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Documentation Index

Fetch the complete documentation index at: https://docs.getbasalt.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

This page explains how to work with Datasets using the Basalt Python SDK. Datasets let you organize, retrieve, and extend structured test data (inputs, ideal outputs, metadata) to evaluate prompts, models, and full workflows.

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Initialization

Create a single Basalt client and reuse it across your application or script. 
from basalt import Basalt

basalt = Basalt(api_key="your-api-key")
When you are done (for example in a CLI script or worker), call: 
basalt.shutdown()
to clean up resources.

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Listing datasets

Retrieve all datasets accessible to your API key.

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Basic listing (sync)

from basalt import Basalt

basalt = Basalt(api_key="your-api-key")

# List all datasets
response = basalt.datasets.list_sync()

print(f"Total datasets: {response.total}")

for dataset in response.datasets:
    print(f"\nSlug: {dataset.slug}")
    print(f"  Name: {dataset.name}")
    print(f"  Description: {dataset.description}")
    print(f"  Rows: {dataset.num_rows}")
    print(f"  Columns: {len(dataset.columns)}")

basalt.shutdown()

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Async listing

import asyncio
from basalt import Basalt

async def list_datasets_async():
    basalt = Basalt(api_key="your-api-key")

    response = await basalt.datasets.list()

    for dataset in response.datasets:
        print(f"{dataset.slug}: {dataset.num_rows} rows")

    basalt.shutdown()

asyncio.run(list_datasets_async())
Use the sync version in scripts and simple backends; prefer the async method list in async frameworks like FastAPI.

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Getting datasets

Retrieve a specific dataset with all its rows and columns. 
from basalt import Basalt

basalt = Basalt(api_key="your-api-key")

# Get dataset by slug
dataset = basalt.datasets.get_sync(slug="customer-support-qa")

print(f"Dataset: {dataset.name}")
print(f"Description: {dataset.description}")
print(f"Total rows: {dataset.num_rows}")
print(f"Columns: {[col.name for col in dataset.columns]}")

# Access rows
for row in dataset.rows:
    print(f"\nRow: {row.name}")
    print(f"  Values: {row.values}")
    print(f"  Ideal output: {row.ideal_output}")
    print(f"  Metadata: {row.metadata}")

basalt.shutdown()

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Inspecting columns

from basalt import Basalt

basalt = Basalt(api_key="your-api-key")

dataset = basalt.datasets.get_sync(slug="my-dataset")

for column in dataset.columns:
    print(f"\nColumn: {column.name}")
    print(f"  Type: {column.type}")
    print(f"  Description: {column.description}")

basalt.shutdown()

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Dataset objects

The main objects you work with are:
  • Dataset
  • DatasetColumn
  • DatasetRow

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Dataset

  • slug: Unique identifier for the dataset
  • name: Human-readable name
  • description: Description of what the dataset is for
  • num_rows: Number of rows
  • columns: List of DatasetColumn objects
  • rows: List of DatasetRow objects

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DatasetColumn

  • name: Column name
  • type: Data type (e.g. "string", "number")
  • description: Column description

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DatasetRow

  • name: Row identifier
  • values: Dict of column_name -> value. Values can be strings, numbers, or FileAttachment objects.
  • ideal_output: Optional expected output for evaluation
  • metadata: Optional dict with additional context

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FileAttachment

  • source: Path to the file to upload
  • content_type: MIME type of the file
See the API Reference for method signatures and all available fields.