Agent Skills
› synthetic-sciences/openscience
› faiss
faiss
GitHub用于十亿级向量的高效相似性搜索与聚类。支持GPU加速、多种索引类型及纯向量检索，适用于高吞吐低延迟场景。无需元数据过滤时优于Chroma等数据库，适合大规模嵌入的k-NN搜索。
Trigger Scenarios
需要大规模向量相似性搜索
要求GPU加速或低延迟检索
纯向量匹配且无元数据过滤需求
处理十亿级向量数据集
Install
npx skills add synthetic-sciences/openscience --skill faiss -g -y
SKILL.md
Frontmatter
{
"name": "faiss",
"tags": [
"RAG",
"FAISS",
"Similarity Search",
"Vector Search",
"Facebook AI",
"GPU Acceleration",
"Billion-Scale",
"K-NN",
"HNSW",
"High Performance",
"Large Scale"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "llm-tools",
"description": "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.",
"dependencies": [
"faiss-cpu",
"faiss-gpu",
"numpy"
]
}
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
# CPU only
pip install faiss-cpu
# GPU support
pip install faiss-gpu
Basic usage
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 # Find 5 nearest neighbors
query = np.random.random((1, d)).astype('float32')
distances, indices = index.search(query, k)
print(f"Nearest neighbors: {indices}")
print(f"Distances: {distances}")
Index types
1. Flat (exact search)
# L2 (Euclidean) distance
index = faiss.IndexFlatL2(d)
# Inner product (cosine similarity if normalized)
index = faiss.IndexFlatIP(d)
# Slowest, most accurate
2. IVF (inverted file) - Fast approximate
# Create quantizer
quantizer = faiss.IndexFlatL2(d)
# IVF index with 100 clusters
nlist = 100
index = faiss.IndexIVFFlat(quantizer, d, nlist)
# Train on data
index.train(vectors)
# Add vectors
index.add(vectors)
# Search (nprobe = clusters to search)
index.nprobe = 10
distances, indices = index.search(query, k)
3. HNSW (Hierarchical NSW) - Best quality/speed
# HNSW index
M = 32 # Number of connections per layer
index = faiss.IndexHNSWFlat(d, M)
# No training needed
index.add(vectors)
# Search
distances, indices = index.search(query, k)
4. Product Quantization - Memory efficient
# PQ reduces memory by 16-32×
m = 8 # Number of subquantizers
nbits = 8
index = faiss.IndexPQ(d, m, nbits)
# Train and add
index.train(vectors)
index.add(vectors)
Save and load
# Save index
faiss.write_index(index, "large.index")
# Load index
index = faiss.read_index("large.index")
# Continue using
distances, indices = index.search(query, k)
GPU acceleration
# Single GPU
res = faiss.StandardGpuResources()
index_cpu = faiss.IndexFlatL2(d)
index_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu) # GPU 0
# Multi-GPU
index_gpu = faiss.index_cpu_to_all_gpus(index_cpu)
# 10-100× faster than CPU
LangChain integration
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
# Create FAISS vector store
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
# Save
vectorstore.save_local("faiss_index")
# Load
vectorstore = FAISS.load_local(
"faiss_index",
OpenAIEmbeddings(),
allow_dangerous_deserialization=True
)
# Search
results = vectorstore.similarity_search("query", k=5)
LlamaIndex integration
from llama_index.vector_stores.faiss import FaissVectorStore
import faiss
# Create FAISS index
d = 1536
faiss_index = faiss.IndexFlatL2(d)
vector_store = FaissVectorStore(faiss_index=faiss_index)
Best practices
- Choose right index type - Flat for <10K, IVF for 10K-1M, HNSW for quality
- Normalize for cosine - Use IndexFlatIP with normalized vectors
- Use GPU for large datasets - 10-100× faster
- Save trained indices - Training is expensive
- Tune nprobe/ef_search - Balance speed/accuracy
- Monitor memory - PQ for large datasets
- Batch queries - Better GPU utilization
Performance
| Index Type | Build Time | Search Time | Memory | Accuracy |
|---|---|---|---|---|
| Flat | Fast | Slow | High | 100% |
| IVF | Medium | Fast | Medium | 95-99% |
| HNSW | Slow | Fastest | High | 99% |
| PQ | Medium | Fast | Low | 90-95% |
Resources
- GitHub: https://github.com/facebookresearch/faiss ⭐ 31,700+
- Wiki: https://github.com/facebookresearch/faiss/wiki
- License: MIT
Version History
- e9844a4 Current 2026-07-11 17:26
Dependencies
-
required
faiss-cpu -
required
faiss-gpu -
required
numpy
