faiss

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.

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Repositorio: davila7/claude-code-templates
Última actividad en el origen: 8 de enero de 2026 a las 14:24
Idioma detectado de SKILL.md: inglés
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Forks: 3394

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npx skills add https://github.com/davila7/claude-code-templates --skill faiss

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# CPU only
pip install faiss-cpu

# GPU support
pip install faiss-gpu

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}")

# L2 (Euclidean) distance
index = faiss.IndexFlatL2(d)

# Inner product (cosine similarity if normalized)
index = faiss.IndexFlatIP(d)

# Slowest, most accurate

# 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)

# 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)

# 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 index
faiss.write_index(index, "large.index")

# Load index
index = faiss.read_index("large.index")

# Continue using
distances, indices = index.search(query, k)

# 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

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)

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)

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
tags	["RAG","FAISS","Similarity Search","Vector Search","Facebook AI","GPU Acceleration","Billion-Scale","K-NN","HNSW","High Performance","Large Scale"]
dependencies	["faiss-cpu","faiss-gpu","numpy"]

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
tags	["RAG","FAISS","Similarity Search","Vector Search","Facebook AI","GPU Acceleration","Billion-Scale","K-NN","HNSW","High Performance","Large Scale"]
dependencies	["faiss-cpu","faiss-gpu","numpy"]

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%

faiss

Opciones de instalación

Revisa los archivos de origen

FAISS - Efficient Similarity Search

When to use FAISS

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

faiss

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

FAISS - Efficient Similarity Search

When to use FAISS

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