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AlvaroBiano
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faiss

Build and query FAISS indices for billion-scale vector similarity search.

Updated May 2, 2026
Search Tags:#embeddings#high-performance#vector-search#machine-learning#gpu-acceleration#faiss#nn-search
One-click install
npx skills add https://github.com/AlvaroBiano/hermes-agent --skill faiss-alvarobiano
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: faiss
Source: https://github.com/AlvaroBiano/hermes-agent/tree/main/optional-skills/mlops/faiss
Command: npx skills add https://github.com/AlvaroBiano/hermes-agent --skill faiss-alvarobiano

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

FAISS is a library for efficient similarity search in dense vector spaces, enabling fast and scalable nearest-neighbor retrieval across billions of vectors without sacrificing performance.

Core Features & Use Cases

  • Fast, scalable vector search with multiple index types (Flat, IVF, HNSW, PQ)
  • GPU-accelerated training and querying for large datasets
  • Use cases include large-scale document retrieval, recommendation, and embedding-based search across products and content.

Quick Start

Create a GPU-accelerated FAISS index from your embedding vectors and perform a k-NN search.

Frequently Asked Questions about faiss

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

FAQPage Schema
How do I perform billion-scale vector similarity search for machine learning?▼

Billion-scale vector similarity search is performed by building indices across dense embeddings using FAISS, which supports multiple index types like Flat, IVF, HNSW, and PQ for fast k-NN retrieval.

Can I use GPU acceleration for dense vector search and retrieval?▼

Yes, GPU acceleration is supported for both training and querying large datasets, enabling high-performance nearest-neighbor retrieval across billions of dense embeddings.

What is the best way to build a recommendation system index from fixed dimension embeddings?▼

Building a recommendation system index requires fixed dimension embeddings and the FAISS library to construct and query scalable indices for fast document and content retrieval.

Which index types are available for high-performance k-NN search?▼

Available index types for high-performance k-NN search include Flat, IVF, HNSW, and PQ, allowing optimized similarity search and retrieval across large-scale vector spaces.

Do I need fixed dimension embeddings to use FAISS for document retrieval?▼

Yes, you need fixed dimension embeddings to build and query indices, as FAISS requires consistent vector dimensions to perform efficient similarity search and retrieval.

When should I not use HNSW or PQ indices for vector search?▼

Index choice depends on your specific scale and performance needs; while HNSW and PQ offer optimized retrieval, you must evaluate whether their memory and training trade-offs suit your high-performance ML deployment.

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