faiss

# 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)