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faiss

Implements billion-scale vector similarity search using FAISS index types and GPU acceleration.

Updated Jun 5, 2026
Search Tags:#embeddings#rag#vector search#similarity search#faiss#gpu acceleration#k-nn
One-click install
npx skills add https://github.com/xu1713/openhorse --skill faiss-xu1713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: faiss
Source: https://github.com/xu1713/openhorse/tree/main/openhorse/openhorse/optional-skills/mlops/faiss
Command: npx skills add https://github.com/xu1713/openhorse --skill faiss-xu1713

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Performing nearest-neighbor search over millions or billions of dense embedding vectors is too slow with brute-force approaches, and many vector databases add unwanted overhead when you only need pure similarity search without metadata filtering. ## Core Features & Use Cases - Multiple Index Types: Choose from Flat (exact), IVF (clustered approximate), HNSW (graph-based), and PQ (memory-compressed) indices to balance speed, memory, and accuracy. - GPU Acceleration: Move indices to single or multiple GPUs for 10-100x faster search on large datasets. - Framework Integration: Works with LangChain and LlamaIndex vector stores for RAG pipelines. - Use Case: Build a semantic search engine over 10 million document embeddings by training an IVFPQ index, saving it to disk, and serving sub-millisecond k-NN queries. ## Quick Start Use the faiss skill to create an index for my embedding vectors and find the 5 nearest neighbors of a query vector.

Frequently Asked Questions about faiss

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

FAQPage Schema
How do I perform similarity search on vectors with FAISS?▼

Create an index such as faiss.IndexFlatL2(d) with your vector dimension, add float32 vectors with index.add(), then call index.search(query, k) to get the k nearest neighbors with distances and indices.

Which FAISS index type should I use for my dataset size?▼

Use Flat for under 10K vectors with exact results, IVF for 10K-1M with fast approximate search, HNSW for 1M-10M with best quality, and IVF+PQ for over 10M vectors needing low memory.

FAISS vs Chroma or Pinecone for vector search?▼

FAISS provides pure similarity search without metadata filtering, ideal for high-performance offline or batch workloads. Choose Chroma or Pinecone when you need metadata filtering or full database features.

Does FAISS support GPU acceleration?▼

Yes, install faiss-gpu and use faiss.index_cpu_to_gpu(res, 0, index) for a single GPU or faiss.index_cpu_to_all_gpus(index) for multi-GPU, achieving 10-100x speedup over CPU.

How do I use cosine similarity with FAISS?▼

Use faiss.IndexFlatIP(d) for inner product search and normalize vectors first with faiss.normalize_L2(vectors). Normalized inner product is equivalent to cosine similarity.

Why does my FAISS IVF index return poor results?▼

IVF indices require training on representative data via index.train(vectors) before adding vectors. Also increase index.nprobe to search more clusters, trading speed for higher recall.

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