metadata

license: odc-by
language:
  - en
size_categories:
  - 1M<n<10M
dataset_info:
  features:
    - name: uid
      dtype: string
    - name: vendor
      dtype: string
    - name: title
      dtype: string
    - name: paragraph
      dtype: string
    - name: embedding
      dtype:
        sequence: float32
task_categories:
  - text-retrieval
  - sentence-similarity
task_ids:
  - document-retrieval
  - semantic-similarity-classification
tags:
  - ecommerce
  - small-business
  - rag
  - grounding
  - vector-search
  - open-data
  - embedding
  - tokuhn
  - shopify
  - real-world-data
  - sbert
  - huggingface-datasets

[Updated with SBERT Embeddings + Search Notebook]

TSMPD‑US: U.S. Small Merchant Product Dataset + SBERT Embeddings + Search Notebook

⚡ New in this release (April 2025):

SBERT vector embeddings for all products (MiniLM‑L6)

Chunked Parquet format for scalable vector search

Jupyter notebook demo for live semantic queries

These additions make it easier to integrate small merchant data into RAG pipelines, grounding tasks, and real-time AI agents.

An open-source initiative to keep small merchants visible in LLMs, RAG systems, and AI-powered commerce workflows.**

This repository contains multiple assets for the TSMPDUS dataset a structured, U.S.-only dataset of verified small business product listings, curated from over 355,000 independent stores. It is designed for:

Semantic product search
LLM grounding and fine-tuning
Retrieval-Augmented Generation (RAG)
Metadata classification
Commerce-aware agent design

Directory Overview

`public-products/`

A lightweight, text-only snapshot of the dataset.

~3.2M products from 355,000+ verified U.S. merchants
~10 products per merchant, no images or variant details
Suitable for general research, classification, and basic grounding tasks

Includes:

tsmpd_public_v1.0.json or .parquet core dataset
LICENSE.txt ODC-By license
README.md Format & schema details

`parquet-embeddings/`

Semantic searchready version of the dataset with SBERT embeddings (MiniLML6).

Split into Parquet chunks for scalability
Embeddings aligned with Hugging Face sentence-transformers/all-MiniLM-L6-v2

Use cases:

Vector search & similarity pipelines
Retrieval-Augmented Generation (RAG)
AI agent product reasoning

Includes:

tsmpd_public_000.parquet, ...001.parquet, etc.
README.md Usage notes + embedding shape
LICENSE.txt Same ODC-By license unless extended

`notebook-demo/`

A minimal working demo for semantic product search over the embedded dataset.

Loads Parquet embeddings
Performs cosine similarity on live queries
Displays top product hits from the network

Includes:

tsmpd_search_demo.ipynb Search notebook
README.md Instructions & dependencies

Why This Matters

Large models like ChatGPT and Claude do not crawl small stores the way Google does. Without structured visibility, the long tail of small commerce risks becoming invisible in AI-powered discovery systems.

TSMPD-US is designed to prevent that by making small merchant data accessible, embeddable, and usable in todays LLM workflows.

Licensing

All public assets are distributed under the Open Data Commons Attribution License (ODCBy).

For full product variants, image URLs, merchant domains, and source tracking, request access to the Partner dataset by emailing jim@tokuhn.com.

How to Use This Repository

Load the text-only dataset via Hugging Face Datasets or polars
Run vector search with the SBERT Parquet chunks
Adapt the notebook demo for your own semantic or retrieval tasks
Fine-tune or evaluate grounding quality with real-world small merchant data

Lets make sure AI doesnt erase the 99%.