Faiss

orchestra-research/ai-research-skills/15-rag/faiss

作者 orchestra-research773a52944ba4MIT13K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 個月前更新

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.

僅含說明AI & Agents

AI 產生的概覽

使用 FAISS 建立與調校向量相似度搜尋索引的參考指南。

功能: 此技能是 Facebook AI 的 FAISS 函式庫參考指南，用於稠密向量的相似度搜尋與分群。內容說明安裝方式、索引類型（Flat、IVF、HNSW、乘積量化）、索引的儲存與載入、GPU 加速，以及與 LangChain/LlamaIndex 的整合，並附有程式碼範例與效能比較。它產出的是指引說明與範例程式碼，而非可執行成品。
適用情境: 當你需要快速 k 近鄰搜尋或大規模向量檢索（包括十億級資料集或 GPU 加速搜尋）時使用。它也適合在 FAISS 各種索引類型之間做選擇，或將 FAISS 與 Chroma、Pinecone、Weaviate、Annoy 等替代方案進行比較。
執行需求: 需要 faiss-cpu 或 faiss-gpu Python 套件以及 numpy；GPU 加速需要 faiss-gpu 與合適的硬體。選用整合涉及 LangChain、LlamaIndex 與 OpenAI 嵌入。此技能未附帶指令碼，只有說明文件與一份參考文件。

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

Basic usage

python

import faissimport numpy as np
# Create sample data (1000 vectors, 128 dimensions)d = 128nb = 1000vectors = np.random.random((nb, d)).astype('float32')
# Create indexindex = faiss.IndexFlatL2(d)  # L2 distanceindex.add(vectors)             # Add vectors
# Searchk = 5  # Find 5 nearest neighborsquery = np.random.random((1, d)).astype('float32')distances, indices = index.search(query, k)
print(f"Nearest neighbors: {indices}")print(f"Distances: {distances}")

import faissimport numpy as np
# Create sample data (1000 vectors, 128 dimensions)d = 128nb = 1000vectors = np.random.random((nb, d)).astype('float32')
# Create indexindex = faiss.IndexFlatL2(d)  # L2 distanceindex.add(vectors)             # Add vectors
# Searchk = 5  # Find 5 nearest neighborsquery = 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)

python

# L2 (Euclidean) distanceindex = faiss.IndexFlatL2(d)
# Inner product (cosine similarity if normalized)index = faiss.IndexFlatIP(d)
# Slowest, most accurate

# L2 (Euclidean) distanceindex = faiss.IndexFlatL2(d)
# Inner product (cosine similarity if normalized)index = faiss.IndexFlatIP(d)
# Slowest, most accurate

2. IVF (inverted file) - Fast approximate

python

# Create quantizerquantizer = faiss.IndexFlatL2(d)
# IVF index with 100 clustersnlist = 100index = faiss.IndexIVFFlat(quantizer, d, nlist)
# Train on dataindex.train(vectors)
# Add vectorsindex.add(vectors)
# Search (nprobe = clusters to search)index.nprobe = 10distances, indices = index.search(query, k)

# Create quantizerquantizer = faiss.IndexFlatL2(d)
# IVF index with 100 clustersnlist = 100index = faiss.IndexIVFFlat(quantizer, d, nlist)
# Train on dataindex.train(vectors)
# Add vectorsindex.add(vectors)
# Search (nprobe = clusters to search)index.nprobe = 10distances, indices = index.search(query, k)

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

python

# HNSW indexM = 32  # Number of connections per layerindex = faiss.IndexHNSWFlat(d, M)
# No training neededindex.add(vectors)
# Searchdistances, indices = index.search(query, k)

# HNSW indexM = 32  # Number of connections per layerindex = faiss.IndexHNSWFlat(d, M)
# No training neededindex.add(vectors)
# Searchdistances, indices = index.search(query, k)

4. Product Quantization - Memory efficient

python

# PQ reduces memory by 16-32×m = 8   # Number of subquantizersnbits = 8index = faiss.IndexPQ(d, m, nbits)
# Train and addindex.train(vectors)index.add(vectors)

# PQ reduces memory by 16-32×m = 8   # Number of subquantizersnbits = 8index = faiss.IndexPQ(d, m, nbits)
# Train and addindex.train(vectors)index.add(vectors)

Save and load

python

# Save indexfaiss.write_index(index, "large.index")
# Load indexindex = faiss.read_index("large.index")
# Continue usingdistances, indices = index.search(query, k)

# Save indexfaiss.write_index(index, "large.index")
# Load indexindex = faiss.read_index("large.index")
# Continue usingdistances, indices = index.search(query, k)

GPU acceleration

python

# Single GPUres = faiss.StandardGpuResources()index_cpu = faiss.IndexFlatL2(d)index_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0
# Multi-GPUindex_gpu = faiss.index_cpu_to_all_gpus(index_cpu)
# 10-100× faster than CPU

# Single GPUres = faiss.StandardGpuResources()index_cpu = faiss.IndexFlatL2(d)index_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0
# Multi-GPUindex_gpu = faiss.index_cpu_to_all_gpus(index_cpu)
# 10-100× faster than CPU

LangChain integration

python

from langchain_community.vectorstores import FAISSfrom langchain_openai import OpenAIEmbeddings
# Create FAISS vector storevectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
# Savevectorstore.save_local("faiss_index")
# Loadvectorstore = FAISS.load_local(    "faiss_index",    OpenAIEmbeddings(),    allow_dangerous_deserialization=True)
# Searchresults = vectorstore.similarity_search("query", k=5)

from langchain_community.vectorstores import FAISSfrom langchain_openai import OpenAIEmbeddings
# Create FAISS vector storevectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
# Savevectorstore.save_local("faiss_index")
# Loadvectorstore = FAISS.load_local(    "faiss_index",    OpenAIEmbeddings(),    allow_dangerous_deserialization=True)
# Searchresults = vectorstore.similarity_search("query", k=5)

LlamaIndex integration

python

from llama_index.vector_stores.faiss import FaissVectorStoreimport faiss
# Create FAISS indexd = 1536faiss_index = faiss.IndexFlatL2(d)
vector_store = FaissVectorStore(faiss_index=faiss_index)

from llama_index.vector_stores.faiss import FaissVectorStoreimport faiss
# Create FAISS indexd = 1536faiss_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

Resources

GitHub: https://github.com/facebookresearch/faiss ⭐ 31,700+
Wiki: https://github.com/facebookresearch/faiss/wiki
License: MIT

來源與署名

來源：orchestra-research/ai-research-skills位於15-rag/faiss提交773a529

授權條款： MIT

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

檢舉或申請下架

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

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

來源與署名

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