, etc.) is also available.

FAISS code supports multithreading via OpenMP, utilizes BLAS via OpenBLAS or Intel MKL, and also uses custom SIMD kernels for x86 and ARM Neon CPUs.

Besides the similarity search, FAISS provides the following useful facilities:
 k-means clustering
 Random-matrix rotations for spreading the variance over all the dimensions without changing the measured distances
 Principal component analysis
 Data deduplication, which is especially useful for image datasets.

FAISS has a standalone Vector Codec functionality for the lossy compression of vectors, allowing to trade the representation accuracy for the binary size.

Applications

Typical FAISS applications include  recommender systems, data mining, text retrieval and content moderation.

FAISS was reported to index 1.5 trillion 144-dimensional vectors for internal Meta Platforms applications.

FAISS is used in vector databases as a core component of a search engine (OpenSearch, Milvus, Vearch).

FAISS is often considered as a baseline in similarity search benchmarks.

FAISS has an integration with Haystack, LangChain frameworks.

Various advanced code snippets for FAISS can be found on its snippets wiki page and case studies wiki page.

See also

 Nearest neighbor search
 Similarity search
 Vector database
 Vector quantization

References

External links
 
 
 Official FAISS wiki
 Guidelines to choose a FAISS index
 Autofaiss - automatically create Faiss knn indices with the most optimal similarity search parameters

Free software programmed in C++
Software using the MIT license