/rag-faiss

name: rag-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.
license: MIT
metadata:
  version: 1.0.0
  author: Orchestra Research
  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]

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)

# 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

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