dataset-foundry 0.5.0

Build, refine and validate AI safety datasets using LLM pipelines

Dataset Foundry

A toolkit for building validated datasets.

Dataset Foundry uses the concept of data pipelines to load, generate or validate datasets. A pipeline is a sequence of actions executed either against the dataset itself or the individual items in the dataset.

For details on which actions are supported, see the actions documentation.

Installation

1. Install the package:

   pip install dataset-foundry
   

2. Create a .env file in the project root:

   # Provider API keys
   OPENAI_API_KEY=
   ANTHROPIC_API_KEY=
   
   # Command-line defaults
   DF_MODEL=anthropic/claude-sonnet-4-20250514
   
   # Keep full display open after finishing so you can browse the results
   # DF_NO_EXIT=true
   

Default Settings

• Default Model: gpt-4o-mini
• Default Temperature: 0.7
• Default Number of Samples: 10
• Dataset Directory: ./datasets
• Logs Directory: ./logs

Project Structure

dataset-foundry/
├── src/
│   └── dataset_foundry/
│       ├── actions/       # Actions for processing datasets and items within those datasets
│       ├── cli/           # Command-line interface tools
│       ├── core/          # Core functionality
│       └── utils/         # Utility functions
├── datasets/              # Generated datasets
├── examples/              # Example pipelines
│   └── refactorable_code/ # Example pipelines to build a dataset of code requiring refactoring
└── logs/                  # Operation logs

Running Pipelines

Pipelines can be run from the command line using the dataset-foundry command.

dataset-foundry <pipeline_module> <dataset_name>

For example, to run the generate_spec pipeline to create specs for a dataset saved to datasets/dataset1, you would use:

dataset-foundry examples/refactorable_code/generate_spec/pipeline.py dataset1

Use dataset-foundry --help to see available arguments.

Running Examples

To generate a set of specs for a dataset named o3v5, you would use:

dataset-foundry examples/refactorable_code/generate_spec/pipeline.py samples --num-samples=2

To generate a set of functions and unit testsfrom the specs, you would use:

dataset-foundry examples/refactorable_code/generate_all_from_spec/pipeline.py samples

To run the unit tests for the generated functions, you would use:

dataset-foundry examples/refactorable_code/regenerate_unit_tests/pipeline.py samples

If some of the unit tests fail, you can regenerate them by running:

dataset-foundry examples/refactorable_code/regenerate_unit_tests/pipeline.py samples

Variable Substitutions

Variable substitutions allows you to use variables in your prompts and in certain parameters passed into pipeline actions.

Prompt templates and certain parameters are parsed as f-strings, with the following enhancements:

• Dotted references are supported and resolve both dictionary keys or object attributes. For instance, {spec.name} will return the value of spec['name'] if spec is a dictionary, or the value of spec.name if spec is an object.
• Formatters can be specified after a colon. For example, {spec:yaml} will return the spec object formatted as a YAML string. Supported formatters include: yaml, json, upper, lower.

For instance, if an item is being processed with an id of 123 and a spec dictionary with a name key of my_function, the following will save the code property of the item as a file named item_123_my_function.py:

   ...
   save_item(contents=Key("code"), filename="item_{id}_{spec.name}.py"),
   ...

License

This project is licensed under the MIT License - see the LICENSE file for details.