Build local vector retrieval indexes with Faiss
Use Faiss to create and query local vector indexes for agent retrieval and RAG workflows before adding heavier managed vector infrastructure.
npx skills add agentskillexchange/skills --skill build-local-vector-retrieval-indexes-with-faiss
Use this when an agent or operator needs to build a local retrieval index from embeddings, tune nearest-neighbor search behavior, and hand back ranked candidates for RAG, semantic search, or offline evaluation. The workflow is bounded to indexing and querying vectors: prepare embeddings, choose an index type, add vectors with stable IDs, run similarity search, and return evidence for downstream agent steps. It is useful when a workflow needs fast local experiments, offline retrieval checks, or reproducible comparison against heavier vector database choices. This is not a generic Faiss library card; it is for local vector retrieval operations where the agent needs repeatable search setup and inspection instead of relying on a hosted vector database.
What this skill actually does
Inputs and prerequisites: Faiss, embeddings, Python or native runtime bindings.
Setup notes: Install the appropriate Faiss package or build from source, prepare embedding vectors, create a Faiss index, add vectors with IDs, and query the index from the agent retrieval workflow.
Source and verification boundary: use https://github.com/facebookresearch/faiss as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.