RAG и базы знаний
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AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
★ 4,7 тыс. звёздApache-2.0
Смотреть руководство →faiss — проект с открытым исходным кодом из категории «RAG и базы знаний» от facebookresearch (facebookresearch/faiss на GitHub). У него 41 112 звёзд на GitHub и 4 551 форк, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 7 октября 2026.
Meta's library for efficient similarity search and clustering of dense vectors, in C++ and Python
Decide what you want from faiss and check the README and requirements to confirm it fits your setup and C++ environment.
Clone the repository from GitHub (github.com/facebookresearch/faiss) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Searches sets of vectors of any size, up to sets that do not fit in RAM.
Index types trade off speed, memory and accuracy, from exact search to compressed indexes.
Useful algorithms are implemented on the GPU via CUDA or ROCm, with optional cuVS backends.
Written in C++ with complete Python/NumPy wrappers.
Supports L2 distance, dot product and cosine similarity on normalized vectors.
Includes supporting code for evaluation and parameter tuning.
Start by getting the source and entering the project directory.
git clone https://github.com/facebookresearch/faiss.git
cd faissPrecompiled Anaconda packages are the documented route for Python users.
conda install -c pytorch faiss-cpu # see README / INSTALL.md for variantsCreate an index in Python, add vectors, and search for nearest neighbours.
python -c "import faiss; print(faiss.__version__)" # then follow the wiki Getting Started tutorialIt is maintained by facebookresearch and contributors on GitHub, where the repository github.com/facebookresearch/faiss is the canonical home for code, issues and releases.
Yes — it is open source under the MIT license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. Faiss is a local library with no service or key involved.
Compared with a hosted vector database: Faiss is an embeddable library, not a server — you run it inside your own process with no database to operate.
Clone github.com/facebookresearch/faiss, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
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