Fast vector similarity search at billion scale.
Skill metadata
| Source | Optional — install with hermes skills install official/mlops/faiss |
| Path | optional-skills/mlops/faiss |
| Version | 1.0.0 |
| Author | Orchestra Research |
| License | MIT |
| Dependencies | faiss-cpu, faiss-gpu, numpy |
| Platforms | linux, macos |
| Tags | RAG, FAISS, Similarity Search, Vector Search, Facebook AI, GPU Acceleration, Billion-Scale, K-NN, HNSW, High Performance, Large Scale |
Reference: full SKILL.md
info
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
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