Data Sources

A data source produces the initial source samples that a dataset generator’s synthesizers then transform into the final dataset. Every declarative dataset generator declares exactly one data_source under its definition, and each synthesizer runs once per source sample it yields.

Data sources are configured with a type discriminator. The available types are documented below.

Note

Data sources are not the same as dataset sources. A data source seeds the generation pipeline of a single dataset generator; it is an implementation detail of that generator, not a standalone dataset.

Empty

Emits a fixed number of empty source samples. Use it when the synthesizers generate everything from scratch (e.g. an LLM driven purely by the config) and need no seed data.

Example: Generate from config only. An empty source drives an LLM synthesizer that produces questions about a config-provided topic.

Empty Data Source

# ...
definition:
  # ...
  data_source:
    type: "empty"

Configuration

Properties

---

type Literal “empty” required

The type of the data source.

---

num_samples integer, TemplateValue

The number of (empty) source samples returned.

Default: 1

Dataset Samples

Emits the samples of an existing dataset, one source sample per dataset row. Use it to synthesize new columns from, or variations of, an existing dataset.

Example: Seed from an existing dataset. Each row of the referenced dataset becomes a source sample.

Dataset Samples Data Source

# ...
config_spec:
  - key: "source_dataset_key"
    type: "dataset"
    display_name: "Source Dataset"
    default_value: "fairllm_sensitive_attributes-country"
    description: "Dataset containing country entries to generate user profiles from."
definition:
  # ...
  data_source:
    type: "dataset_samples"
    dataset_key: "<< config.source_dataset_key >>"

Configuration

Properties

---

type Literal “dataset_samples” required

The type of the data source.

---

dataset_key TemplateValue required

The key of the dataset whose samples should be used.

---

random_seed integer, TemplateValue

The random seed to use for dataset generation. If not provided, the processing order will be sequential and deterministic.

Default: None

Inline Samples

Emits samples defined inline in the config. Use it for small, fixed seed sets that live alongside the generator instead of in a separate dataset.

Example: Inline seed rows. A handful of topic/style pairs defined directly in the generator config.

Inline Samples Data Source

# ...
definition:
  # ...
  data_source:
    type: "inline_samples"
    samples:
      - topic: "Biology"
        style: "Multiple choice"
      - topic: "Chemistry"
        style: "Open-ended"
      - topic: "Physics"
        style: "Proofs"

Configuration

Properties

---

type Literal “inline_samples” required

The type of the data source.

---

samples array[object] required

The samples to use as the data source.

Dataset Sample Combinations

Emits the cartesian product of samples from multiple datasets, one source sample per combination. Use it to cover every pairing of independent dimensions (e.g. every city x language). Each source sample holds the columns of all the combined datasets, merged in the order they are listed under dataset_keys, so on a column collision the last dataset wins.

Example: All city x language pairs. Combines a cities dataset and a languages dataset into one source sample per pairing.

Dataset Sample Combinations Data Source

# ...
config_spec:
  - key: "cities_dataset_key"
    type: "dataset"
    display_name: "Cities Dataset"
    default_value: "cities_dataset"
    description: "Dataset containing the cities to generate QA samples from."
  - key: "languages_dataset_key"
    type: "dataset"
    display_name: "Languages Dataset"
    default_value: "languages_dataset"
    description: "Dataset containing the languages to generate QA samples in."
  - key: "synthesizer_model_key"
    type: "model"
    display_name: "Synthesizer Model"
    description: "Model used to generate QA samples."
definition:
  # ...
  data_source:
    type: "dataset_sample_combinations"
    dataset_keys:
      - "<< config.cities_dataset_key >>"
      - "<< config.languages_dataset_key >>"

The two combined datasets both define a language column - the local language of the city, and the language to write the question in:

cities_dataset

country,city,language
Germany,Berlin,German
France,Paris,French
Japan,Tokyo,Japanese

languages_dataset

language
English
French
German

The 3 cities and 3 languages yield 9 source samples, each holding the columns of both datasets. languages_dataset is listed last, so its language value wins over the one of cities_dataset. The first three combinations are:

Source Samples

| country | city   | language |
| :------ | :----- | :------- |
| Germany | Berlin | English  |
| France  | Paris  | French   |
| Japan   | Tokyo  | German   |

Note

The language of cities_dataset (German for Berlin, Japanese for Tokyo) never reaches the synthesizers - it is overwritten by languages_dataset. Rename one of the columns to keep both values.

Combinations are enumerated in a deterministic, fair order rather than as a nested loop, so a generator that stops after num_samples samples still covers every value of every dataset. Set random_seed to shuffle the datasets before combining.

Configuration

Properties

---

type Literal “dataset_sample_combinations” required

The type of the data source.

---

dataset_keys array[TemplateValue] required

The keys of the datasets whose samples should be used to generate combinations.

---

random_seed integer, TemplateValue

The random seed to use for dataset generation. If not provided, the processing order will be sequential and deterministic.

Default: None