Databricks

Configure Databricks Model Serving to access foundation models and custom models through Braintrust.

Authentication

Choose between two authentication methods:

  • Personal Access Token (PAT): Use a Databricks personal access token for authentication
  • Service Principal OAuth: Use OAuth with a service principal for authentication

Configuration

FieldDescription
API base URL
URL String
Required. Your Databricks workspace URL in the format https://{workspace-name}.cloud.databricks.com. Documentation
Authentication type
pat | service_principal_oauth
Optional. Choose between personal access token or service principal OAuth. Default is pat. Documentation
Secret
String
Required if using pat auth type. Your Databricks personal access token. Documentation
Client ID
String
Required if using service_principal_oauth auth type. Client ID for service principal authentication. Documentation
Client Secret
String
Required if using service_principal_oauth auth type. Client secret for service principal authentication. Documentation

Models

Databricks provides access to several foundation models through Model Serving.

Foundation models

  • Meta Llama 3.1 70B Instruct
  • Meta Llama 3.1 8B Instruct
  • Mistral 7B Instruct
  • Mixtral 8x7B Instruct
  • MPT-7B Instruct

Custom models

Deploy your own fine-tuned models through Databricks Model Serving.

Setup requirements

  1. Databricks Workspace: Ensure you have access to a Databricks workspace
  2. Model Serving: Enable Model Serving in your workspace
  3. Authentication: Set up either PAT or service principal authentication
  4. Model Endpoints: Deploy the models you want to use as serving endpoints

Endpoint configuration

Configure the following for model endpoints in Databricks.

  1. Serving Endpoint Name: Use this as the model name in Braintrust
  2. Endpoint URL: Automatically constructed from your workspace URL
  3. Authentication: Uses the configured authentication method

Additional resources

On this page

Databricks - Docs - Braintrust