Faiss

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

by orchestra-research773a52944ba4MIT13K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 months ago

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.

Instructions onlyAI & Agents

AI-generated overview

Reference guide for using FAISS to build and tune vector similarity search indexes.

What it does: This skill is a reference guide for Facebook AI's FAISS library for similarity search and clustering of dense vectors. It documents installation, index types (Flat, IVF, HNSW, Product Quantization), saving and loading indexes, GPU acceleration, and LangChain/LlamaIndex integration, with code examples and performance comparisons. It produces guidance and example code rather than executable artifacts.
When to use it: Use it when you need fast k-nearest-neighbor search or large-scale vector retrieval, including billion-scale datasets or GPU-accelerated search. It is also relevant when choosing between FAISS index types or comparing FAISS with alternatives such as Chroma, Pinecone, Weaviate, or Annoy.
Requirements: Requires the faiss-cpu or faiss-gpu Python package and numpy; GPU acceleration requires faiss-gpu and suitable hardware. Optional integrations reference LangChain, LlamaIndex, and OpenAI embeddings. The skill ships no scripts, only instructions and a reference document.

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

Source and attribution

Source:orchestra-research/ai-research-skillsin15-rag/faissat commit773a529

License: MIT

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from orchestra-research/ai-research-skills

Speculative Decoding

orchestra-research

Guides accelerating LLM inference with speculative decoding, Medusa multi-head prediction, and lookahead decoding.

AI & Agents13Kupdated 3 months ago

Systems Paper Writing

orchestra-research

Guides paragraph-level structure and page budgets for writing systems conference papers such as OSDI, SOSP, ASPLOS and NSDI.

Writing & Content13Kupdated 3 months ago

Moe Training

orchestra-research

Guides training and inference of Mixture of Experts models with DeepSpeed or HuggingFace, covering routing, load balancing and expert parallelism.

AI & Agents13Kupdated 3 months ago

Presenting Conference Talks

orchestra-research

Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.

Awaiting classification13Kupdated 3 months ago

Model Pruning

orchestra-research

Guides LLM pruning with Wanda, SparseGPT, magnitude and N:M methods to cut model size and speed inference.

AI & Agents13Kupdated 3 months ago

Outlines

orchestra-research

Guides use of the Outlines library for structured, schema-constrained text generation with local models.

AI & Agents13Kupdated 3 months ago

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

Source and attribution

More from orchestra-research/ai-research-skills

Speculative Decoding

Systems Paper Writing

Moe Training

Presenting Conference Talks

Model Pruning

Outlines

More in AI & Agents

Ppt Template Creator

Skill Development

Plugin Structure

Command Development

Claude Md Improver

Find Skills