FAISS
FAISS (Facebook AI Similarity Search) is a popular open-source library for efficient similarity search and clustering of dense vectors. Developed by Facebook/Meta, it supports billions of vectors and is widely used to power vector search engines and databases, especially where raw speed and scalability are needed.
About this tool
FAISS
FAISS (Facebook AI Similarity Search) is an open-source library for efficient similarity search and clustering of dense vectors, developed by Facebook/Meta. It is widely used for powering vector search engines and databases, especially in applications requiring high speed and scalability.
Features
- Efficient Similarity Search and Clustering: Handles dense vectors of any size, including sets that do not fit in RAM.
- Multiple Distance Metrics: Supports Euclidean (L2), maximum inner product, and limited support for other metrics (L1, Linf).
- k-Nearest Neighbors: Returns not only the single nearest neighbor but also the k-nearest neighbors.
- Batch Processing: Can search several vectors at a time for faster performance.
- Precision-Speed Tradeoff: Allows trading precision for speed or reduced memory usage (approximate search).
- Range Search: Can return all elements within a given radius of the query point.
- Disk-Based Indexing: Option to store indices on disk rather than RAM.
- Binary Vector Indexing: Supports indexing binary as well as floating-point vectors.
- Predicate Filtering: Can ignore a subset of index vectors via predicates on vector IDs.
- C++ Core with Python Wrappers: Written in C++ with full Python API wrappers.
- GPU Acceleration: Many algorithms are implemented to run on GPUs for high-speed, billion-scale search.
- Extensive Research Backing: Implements numerous state-of-the-art algorithms and quantization methods from academic research (e.g., Inverted File, Product Quantization, IVFADC, HNSW, NSG, Residual Quantization, etc.).
- Parameter Tuning and Evaluation Tools: Includes supporting code for parameter tuning and evaluation.
- Open Source: Freely available under an open-source license.
Installation
- Install via Conda:
- CPU:
conda install -c pytorch faiss-cpu - GPU:
conda install -c pytorch faiss-gpu
- CPU:
Pricing
- Free and open-source
Tags
open-source, ann, similarity-search, scalable
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