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