Faiss
High performance vector similarity search and clustering library from Meta with CPU and GPU support under the MIT license.
Overview
Faiss is a C plus plus and Python library that implements state of the art indexing structures for nearest neighbor search on dense vectors. It includes IVF, HNSW style graphs, product quantization and GPU accelerated pipelines for billion scale search, with utilities for evaluation and parameter tuning. Developers embed Faiss in retrieval augmented generation, recommendation and deduplication systems to search embeddings quickly and compactly.
The project is MIT licensed, enabling commercial and open source use without restrictive terms. Documentation, examples and community discussions make it straightforward to prototype in Python then optimize critical paths in C plus plus or CUDA. As part of Meta’s FAIR tooling, Faiss remains a common baseline for local vector databases and custom search stacks.
Key features
- Efficient CPU and GPU indexes for dense vectors
- Algorithms like IVF PQ HNSW for speed and memory
- Python wrappers for fast prototyping
- Scales to billion plus vectors with sharding
- Rich evaluation and tuning utilities
- Mixed precision and quantization support
- MIT license for commercial friendly use
- Active community and examples
Best for
- Build local RAG retrieval for LLM apps
- Speed up product and content recommendation
- Detect near duplicates and spam in large sets
- Cluster embeddings to discover segments
- Run similarity search on edge or on prem
- Compress indexes to fit tight memory budgets
- Prototype in Python then optimize in C plus plus
- Teach vector search concepts with real code
Capabilities
IVF PQ HNSW
Choose index types that trade memory for speed and recall, tune parameters with built in tools.
GPU pipelines
Use CUDA accelerated training and search to handle massive datasets and tight latency budgets.
Quantization
Adopt product quantization and mixed precision to fit large indexes into RAM or VRAM.
Python and C plus plus
Start in Python for speed then move hot paths to C plus plus for production performance.
Frequently Asked Questions
Is Faiss free to use commercially?
Yes, Faiss is MIT licensed, allowing commercial use, modification and distribution.
Does it run on GPU?
Yes, Faiss provides CUDA implementations for training and search on supported NVIDIA GPUs.
How big can indexes get?
Faiss supports billion scale datasets with appropriate sharding and quantization strategies.
Can I use it with a vector DB?
Faiss is often embedded in custom services and underpins several databases, you can integrate directly.
Where is the documentation?
Documentation and examples are available on faiss.ai and the GitHub repository.
