faiss Skill
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 appli Published by NousResearch in hermes-agent.
What is faiss Skill?
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 appli Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Data analysis
- Research
- Database workflows use cases
- Data analysis use cases
Technical details
- Install or run with Copy skill directory
When to use faiss Skill
- Use it for database workflows.
- Use it for data analysis.
- Use it for research.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/faiss/SKILL.md
What it does
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 appli
Skill instructions
FAISS - Efficient Similarity Search Facebook AI's library for billion-scale vector similarity search. When to use FAISS Use FAISS when: - Need fast similarity search on large vector datasets (millions/billions) - GPU acceleration required - Pure vector similarity (no metadata filtering needed) - High throughput, low latency critical - Offline/batch processing of embeddings Metrics: - 31,700+ GitHub stars - Meta/Facebook AI Research - Handles billions of vectors - C++ with Python bindings Use alternatives instead: - Chroma/Pinecone: Need metadata filtering - Weaviate: Need full database features - Annoy: Simpler, fewer features Quick start Installation bash CPU only pip install faiss-cpu GPU support pip install faiss-gpu Basic usage python import faiss import numpy as np Create sample data (1000 vectors, 128 dimensions) d = 128 nb = 1000 vectors = np.random.random((nb, d)).astype('float32') Create index index = faiss.IndexFlatL2(d) L2 distance index.add(vectors) Add vectors Search k = 5
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Frequently asked questions
What is faiss?
faiss is a open-source AI agent skill with Copy skill directory. 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).
Who is faiss best for?
faiss is best for reusing agent instructions, scripts, and references, database workflows, data analysis workflows, research workflows.
How do I install faiss?
Install or run faiss using Copy skill directory. Check faiss for the latest setup command.
Is faiss actively maintained?
faiss may need a closer maintenance check before production use.
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