Search billions of vectors fast
faissskillsetup L2★9,423
Orchestra-Research/AI-Research-SKILLs ↗What it does
Search billion-scale embeddings with GPU acceleration and multiple index types
Best for
High-throughput vector search where metadata filtering is not required and GPU acceleration helps.
Inputs
- · dense vectors
- · optional query vectors
- · index type (Flat/IVF/HNSW/PQ)
Outputs
- · indices
- · distances
Requires
- · faiss-cpu or faiss-gpu
- · numpy
Preconditions
Vectors as float32 numpy arrays; dimensionality must be consistent
Failure modes
Wrong index type causes poor speed/accuracy tradeoff; training omitted crashes on untrained IVF
Trust signals
- · 31.7k GitHub stars
- · Meta/Facebook AI Research
- · C++/Python bindings