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Datasets serve as inputs to an Evaluate run. Datasets have 3 standard columns: input, output, and metadata. The input column is what you can reference inside a prompt template to craft your prompt. The output column can be used to store reference outputs or ground truth outputs. The metadata column can be used to store any properties useful to group and filter the rows in the dataset.

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Using Datasets in the Galileo Console

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Create a dataset

From the Datasets page, click the “Create Dataset” button. You can upload a CSV, JSON, JSONL, or Feather file, or enter data directly into the table.

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Using a dataset in an evaluation run

When creating a new evaluation run, you can select a dataset to use as input.

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Using Datasets in code

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Prerequisites

For Python, install the promptquality library. For TypeScript, install the @rungalileo/galileo package.

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Create a dataset

You can create a new dataset by running: 
import os
import promptquality as pq

pq.login(os.environ["GALILEO_CONSOLE_URL"])

dataset = pq.create_dataset(
    {
        "input": [
            {"virtue": "benevolence", "voice": "Oprah Winfrey"},
            {"virtue": "trustworthiness", "voice": "Barack Obama"},
        ]
    }
)
These functions accept a few different formats for the dataset.
  1. A dictionary mapping column names to lists of values (as shown above).
  2. A list of dictionaries, where each dictionary represents a row in the dataset, e.g. 
    dataset = pq.create_dataset(
    [
        {"input": {"virtue": "benevolence", "voice": "Oprah Winfrey"}},
        {"input": {"virtue": "trustworthiness", "voice": "Barack Obama"}},
    ]
)
  3. A path to a file in either CSV, Feather, or JSONL format, e.g. 
    dataset = pq.create_dataset("path/to/dataset.csv")

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Using a dataset in an evaluation run

To use the dataset in an evaluation run, provide the dataset ID to the run function (Python only). 
template = "Explain {virtue} to me in the voice of {voice}"

pq.run(
    project_name="test_dataset_project",
    template=template,
    dataset=dataset.id,
    settings=pq.Settings(
        model_alias="ChatGPT (16K context)", temperature=0.8, max_tokens=400
    ),
)
Note that the TypeScript client does not currently support creating runs. However, you can use the dataset for logging workflows.

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Getting the contents of a dataset

You can list the dataset’s contents like so: 
rows = pq.get_dataset_content(dataset.id)
for row in rows:
    print(row)
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