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Vector similarity search with FAISS v1.0.0

Use this resource to set up nearest-neighbor search over dense vectors

New

  • 358 downloads
  • 110 likes
  • 219 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install faiss-similarity-search-2

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent helps you choose a FAISS index (flat, IVF or HNSW), install it for CPU or GPU, then index your vectors and run k-NN searches. You get example code to index and query your vectors.

Mostly picked by

Updated November 2, 2026

Card for the “Vector similarity search with FAISS” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: when to use FAISS, installation and code examples
  • references/index_types.md: Flat, IVF and HNSW index types

Good fit for

  • You have millions of embeddings to query quickly
  • You want pure vector search without metadata filters

Not for

    Tags

    • vectors
    • similarity
    • embeddings
    • search

    About

    You say

    “Create a FAISS index for one million 768-dimension embeddings on GPU.”

    Card for the “Vector similarity search with FAISS” skill

    Skill published by Orchestra Research under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to set up nearest-neighbor search over dense vectors
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/index_types.md6.3 KB
    • SKILL.md5.1 KB

    Origin and licence

    Listed unmodified

    Author
    Orchestra Research
    Source
    github.com/orchestra-research/ai-research-skills/tree/773a52944ba4747a18bd4ae9ade53fff041adcbc/15-rag/faiss
    Licence
    MIT

    Notice

    « faiss » (Orchestra Research, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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