SuperAPI AI Directory
Partner中文AI newsFavorites
Skillsfaiss

faiss

Data & analytics

View repository

Updated 10-21 16:17

About this skill

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:

  • 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

  1. Choose right index type - Flat for <10K, IVF for 10K-1M, HNSW for quality
  2. Normalize for cosine - Use IndexFlatIP with normalized vectors
  3. Use GPU for large datasets - 10-100× faster
  4. Save trained indices - Training is expensive
  5. Tune nprobe/ef_search - Balance speed/accuracy
  6. Monitor memory - PQ for large datasets
  7. 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

Agent operating procedure

  1. Check the environment. Confirm the corpus, embedding model and vector store versions, and where the index will live.
  2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
  3. Run a small version first. Index a small subset and test retrieval on known question-passage pairs.
  4. Execute the full task using the instructions and references above.
  5. Validate the result. Measure recall@k on a labeled set; answers cite retrieved passages only.
  6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.
If this happens Do this
Retrieval returns irrelevant chunks Revisit chunking, add BM25/hybrid search, or re-rank before changing the generator.
A function, flag or endpoint in these instructions is missing in the installed version Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous Ask the user, or state the assumption explicitly before running.

Integrity rules

  • Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
  • Answers must come from retrieved text; say when the corpus does not contain the answer.
  • Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
  • Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

  • pinecone: Guides use of Pinecone, a managed serverless vector database, through its Python client and the LangChain and LlamaIndex integrations.
  • qdrant-vector-search: High-performance vector similarity search engine for RAG and semantic search.
  • chroma: Open-source embedding database for AI applications.