AutoFaiss

Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Using faiss efficient indices, binary search, and heuristics, Autofaiss makes it possible to automatically build in 3 hours a large (200 million vectors, 1TB) KNN index in a low amount of memory (15 GB) with latency in milliseconds (10ms).

Get started by running this colab notebook, then check the full documentation.

How to use autofaiss?

To install run pip install autofaiss

It's probably best to create a virtual env:

python -m venv .venv/autofaiss_env
source .venv/autofaiss_env/bin/activate
pip install -U pip
pip install autofaiss

Create embeddings

import os
import numpy as np
embeddings = np.random.rand(1000, 100)
os.mkdir("embeddings")
np.save("embeddings/part1.npy", embeddings)
os.mkdir("my_index_folder")

Generate a Knn index

autofaiss quantize --embeddings_path="embeddings" --output_path="my_index_folder" --metric_type="ip"

Try the index

import faiss
import glob
import numpy as np

my_index = faiss.read_index(glob.glob("my_index_folder/*.index")[0])

query_vector = np.float32(np.random.rand(1, 100))
k = 5
distances, indices = my_index.search(query_vector, k)

print(list(zip(distances[0], indices[0])))

How are indices selected ?

To understand better why indices are selected and what are their characteristics, check the index selection demo

Command quick overview

Quick description of the autofaiss quantize command:

embeddings_path -> Source path of the embeddings in numpy.
output_path -> Destination path of the created index. metric_type -> Similarity distance for the queries.

index_key -> (optional) Describe the index to build.
index_param -> (optional) Describe the hyperparameters of the index.
current_memory_available -> (optional) Describe the amount of memory available on the machine.
use_gpu -> (optional) Whether to use GPU or not (not tested).

Command details

The autofaiss quantize command takes the following parameters:

Install from source

First, create a virtual env and install dependencies:

python -m venv .venv/autofaiss_env
source .venv/autofaiss_env/bin/activate
make install