weaviate-demo-datasets

UNOFFICIAL Weaviate demo data uploader

This is an educational project that aims to make it easy to upload demo data to your instance of Weaviate. The target audience is developers learning how to use Weaviate.

Usage

pip install -U weaviate-demo-datasets

Each dataset includes a default vectorizer configuration for convenience. The target Weaviate instance must include the specified vectorizer module.

Once you instantiate a dataset, you can upload it to Weaviate with the following:

import weaviate_datasets as wd
dataset = wd.JeopardyQuestions1k()  # Instantiate dataset
dataset.upload_dataset(client)  # Pass the Weaviate client instance

Where client is the instantiated weaviate.WeaviateClient object, such as:

import weaviate
import os

client = weaviate.connect_to_local(
    headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_APIKEY")}
)

To use a weaviate.Client object, use 0.5.x or older version of this package.

Built-in methods

.upload_dataset(client) - add defined classes to schema, adds objects
.get_sample() - yields sample data object(s)

Available classes

Wiki100 (Top 100 Wikipedia articles)
- WikiChunk collection
- Various chunking options available:
  - Default: wiki_sections (sections of the Wikipedia article)
  - wiki_section_chunked (sections of the Wikipedia article, chunked into 200 character chunks)
  - wiki_heading_only (only the headings of the Wikipedia article sections)
  - fixed (fixed length chunks of 200 characters)
- Use it as follows:
```
d = wd.Wiki100()
d.collection_name = "WikiChunk"
d.set_chunking("wiki_section_chunked")
upload_responses = d.upload_dataset(client, overwrite=True)
```
WineReviews (50 wine reviews)
- WineReview collection
WineReviewsNV (50 wine reviews)
- WineReviewNV collection, with named vectors ("title", "review_body", and "title_country")
  - "title_country" -> Vector from concatenation of "title" + "country"
WineReviewsMT (50 wine reviews)
- WineReviewMT collection, tenants tenantA and tenantB
JeopardyQuestions1k (1,000 Jeopardy questions & answers, vectorized with OpenAI text-embedding-ada-002)
- JeopardyQuestion and JeopardyCategory collections
JeopardyQuestions10k (10,000 Jeopardy questions & answers, vectorized with OpenAI text-embedding-ada-002)
- JeopardyQuestion and JeopardyCategory collections
NewsArticles (News articles, including their corresponding publications, authors & categories, vectorized with OpenAI text-embedding-ada-002)

Available classes - V3 collection

These are available with a V3 suffix, and are compatible with the Weaviate Python client v3.x.

Not including vectors

WineReviews (50 wine reviews)
WineReviewsMT (50 wine reviews, multi-tenancy enabled)

Including vectors

JeopardyQuestions1k (1,000 Jeopardy questions & answers, vectorized with OpenAI text-embedding-ada-002)
JeopardyQuestions10k (10,000 Jeopardy questions & answers, vectorized with OpenAI text-embedding-ada-002)
JeopardyQuestions1kMT (1,000 Jeopardy questions & answers, multi-tenancy enabled, vectorized with OpenAI text-embedding-ada-002)
NewsArticles (News articles, including their corresponding publications, authors & categories, vectorized with OpenAI text-embedding-ada-002)

Data sources

https://www.kaggle.com/datasets/zynicide/wine-reviews https://www.kaggle.com/datasets/tunguz/200000-jeopardy-questions https://github.com/weaviate/DEMO-NewsPublications

Source code

https://github.com/databyjp/wv_demo_uploader

Metadata

Release files for weaviate-demo-datasets 0.8.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for weaviate-demo-datasets 0.8.1
File	Size	Uploaded
weaviate_demo_datasets-0.8.1.tar.gz	69.5 MB	Nov 14, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for weaviate-demo-datasets 0.8.1
File	Interpreter	ABI	Platform	Reset
weaviate_demo_datasets-0.8.1-py3-none-any.whl 73.9 MB Nov 14, 2025	Python 3	none	any	Details

Total release size: 143.4 MB

Release files / weaviate_demo_datasets-0.8.1.tar.gz

Download URL	weaviate_demo_datasets-0.8.1.tar.gz
Size	69.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`d2b23f138cf4c5c3f9142869be093a8ac645cd3c266a5106fb64b8d9b1f045a4`
BLAKE2b-256 checksum How to use checksums	`ae6b558755735d030542b28c5a0a8c051691409930e4975983b97d2e8d926602`
Upload date	Nov 14, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.10.12`

Release files / weaviate_demo_datasets-0.8.1-py3-none-any.whl

Download URL	weaviate_demo_datasets-0.8.1-py3-none-any.whl
Size	73.9 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`1495bd908200f0dac88c6c2aa4a8bc8c5a0b1b4636b407232a79be7ffd9a1291`
BLAKE2b-256 checksum How to use checksums	`ffc9f78f61ea1745b16968b4ee343c7f00a8977d9577cf71e02ecdb8f518cd3f`
Upload date	Nov 14, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.10.12`