In TensorZero, datasets are collections of data that can be used for workflows like evaluations and optimization recipes. You can create and manage datasets using the TensorZero UI or programmatically using the TensorZero Gateway. A dataset is a named collection of datapoints. Each datapoint belongs to a function, with fields that depend on the function’s type. Broadly speaking, each datapoint largely mirrors the structure of an inference, with an input, an optional output, and other associated metadata (e.g. tags).
You can find a complete runnable example of how to use the datasets and datapoints API in our GitHub repository.

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Endpoints & Methods

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List datapoints in a dataset

This endpoint returns a list of datapoints in the dataset. Each datapoint is an object that includes all the relevant fields (e.g. input, output, tags).
  • Gateway Endpoint: GET /datasets/{dataset_name}/datapoints
  • Client Method: list_datapoints
  • Parameters:
    • dataset_name (string)
    • function (string, optional)
    • limit (int, optional, defaults to 100)
    • offset (int, optional, defaults to 0)
If function is set, this method only returns datapoints in the dataset for the specified function.

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Get a datapoint

This endpoint returns the datapoint with the given ID, including all the relevant fields (e.g. input, output, tags).
  • Gateway Endpoint: GET /datasets/{dataset_name}/datapoints/{datapoint_id}
  • Client Method: get_datapoint
  • Parameters:
    • dataset_name (string)
    • datapoint_id (string)

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Add datapoints to a dataset (or create a dataset)

This endpoint adds a list of datapoints to a dataset. If the dataset does not exist, it will be created with the given name.
  • Gateway Endpoint: POST /datasets/{dataset_name}/datapoints/bulk
  • Client Method: bulk_insert_datapoints
  • Parameters:
    • dataset_name (string)
    • datapoints (list of objects, see below)
For chat functions, each datapoint object must have the following fields:
  • function_name (string)
  • input (object, identical to an inference’s input)
  • output (a list of objects, optional, each object must be a content block like in an inference’s output)
  • allowed_tools (list of strings, optional, identical to an inference’s allowed_tools)
  • tool_choice (string, optional, identical to an inference’s tool_choice)
  • parallel_tool_calls (boolean, optional, defaults to false)
  • tags (map of string to string, optional)
For json functions, each datapoint object must have the following fields:
  • function_name (string)
  • input (object, identical to an inference’s input)
  • output (object, optional, an object that matches the output_schema of the function)
  • output_schema (object, optional, a dynamic JSON schema that overrides the output schema of the function)
  • tags (map of string to string, optional)

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Delete a datapoint

This endpoint performs a soft deletion: the datapoint is marked as stale and will be disregarded by the system in the future (e.g. when listing datapoints or running evaluations), but the data remains in the database.
  • Gateway Endpoint: DELETE /datasets/{dataset_name}/datapoints/{datapoint_id}
  • Client Method: delete_datapoint
  • Parameters:
    • dataset_name (string)
    • datapoint_id (string)