feat: README update

README.md

@@ -34,3 +34,75 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# 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](https://github.com/superlinked/external-benchmarks).
+
+## 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_dir`s 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](https://huggingface.co/docs/datasets/en/index).
+
+```python
+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](https://amazon-reviews-2023.github.io/)
+- **Categories**: `["Books", "Automotive", "Tools and Home Improvement", "All Beauty", "Electronics", "Software", "Health and Household"]`
+
+### Embeddings
+
+The embeddings are created via a [superlinked config](superlinked_app). 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.