RAG y conocimiento
infinity
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
★ 4,7k estrellasApache-2.0
Ver la guía →faiss es un proyecto de RAG y conocimiento de código abierto de facebookresearch (facebookresearch/faiss en GitHub). Tiene 41.112 estrellas en GitHub y 4.551 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 7 de octubre de 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
Vector similarity search inside Postgres - store embeddings next to your data
Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.
Transparent Python RAG reference with FAISS + BM25 hybrid retrieval.
Vespa — the AI search platform: vector, text and tensor search at production scale.
A private AI platform for agents, assistants and enterprise search over your documents
Los demás proyectos de RAG y conocimiento con más estrellas del catálogo de LibreAI: la lista realista de alternativas si faiss no es lo que buscas.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Proyectos de RAG y conocimiento cercanos en el catálogo, los más próximos en estrellas a faiss: sigue explorando a ambos lados.
Complete API layer for private AI apps on local models: RAG, skills, tools, MCP and text-to-SQL
Marker — converts PDFs to Markdown and JSON quickly and accurately, with layout, tables and math intact, Apache-2.0
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Open-source engine turning continuous content streams into search, monitoring and alerts
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.