FAISS
Facebook AI Similarity Search, a library for efficient similarity search and clustering of dense vectors at scale.
In-Depth Explanation
FAISS (Facebook AI Similarity Search) is a library developed by Meta AI for efficient similarity search of dense vectors. It's designed for billion-scale search and is the foundation for many vector database implementations.
Key features:
- Massive scale: Billions of vectors
- GPU support: CUDA acceleration
- Index types: Multiple algorithms
- Clustering: K-means and variants
- Compression: Product quantisation
- C++/Python: Core library
Index types:
- Flat: Exact search (baseline)
- IVF: Inverted file index
- HNSW: Graph-based
- PQ: Product quantisation
- Composite: Combined approaches
Use cases:
- Large-scale similarity search
- Recommendation systems
- Duplicate detection
- Clustering applications
Business Context
FAISS provides the highest-performance vector search for organisations with massive datasets and specific performance requirements.
How Clever Ops Uses This
We use FAISS for Australian businesses with large-scale search needs where performance is critical and team has technical capacity for lower-level tools.
Example Use Case
"Building a billion-image search system: encode images with CLIP, index with FAISS GPU, achieve sub-millisecond search across entire collection."