faiss

name	faiss
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.
version	1.0.0
author	Orchestra Research
license	MIT
dependencies	["faiss-cpu","faiss-gpu","numpy"]
platforms	["linux","macos"]
metadata	{"sonic":{"tags":["RAG","FAISS","Similarity Search","Vector Search","Facebook AI","GPU Acceleration","Billion-Scale","K-NN","HNSW","High Performance","Large Scale"]}}

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)