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facebookresearch/faiss

faiss

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

View on GitHub↗Upstream facebookresearch/faiss↗

Builder

Meta Research

Meta Research

facebookresearch • ai-lab

Stars

40,163

Using upstream star count

Forks

4,398

Using upstream fork count

Open Issues

0

Activity Score

0/100

0 commits in 30d

Created

Feb 7, 2017

Project creation date

README Summary

Faiss is a library for efficient similarity search and clustering of dense 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/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed primarily at Meta's [Fundamental AI Research](https://ai.facebook.com/) group.

Community Evaluation

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AI Dev Skills

Unmapped

Approximate Nearest Neighbors (ANN)Dense Vector RetrievalEmbedding SearchGPU-Accelerated ComputingHigh-Dimensional Vector IndexingLarge-Scale Vector DatabasesMemory-Efficient AlgorithmsVector ClusteringVector QuantizationVector Similarity Search

Tags

Approximate Nearest Neighbors (ANN)Dense Vector RetrievalEmbedding SearchGPU-Accelerated ComputingHigh-Dimensional Vector IndexingLarge-Scale Vector DatabasesMemory-Efficient AlgorithmsVector ClusteringVector QuantizationVector Similarity SearchBenchmarkingC++EvalsForkedGPU / CUDANumPyPyTorchPythonQuantizationResearch / PapersTutorial

Taxonomy

AI Trends

Retrieval-Augmented GenerationVector DatabasesEmbedding-based AILarge-Scale AI InfrastructureGPU-Accelerated AI

category

Evals & BenchmarkingFoundation ModelsModel TrainingInference & ServingLearning ResourcesData Science & Analytics

Deployment Context

Self-hostedCloud APIOn-premiseGPU Clusters

Modalities

Dense VectorsText EmbeddingsImage EmbeddingsMultimodal Embeddings

Skill Areas

Vector Similarity SearchApproximate Nearest Neighbors (ANN)High-Dimensional Vector IndexingVector ClusteringGPU-Accelerated ComputingMemory-Efficient AlgorithmsLarge-Scale Vector DatabasesVector QuantizationEmbedding SearchDense Vector Retrieval

tag

BenchmarkingC++EvalsForkedGPU / CUDANumPyPyTorchPythonQuantizationResearch / PapersTutorial

Use Cases

Semantic SearchRecommendation SystemsImage Similarity SearchDocument RetrievalEmbedding-based SearchContent-Based FilteringDuplicate DetectionVector Database OperationsInformation Retrieval SystemsLarge-Scale Clustering

Recent Activity

Updated 2 months ago

7 Days

0

30 Days

0

90 Days

20

Validate inverted lists pointer in IndexIVF operations (#4951)

Justin Gibbs • Mar 21, 2026

bd5ed00

Validate graph index data during deserialization (#4949)

Justin Gibbs • Mar 21, 2026

d647b59

Handle empty index in IndexHNSWCagra base_level_only search (#4950)

Justin Gibbs • Mar 21, 2026

398857b

Quality

production
Quality
high
Maturity
production

Categories

Evals & BenchmarkingPrimaryInference & ServingLearning ResourcesData Science & AnalyticsFoundation ModelsModel TrainingSearch & Knowledge

PM Skills

Cost & EfficiencyData & Evaluation

Languages

C++100.0%

Timeline

Project created
Feb 7, 2017
Forked
Mar 22, 2026
Your last push
2 months ago
Upstream last push
18 days ago
Tracked since
Mar 21, 2026

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