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faiss

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when y

  • Skills
  • AI research
  • 3

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

  1. Install the skill (command above).
  2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
  3. No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/faiss && curl -fsSL https://doutorai.pt/data/lib/skills__faiss.md -o .claude/skills/faiss/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

---
name: faiss
description: Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [RAG, FAISS, Similarity Search, Vector Search, Facebook AI, GPU Acceleration, Billion-Scale, K-NN, HNSW, High Performance, Large Scale]
dependencies: [faiss-cpu, faiss-gpu, numpy]
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