faiss

SKILL.md

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

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