> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Dataset

> Create a dataset for your product.

## Returns

Returns a [Dataset](/concepts/product/dataset) object for the given parameters.

## Examples

<Tabs>
  <Tab title="Accuracy Dataset">
    Create an accuracy dataset to evaluate the quality and correctness of outputs.

    ```python theme={"system"}
    dataset = galtea.datasets.create(
        name=dataset_name,
        type="ACCURACY",
        product_id=product_id,
        ground_truth_file_path="path/to/knowledge.md",
        language="english",
        max_test_cases=5,
    )
    ```
  </Tab>

  <Tab title="Security Dataset">
    Create security and safety datasets to identify potential vulnerabilities in your product.

    ```python theme={"system"}
    security_dataset = galtea.datasets.create(
        name=f"security-test-{run_identifier}",
        type="SECURITY",
        product_id=product_id,
        variants=["toxicity"],
        strategies=["original", "role_play", "base64"],
        max_test_cases=50,
    )
    ```
  </Tab>

  <Tab title="Behavior Dataset">
    Generate conversation scenarios tailored to specific user personas and use cases.

    ```python theme={"system"}
    behavior_dataset = galtea.datasets.create(
        name=f"behavior-test-{run_identifier}",
        type="BEHAVIOR",
        product_id=product_id,
        custom_user_focus="A medical professional specialized in dementia with more than 15 years in the field",
        language="english",
        max_test_cases=25,
        max_iterations=10,
        strategies=["written"],
    )
    ```
  </Tab>

  <Tab title="Accuracy Custom Dataset from CSV">
    Upload your own test cases using a CSV file for complete control over dataset content.

    ```python theme={"system"}
    csv_dataset = galtea.datasets.create(
        name=f"csv-accuracy-test-{run_identifier}",
        type="ACCURACY",
        product_id=product_id,
        dataset_file_path="path/to/accuracy_dataset.csv",
    )
    ```

    <Info>
      See a complete example of creating **custom datasets** in our [Create a Custom Dataset example](/sdk/tutorials/create-dataset).
    </Info>
  </Tab>
</Tabs>

## Parameters

<ResponseField name="name" type="string" required>
  The name of the dataset.
</ResponseField>

<ResponseField name="type" type="string | DatasetType" optional>
  The type of dataset. **Required** unless `specification_id` is provided (in which case the type is auto-derived from the specification).

  * `ACCURACY`: Datasets that evaluate the quality and correctness of outputs
  * `SECURITY`: Datasets that evaluate security, safety, and bias aspects
  * `BEHAVIOR`: Datasets that use conversation simulation to evaluate multi-turn dialogue interactions

  You can also use the `DatasetType` enum: `DatasetType.ACCURACY`, `DatasetType.SECURITY`, `DatasetType.BEHAVIOR`.
</ResponseField>

<ResponseField name="product_id" type="string" required>
  The ID of the product you want to evaluate.
</ResponseField>

<ResponseField name="specification_id" type="string" optional>
  The ID of a [Specification](/concepts/product/specification) to derive the dataset type from. When provided, `type` is auto-resolved and becomes optional.
</ResponseField>

<ResponseField name="ground_truth_file_path" type="string" optional>
  Path to a local file containing the knowledge base. This file is uploaded to Galtea and used to build the knowledge base for your dataset.

  <Note>
    Supported formats: `.pdf`, `.txt`, `.json`, `.html`, `.md`, `.yaml`, `.yml`,
    `.xml`, `.docx`, and `.zip`

    **Maximum file size: 100 MB**
    **Maximum ZIP contents: 1,000 files**
  </Note>
</ResponseField>

<ResponseField name="few_shot_examples" type="string" optional>
  Optional few-shot examples to provide more context to our system about how the test cases should be generated. This can help our system better understand the expected format and style wanted for the test cases.
  **Example**:

  ```
  Q: What is the capital of France?
  A: The capital of France is Paris.
  Q: What is the capital of Germany?
  A: The capital of Germany is Berlin.
  ```

  <Note>This field only applies if datasets are generated by Galtea and are of type `ACCURACY`.</Note>
</ResponseField>

<ResponseField name="custom_user_focus" type="string" optional>
  Narrow down the scope of generated scenarios by describing a specific type of user, context, or situation. This helps ensure test cases align with your most relevant goals and flows.
  Example: "A medical professional specialized in dementia with more than 15 years on the field."
  <Note>This field only applies if datasets are generated by Galtea and are of type `BEHAVIOR`.</Note>
</ResponseField>

<ResponseField name="language" type="string" optional>
  The language for generating synthetic test cases if `ground_truth_file_path` is provided. Defaults to the language detected in the ground truth file. Accepts a [BCP-47 language tag](https://en.wikipedia.org/wiki/IETF_language_tag): a bare ISO 639-1 code (`"es"`) or a code with a region subtag (`"es-MX"`, `"en-GB"`). The region subtag selects the regional variety in the simulated user's messages and in voice synthesis (the accent the simulated caller speaks with); generated test text keeps the base language. The value is stored as the full tag.
</ResponseField>

<ResponseField name="background_noise_profile" type="string" optional>
  Background noise mixed into the simulated caller's audio for voice evaluations run over a [phone connection](/concepts/product/phone-connection). Ignored for datasets that don't run over a phone connection.

  * Clip-backed: `office`, `street`, `car`
  * Synthetic: `white`, `pink`

  Every test case Galtea generates or ingests from a CSV file inherits this value at creation time. Test cases added afterward with [`test_cases.create()`](/sdk/api/test-case/create) do **not** inherit it automatically — pass the same values there to match.

  <Note>
    Must be set together with `background_noise_level` — passing only one of the two is rejected by the API. Leave both unset for a clean recording (the default).
  </Note>
</ResponseField>

<ResponseField name="background_noise_level" type="string" optional>
  How loud the background noise is relative to the caller's speech, as a fixed signal-to-noise ratio per level. One of `light`, `medium`, or `heavy`. Must be set together with `background_noise_profile`.
</ResponseField>

<ResponseField name="variants" type="list[string]" optional>
  A list of strings that specifies how to generate test cases. The meaning of this parameter depends on the dataset `type`:

  * **For Security datasets (`SECURITY`)**: Specifies which [threat category](/concepts/product/dataset/security-threats) to generate test cases for. **Exactly one threat must be provided** (e.g., `["toxicity"]`). Available threats: `toxicity`, `data_leakage`, `misuse`, `custom`, `financial_attacks`, `illegal_activities`.

  <Warning>Required when generating Security datasets (not needed when uploading a custom dataset file via `dataset_file_path`). Not applicable for Behavior datasets.</Warning>
</ResponseField>

<ResponseField name="strategies" type="list[string]" optional>
  A list of strings that specifies how to generate test cases related to its style.

  * **For Security datasets (`SECURITY`)**: Strategies are techniques for modifying or obfuscating prompts generated for each threat. The `original` strategy must always be included. See the full list of [security strategies](/concepts/product/dataset/security-strategies).
  * **For Behavior datasets (`BEHAVIOR`)**: Strategies define the conversation style. Currently `written` and `spoken` are supported, which influence the tone and formality of the generated dialogues. If not specified, the default strategy is `written`.
</ResponseField>

<ResponseField name="custom_variant_description" type="str" optional>
  Description for guiding synthetic data generation for security datasets. This parameter defines the behavior of the `custom` threat when used as the variant for Security datasets (`SECURITY`).

  <Warning>Required when `variants` is set to `["custom"]`. The API will reject the request if this field is missing for custom threats.</Warning>
</ResponseField>

<ResponseField name="max_test_cases" type="int" optional>
  Maximum number of test cases to generate from the knowledge base.

  **Default values:**

  * Accuracy/Security datasets: `50` test cases
  * Behavior datasets: `20` test cases

  **Maximum limits:**

  * Accuracy/Security datasets: `1000` test cases
  * Behavior datasets: `100` test cases

  <Warning>
    Generating large numbers of test cases can take significant time and consume
    more credits. Start with the default values and increase as needed.
  </Warning>

  <Note>
    If you need more than the maximum limit, consider:

    * Splitting your knowledge base into multiple datasets
    * Creating test cases manually with `test_cases.create()`
    * Uploading a CSV file with predefined test cases using `dataset_file_path`
  </Note>
</ResponseField>

<ResponseField name="max_iterations" type="int" optional>
  Maximum number of conversation turns per generated scenario for Behavior datasets (`BEHAVIOR`).

  Only applies when scenarios are generated by Galtea. The value is stamped on every generated test case and consumed by the [conversation simulator](/sdk/tutorials/simulating-conversations) at evaluation time (read as `max_turns`).

  Ignored for Accuracy (`ACCURACY`) and Security (`SECURITY`) datasets, and ignored when a custom dataset file is uploaded via `dataset_file_path` (no generation runs).

  **Constraints:** must be `>= 1`.

  **Default:** if omitted, the scenario-generator default is used.
</ResponseField>

<ResponseField name="dataset_file_path" type="string" optional>
  Path to a local CSV file containing predefined test cases. This file is uploaded to Galtea.

  **File Format by Dataset Type:**

  * **Accuracy/Security datasets**: Standard format with `input`, `expected_output`, `tag`, `source` columns
  * **Behavior datasets**: Conversation simulator format with `goal`, `user_persona`, `input`, `stopping_criterias`, `max_iterations`, `scenario` columns

  See the [Conversation Simulator Tutorial](/sdk/tutorials/simulating-conversations) for detailed Behavior dataset CSV format examples.

  <Note>Deprecated alias: `test_file_path`.</Note>
</ResponseField>

<ResponseField name="metadata" type="Any" optional>
  An open field where you can store any value for tracking or organizational purposes. Accepts plain text, numbers, JSON objects, arrays, or booleans.

  **Example**: `{"environment": "staging", "team": "ml-ops"}` or `"my tracking note"`
</ResponseField>