prrao87/tea-hypervectors

Cha Yi Tea Profiles in Lance Format

Dataset of loose-leaf teas for multimodal research and experiments in LanceDB. The data is stored in Lance format, including normalized JPEG bytes and vetted tea metadata.

Dataset description

This dataset contains 166 English-language loose-leaf tea profiles collected from the public storefront of Maison de thé Cha Yi, a tea vendor based in Gatineau, Quebec, Canada. It covers the store's Oolong, green, white, black, and yellow tea collections.

The data is stored in Lance format, and searched using LanceDB, a multimodal lakehouse for AI.

Each row combines product text, a tea-class label, sensory and processing labels, region and elevation metadata, a source URL, and the primary product image stored as bytes. The dataset is distributed as a ready-to-query local Lance table with a full-text search index over the product description.

This is a small, point-in-time research snapshot. It is not an official Cha Yi dataset or a live copy of the store catalog.

Dataset summary

Tea class	Rows
Black	51
Green	65
Oolong	35
White	12
Yellow	3
Total	166

• Language: English
• Modalities: Text, images, tabular metadata
• Format: Lance
• Dataset path: data/train.lance
• Rows: 166
• Hub split: train (the complete 166-row snapshot)
• Geographic context: Canadian retailer; teas originate from multiple tea-producing regions
• Metadata collected: July 26, 2026
• Vetted snapshot finalized: July 28, 2026

Dataset structure

The Hugging Face repository contains the Lance dataset at data/train.lance/, alongside this dataset card at root-level README.md. This matches the structure used by lance-format/textvqa-lance.

Fields

Field	Type	Description
id	int64	Stable product identifier from the source storefront
source_url	string	Direct link to the corresponding Cha Yi product page
image	binary	Bytes of the normalized JPEG product image
class	string	One of black, green, oolong, white, or yellow
title	string	English product display title
description	string	English product description and tasting or brewing text
country	string, nullable	Tea origin country when available
region	string	Vetted growing-region label
oxidation	string	Derived oxidation label
roast	string	Derived roast label
aroma	list<string>	Description-grounded aroma spans
taste	list<string>	Description-grounded taste spans
elevation_meters	int64	Vetted representative growing elevation
elevation_confidence	float64	Confidence in the elevation value, from 0 to 1

The included named full-text search index is description_fts, built on description.

Data splits

The complete snapshot is exposed as the Hub's train split so the dataset viewer can render it. This is a serving convention, not a predefined evaluation split. Users who evaluate models should define and report their own split strategy. Be mindful that related teas, regional styles, and naming patterns can make a random row-level split easier than a real-world generalization task.

Load and query the dataset

Open the Lance dataset directly from the Hub:

import lance

dataset = lance.dataset(
    "hf://datasets/prrao87/tea-hypervectors/data/train.lance"
)

rows = (
    dataset.scanner(
        columns=[
            "title",
            "class",
            "description",
            "region",
            "elevation_meters",
            "source_url",
        ],
        limit=5,
    )
    .to_table()
    .to_pylist()
)
print(rows)

Filter and project metadata without materializing the binary image column:

oolongs = (
    dataset.scanner(
        columns=[
            "id",
            "title",
            "class",
            "description",
            "region",
            "elevation_meters",
            "source_url",
        ],
        filter="class = 'oolong'",
        limit=3,
    )
    .to_table()
    .to_pylist()
)
print(oolongs)

The repository uses LanceDB rather than a standard Parquet-backed Hugging Face datasets configuration, so it may not have the usual Dataset Viewer preview.

Dataset creation

Source data

All records were derived from public pages and public collection data on the Cha Yi storefront. Every row retains a source_url for traceability and for checking the current product page.

The extraction process:

1. Discovered products in five public tea collections.
2. Requested each public product page with English Canadian language preferences.
3. Kept English display text and class information.
4. Derived oxidation, roast, aroma, and taste labels.
5. Estimated and manually vetted region/elevation metadata.
6. Downloaded and normalized one JPEG image per exported tea.
7. Used Polars to select the serving schema and attach local image bytes.
8. Ingested the Polars DataFrame into LanceDB.
9. Built a named full-text search index over description.

The metadata extraction made 171 sequential requests over approximately 172 seconds. All collection, product-page, retry, and image requests were separated by at least one second. Cached normalized JPEGs were reused during repeat ingestions.

Language processing

The extractor requested locale=en and sent Accept-Language: en-CA. It did not translate source content. Text detected as French was omitted from the language fields, while source URLs and public handles were preserved even when they contained French words.

The source storefront's tag list is partly French. The richer source export therefore retained only a conservative allowlist of unambiguously English tags and proper tea or place names. The LanceDB table contains the smaller serving schema documented above rather than the complete extraction schema.

Labels

The class label comes from the one selected storefront collection associated with each product:

• black
• green
• oolong
• white
• yellow

These labels are useful for experiments but should not be treated as a comprehensive or authoritative tea taxonomy.

Personal and sensitive information

The dataset consists of public commercial product information. It was not designed to include personal, private, or sensitive information.

Intended uses

This dataset is intended for non-commercial educational and research work, including:

• multimodal and text retrieval experiments;
• tea-type image or text classification;
• feature engineering and metadata enrichment;
• full-text, vector, or hybrid search prototypes;
• demonstrations of storing binary images alongside structured metadata in LanceDB.

Users should preserve source attribution and follow each row's source_url when current product information matters.

Limitations

• The dataset represents one retailer and a small catalog, so it is not representative of the global tea market.
• The five classes are imbalanced, with only three yellow teas.
• Labels follow storefront collection membership rather than an independently validated botanical or production taxonomy.
• Descriptions and photography were written and selected for retail use and may encode the vendor's editorial and marketing perspective.
• Prices, availability, descriptions, brewing guidance, and images may have changed since the snapshot was collected.
• English-only filtering removes some of the source storefront's multilingual information.
• Product images can include differences in composition, lighting, packaging, and presentation that models may learn instead of tea-specific features.
• There are no predefined splits or benchmark metrics.

Licensing and attribution

The dataset curation is released under the Creative Commons Attribution-NonCommercial 4.0 International license to the extent that it is licensable by the dataset maintainer.

All underlying intellectual property—including tea names, tasting descriptions, vendor branding, and product photography—remains the property of Maison de thé Cha Yi and its respective rights holders. The dataset license does not relicense or override those underlying rights.

• Original source: Maison de thé Cha Yi
• Record-level attribution: Each row contains a direct source_url
• Permitted purpose: Non-commercial education and research

If you use the dataset, retain attribution, link back to the original product pages, and consider supporting the vendor by buying their products online, and spreading the word on social media.

Citation

There is no formal academic citation for this dataset. If you use it, please cite the dataset repository and acknowledge Maison de thé Cha Yi as the copyright holder of the underlying product content. For example:

@dataset{chayi_tea_lancedb_2026,
  title        = {Cha Yi Tea Profiles in LanceDB},
  year         = {2026},
  month        = {7},
  publisher    = {Hugging Face},
  note         = {Underlying product content and photography from Maison de thé Cha Yi},
  url          = {https://huggingface.co/datasets/prrao87/tea-hypervectors}
}