faiss

Facebook's library for finding, among millions or billions of items, the handful most like the one you are holding — with knobs to trade accuracy for speed or memory, and an option to run on the graphics card.

WHEN TO USE

SCOPE

USE FOR

Find the k nearest vectors to a query across a large embedding collection.
Pick an index type — flat, IVF, HNSW, or product quantization — to balance speed against memory.
Move an index onto one or more GPUs for a 10-100x search speedup.
Save a trained index to disk and reload it later without retraining.

NOT FOR

When results need metadata filtering, use Chroma or Pinecone instead.
When full database features are needed, use Weaviate instead.
For a simpler library with fewer features, use Annoy instead.

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In this document (11)

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

This skill ships inside the AI Research Skills plugin.

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What's inside

Files included with this skill.

View on GitHub

FAISS - Efficient Similarity Search

When to use FAISS

Quick start

Installation

Basic usage

Index types

1. Flat (exact search)

2. IVF (inverted file) - Fast approximate

3. HNSW (Hierarchical NSW) - Best quality/speed

4. Product Quantization - Memory efficient

Save and load

GPU acceleration

LangChain integration

LlamaIndex integration

Best practices

Performance

Resources

Local tool required

What's inside

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