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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…
Section: Search engine and framework · 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…
Section: Machine Learning · A library for efficient similarity search and clustering of dense vectors. [MIT]
Section: Embeddings and Semantic Search · A library for efficient similarity search and clustering of dense vectors
Section: Vector Databases (RAG) · A library for efficient similarity search and clustering of dense vectors.
Section: 5. Retrieval-Augmented Generation (RAG) & Knowledge · Similarity search and clustering library for dense vectors with CPU and GPU implementations.
Section: Industry Strength Information Retrieval · Faiss is a library for efficient similarity search and clustering of dense vectors.
Section: Library
Section: Other · A library for efficient similarity search and clustering of dense vectors.
Transpile trained ML models into other languages. sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript and others. mlflow - Manage the machine learning lifecycle, including experimentation, reproducibility and deployment. skll - Command-line utilities to make it easier…
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. [Apache2]
General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
All-in-one AI framework for semantic search, LLM orchestration and language model workflows. Embeddings database with customizable pipelines.