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matthew-johnson
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faiss

Perform scalable vector similarity search and clustering across billions of embeddings.

Updated Apr 20, 2026
Search Tags:#embeddings#vector-search#gpu-acceleration#faiss#similarity-search#nearest-neighbors#kNN
One-click install
npx skills add https://github.com/matthew-johnson/hermes-agent --skill faiss-matthew-johnson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: faiss
Source: https://github.com/matthew-johnson/hermes-agent/tree/main/optional-skills/mlops/faiss
Command: npx skills add https://github.com/matthew-johnson/hermes-agent --skill faiss-matthew-johnson

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faiss-cpu, faiss-gpu, numpy.

What problem does it solve?

FAISS provides efficient similarity search and clustering for dense vector representations, enabling fast matching and retrieval at scale.

Core Features & Use Cases

  • Efficient, scalable vector search across billions of embeddings using Flat, IVF, and HNSW indices.
  • GPU-accelerated performance for large datasets and real-time inference.
  • Pure vector similarity without mandatory metadata filtering, plus integrations with popular ML tooling.

Quick Start

Index your embeddings with FAISS and run nearest-neighbor queries on large vector datasets.

Frequently Asked Questions about faiss

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform similarity search across billions of dense vector embeddings?▼

To perform similarity search across billions of dense vector embeddings, you can index your datasets using Flat, IVF, or HNSW indices. This approach enables fast nearest-neighbor matching and offline clustering at scale without sacrificing retrieval speed.

Can I use GPU acceleration for real-time nearest-neighbor queries on large datasets?▼

Yes, GPU acceleration is supported for real-time nearest-neighbor queries on large datasets. By utilizing GPU-accelerated indices alongside standard CPU indices, you can significantly reduce inference latency for high-volume vector search workloads.

Does faiss work with LangChain and LlamaIndex for building similarity-based recommendations?▼

Yes, faiss integrates with LangChain and LlamaIndex to build similarity-based recommendations. These common integrations allow you to embed documents and query the resulting vector datasets directly within popular ML tooling environments.

What is the difference between Flat, IVF, and HNSW indices for vector search?▼

Flat, IVF, and HNSW indices represent different vector search strategies for dense vector datasets. Flat provides exact matching, IVF partitions data for faster approximate search, and HNSW builds graph structures, allowing you to balance search accuracy and speed.

When should I not use pure vector similarity for my retrieval tasks?▼

You should not use pure vector similarity when your retrieval tasks require mandatory metadata filtering. This approach provides efficient nearest-neighbor matching based on dense vector representations but does not natively support filtering by structured metadata attributes.

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