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. Some of the most useful algorithms are implemented on the GPU. It is developed primarily at FAIR, the fundamental AI research team of Meta.
Key Features
- Efficient Similarity Search: Faiss builds data structures in RAM for fast similarity search operations.
- Scalability: Handles datasets that do not fit in RAM.
- GPU Support: Some algorithms are implemented on the GPU for enhanced performance.
- Multiple Search Types: Supports various search operations like nearest neighbor search, batch processing, and range search.
- Quantization Techniques: Implements methods like product quantization for efficient vector compression and distance computation.
- Research Foundations: Based on years of research, incorporating methods from various academic papers.
Use Cases
- Large-scale Data Processing: Ideal for applications requiring fast similarity search in large datasets.
- Machine Learning: Useful for tasks involving vector similarity, such as recommendation systems and image retrieval.
- AI Research: Provides tools for evaluating and tuning parameters in AI models.
