weaviate-demo-datasets 0.1.0

Unofficial demo datasets for Weaviate

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 weaviate-demo-datasets

All datasets are based on the Dataset superclass, which includes a number of built-in methods to make it easier to work with it.

Each dataset includes a default vectorizer configuration for convenience, which can be:

• viewed via the .get_class_definitions method and
• changed via the .set_vectorizer method. 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
dataset = weaviate_datasets.JeopardyQuestions10k()  # Instantiate dataset
dataset.upload_dataset(client)  # Add class to schema & upload objects (uses batch uploads by default)

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

import weaviate
import os
import json

wv_url = "https://some-endpoint.weaviate.network"
api_key = os.environ.get("OPENAI_API_KEY")

# If authentication required (e.g. using WCS)
auth = weaviate.AuthClientPassword(
    username=os.environ.get("WCS_USER"),
    password=os.environ.get("WCS_PASS"),
)

client = weaviate.Client(
    url=wv_url,
    auth_client_secret=auth,  # If authentication required
    additional_headers={"X-OpenAI-Api-Key": api_key},  # If using OpenAI inference
)

Built-in methods

• .upload_dataset(client) - add defined classes to schema, adds objects

• .get_class_definitions(): See the schema definition to be added

• .get_class_names(): See class names in the dataset

• .get_sample(): See a sample data object

• .classes_in_schema(client): Check whether each class is already in the Weaviate schema

• .delete_existing_dataset_classes(client): If dataset classes are already in the Weaviate instance, delete them from the Weaviate instance.

• .set_vectorizer(vectorizer_name, module_config): Set the vectorizer and corresponding module configuration for the dataset. Datasets come pre-configured with a vectorizer & module configuration.

Available classes

Not including vectors

• WikiArticles (A handful of Wikipedia summaries)
• WineReviews (50 wine reviews)

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)
• 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