FAISS
FAISS is Meta’s C++ library for dense-vector similarity search and clustering, with Python wrappers and optional GPU acceleration. We assess it as the established baseline for local exact and approximate nearest-neighbor search.
Blurb
Faiss is a library for efficient similarity search and clustering of dense vectors.
Summary
When to use:
- You need a mature local library rather than a database service.
- You need exact search, HNSW, product quantization, or GPU indexes under one API.
- You need a baseline for evaluating TurboVec, pgvector, or another vector engine.
When to skip:
- You need SQL joins, transactions, metadata filtering, and operational persistence. Start with pgvector.
- You want online low-bit quantization without a separate training phase. Assess TurboQuant or TurboVec.
- You need a managed, distributed vector service rather than an embedded library.
Trade-offs: FAISS exposes many index families and tuning controls, but that flexibility adds complexity. Index choice affects recall, latency, memory, training cost, and update behavior. Persistence and application-level metadata remain your responsibility.
Details
Index Families
| Family | Use |
|---|---|
| Flat | Exact L2 or inner-product search; useful as a recall oracle |
| HNSW | Graph-based approximate search without quantizer training |
| IVF | Partition vectors into inverted lists; tune probes for speed versus recall |
| PQ | Compress vectors with learned product-quantization codebooks |
| GPU | Run supported exact and approximate indexes on one or more GPUs |
FAISS compares vectors with L2 distance or dot product. Cosine similarity uses dot product after normalization. Some compressed indexes store only encoded vectors, reducing memory at the cost of precision.
Comparison Points
| Option | Better Fit |
|---|---|
| FAISS | Broad local ANN toolkit, benchmarking, GPU search |
| TurboVec | Compact online ingest using TurboQuant, especially for local RAG |
| pgvector | Vectors beside relational data in Postgres |