| name | 0.4.3-understand-faiss |
| description | [0.4.3] Meta's open-source C++/Python library for dense vector similarity search; implements IVF, PQ, HNSW, ONNG, and |
understand-faiss
CALL NUMBER: vector_databases_and_embeddings.faiss
DEFINITION: Meta's open-source C++/Python library for dense vector similarity search; implements IVF, PQ, HNSW, ONNG, and GPU-accelerated kernels with optional 8-bit quantization.
Invoke this skill to understand faiss down to its primitives. The RELATIVE ROOT below is the least-fixed-point closure of everything it bundles from — the full import cone, grouped by the lib each prim comes from. Projected from a prover-typed KB (MAP/SWI-Prolog consistency gate): every reference below resolves.
THE RELATIVE ROOT (the import cone, by lib)
from vector_databases_and_embeddings
- product_quantization (d1): A vector quantization technique that splits a high-dimensional vector into subvectors, independently quantizes each subvector, and concatenates codes to form a compact representation.
- scalar_quantization (d1): Floating-point values mapped to low-precision (e.g. int8); reduces memory 4x with modest accuracy loss.
- memory_footprint (d2): RAM or VRAM required to hold index and vectors; quantization (PQ, int8) is the primary compression lever.
CONSUMERS (what needs this)
approximate_nearest_neighbor, idx_algorithm_selection, idx_faiss_index_factory, idx_gpu_acceleration, query_latency
Projected from the vector databases and embeddings KB (262 concepts / 150 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.
(leaf — this is an actual skill.)
