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 applications.
How to install faiss?
aivrar/portable-hermes-agent/faiss
$npx -y skills add aivrar/portable-hermes-agent --skill faissInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/aivrar/portable-hermes-agent" --skill "aivrar/portable-hermes-agent/faiss"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/aivrar/portable-hermes-agent" that are relevant to the current task. Run `npx skills add "https://github.com/aivrar/portable-hermes-agent"` and select the relevant skills, then follow their instructions.
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