faiss

Perform similarity search and clustering on dense vectors with FAISS.

3|Updated Feb 21, 2026

Search Tags:#RAG #vector search #similarity search #faiss #gpu acceleration #large scale

Ready to run

npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill faiss-ihatesea69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: faiss
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/15-rag/faiss
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill faiss-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faiss-cpu, faiss-gpu, numpy, and includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently searching through massive datasets of dense vectors, enabling rapid identification of similar items in large-scale applications.

Core Features & Use Cases

High-Performance Similarity Search: Utilizes Facebook AI's FAISS library for lightning-fast k-NN and similarity searches on billions of vectors.
GPU Acceleration: Supports GPU acceleration for significant speedups in search and indexing.
Diverse Index Types: Offers various index types (Flat, IVF, HNSW, PQ) to balance speed, accuracy, and memory usage.
Use Case: Quickly find the most similar images in a dataset of millions by searching their vector embeddings.

Quick Start

Use the faiss skill to create a FlatL2 index and add 1000 random 128-dimensional vectors to it.

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 on billions of dense vectors?▼

You can perform similarity search on billions of dense vectors using Facebook AI's FAISS library, which supports various index types like Flat, IVF, and HNSW to balance speed, accuracy, and memory for high-performance k-NN search.

How do I build a RAG application with fast vector retrieval?▼

Fast vector retrieval for RAG applications is built by creating a FAISS index for dense vectors and integrating it with frameworks like LangChain and LlamaIndex to efficiently search and retrieve relevant context.

Can I use GPU acceleration for large-scale k-NN search?▼

Yes, GPU acceleration is supported for large-scale k-NN search through the faiss-gpu dependency, providing significant speedups in both search and indexing operations for billions of vectors.

What is the best way to balance speed and memory for similarity search?▼

The best way to balance speed, accuracy, and memory for similarity search is by selecting appropriate FAISS index types, such as IVF, HNSW, or PQ, tailored to your specific dataset scale.

How do I find similar images using vector embeddings?▼

To find similar images, you convert your dataset of millions of images into vector embeddings, add them to a FAISS index, and query with a new image's embedding for rapid similarity identification.