Submit Training Job

Create a tuning job for the specified Agent to specialize it to your specific domain or use case.

This API initiates an asynchronous tuning task. You can provide the required data through one of two ways:

• Provide a training_file and an optional test_file. If no test_file is provided, a portion of the training_file will be held out as the test set. For easy reusability, the training_file is automatically saved as a Tuning Dataset, and the test_file as an Evaluation Dataset. You can manage them via the /datasets/tune and /datasets/evaluation endpoints.

• Provide a Tuning Dataset and an optional Evaluation Dataset. You can create a Tuning Dataset and Evaluation Dataset using the /datasets/tune and /datasets/evaluation endpoints respectively.

The API returns a tune job id which can be used to check on the status of your tuning task through the GET /tune/jobs/{job_id}/metadata endpoint.

After the tuning job is complete, the metadata associated with the tune job will include evaluation results and a model ID. You can then deploy the tuned model to the agent by editing its config with the tuned model ID and the “Edit Agent” API (i.e. the PUT /agents/{agent_id} API). To deactivate the tuned model, you will need to edit the Agent’s config again and set the llm_model_id field to “default”. For an end-to-end walkthrough, see the Tune & Evaluation Guide.