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faiss

Perform k-NN search and clustering on dense vectors with GPU acceleration.

2|Updated Jun 8, 2026
Search Tags:#GPU acceleration#vector search#clustering#Faiss#k-NN search
One-click install
npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill faiss-vikrant-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: faiss
Source: https://github.com/vikrant-project/devil-agent-ai-platform/tree/main/agent_core/optional-skills/mlops/faiss
Command: npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill faiss-vikrant-project

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a solution for efficient similarity search and clustering of dense vectors, catering to large-scale vector datasets and GPU acceleration requirements.

Core Features & Use Cases

  • Similarity Search: Offers fast k-NN search on large vector datasets (millions/billions).
  • Clustering: Supports clustering of dense vectors with various index types (Flat, IVF, HNSW).
  • GPU Acceleration: Utilizes GPU acceleration for enhanced performance.
  • Use Case: Ideal for applications requiring fast similarity search, such as in recommendation systems or search engines.

Quick Start

Use the faiss skill to perform a k-NN search on the attached vectors file 'vectors.dat'.

Frequently Asked Questions about faiss

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

FAQPage Schema
How do I perform k-NN search on millions of dense vectors?▼

To perform k-NN search on millions of dense vectors, this Skill indexes and retrieves nearest neighbors efficiently using Flat, IVF, and HNSW structures. It handles large-scale datasets for fast similarity retrieval.

Can I use GPU acceleration for vector similarity search?▼

Yes, GPU acceleration is supported for vector similarity search. This Skill utilizes faiss-gpu to enhance performance during dense vector retrieval and clustering operations across large-scale datasets.

What is the best way to cluster dense vectors for a recommendation system?▼

Clustering dense vectors for recommendation systems is achieved through optimized indexing. This Skill applies clustering algorithms with index types like IVF to group dense vectors efficiently for fast retrieval.

Does this vector search approach work with Numpy arrays?▼

This vector search approach works with Numpy arrays. It requires Numpy for vector manipulation alongside the Faiss library to process and search dense vector datasets effectively.

When should I use different index types like IVF or HNSW for clustering?▼

Use index types like IVF or HNSW for clustering when balancing search speed and accuracy. This Skill supports these structures to optimize dense vector grouping and retrieval depending on dataset scale.

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