metadata
license: apache-2.0
dataset_info:
features:
- name: parent_asin
dtype: string
- name: value
list: float64
- name: main_category
dtype: string
- name: title
dtype: string
- name: average_rating
dtype: float64
- name: rating_number
dtype: float64
- name: description
dtype: string
- name: price
dtype: float64
- name: categories
dtype: string
- name: image_url
dtype: string
splits:
- name: train
num_bytes: 3482499106
num_examples: 100000
download_size: 2309398330
dataset_size: 3482499106
configs:
- config_name: 10k
data_files:
- split: train
path: benchmark-10k/*.parquet
- config_name: 100k
data_files:
- split: train
path: benchmark-100k/*.parquet
- config_name: 1M
data_files:
- split: train
path: benchmark-1M/*.parquet
- config_name: 10M
data_files:
- split: train
path: benchmark-10M/*.parquet
Vector Search Benchmarks
This repo contains datasets for benchmarking vector search performance, to help Superlinked prioritize integration partners. For performing actual benchmarking on this dataset, see the github repository README.
Overview
We reviewed a number of publicly available datasets and noted 3 core problems + here is how this dataset fixes them:
| Problems of other vector search benchmarks | How this dataset solves it |
|---|---|
| Not enough metadata of various types makes it hard to test filter performance | 3 number, 1 categorical, 3 text, 1 image column |
| Vectors too small, while SOTA models usually output 2k+ even 4k+ dims | 4154 dims |
| Dataset too small, especially if larger vectors are used | 100k, 1M and 10M item variants, all sampled from the large dataset |
Available Datasets
Product data
The data_dirs contain parquet files with the metadata and vectors.
| Dataset | Records | # Files | Size |
|---|---|---|---|
| benchmark_10k | 10,000 | 100 | ~230 MB |
| benchmark_100k | 100,000 | 100 | ~2.3 GB |
| benchmark_1M | 1,000,000 | 100 | ~23 GB |
| benchmark_10M | 10,534,536 | 1000 | ~240 GB |
The structure of the files is the same throughout:
Schema([('parent_asin', String), # the id
('main_category', String),
('title', String),
('average_rating', Float64),
('rating_number', Float64),
('description', String),
('price', Float64),
('categories', String),
('image_url', String)])
('value', List(Float64)), # the vectors
Data Access
The product metadata and vectors are available using HF Datasets.
from datasets import load_dataset
benchmark_10k = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-10k")
benchmark_100k = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-100k")
benchmark_1M = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-1M")
benchmark_10M = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-10M")
Dataset Production
Source Data
- Origin: Amazon Reviews 2023 dataset
- Categories:
["Books", "Automotive", "Tools and Home Improvement", "All Beauty", "Electronics", "Software", "Health and Household"]
Embeddings
The embeddings are created via a superlinked config. The resulting 4154 dim vector contains:
- 1 categorical,
- 3 number,
- 3 text (
Qwen/Qwen3-Embedding-0.6B), - and 1 image (
laion/CLIP-ViT-H-14-laion2B-s32B-b79K)
embeddings concatenated.