faiss Skill
Fast vector similarity search at billion scale. Published by NousResearch in hermes-agent.
Decision snapshot
Is this a fit?
Database workflows, Data analysis, Research, Includes SKILL.md
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Copy skill directory
21 days ago · MIT license
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What is faiss Skill?
Fast vector similarity search at billion scale. 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. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Data analysis
- Research
- Database workflows use cases
- Data analysis use cases
Declared skill metadata
- Declared author: Orchestra Research
- Declared license: MIT
- Source file: optional-skills/mlops/faiss/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- 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
Fast vector similarity search at billion scale.
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
Verified compatibility and discovery
Frequently asked questions
What is faiss?
faiss is a open-source AI agent skill with Copy skill directory. Fast vector similarity search at billion scale.
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.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
