1. Managing API Keys for Large Language Models with Databricks Secret Scopes


    When you are using Large Language Models (like OpenAI's GPT-3) within a cloud environment, it's important to manage API keys and secrets securely. Databricks offers an integrated feature called Secret Scopes that can help effectively manage and use these API keys within your workflows.

    Secret Scopes in Databricks allow you to store secrets such as API keys, database connection strings, and other sensitive information securely. Once stored in a Secret Scope, you can reference these secrets within your Databricks notebooks and jobs without exposing the sensitive details in plain text. This helps in maintaining security and compliance within your applications.

    To get started with managing API keys for large language models using Databricks Secret Scopes via Pulumi, you would first define a Secret Scope resource using the pulumi_databricks package. In this example, we will create a new Secret Scope and then add a secret to that scope. This secret could be the API key for a Large Language Model.

    import pulumi import pulumi_databricks as databricks # Create a new Secret Scope. The Secret Scope is a secure container for your secrets. secret_scope = databricks.SecretScope("language-model-api-keys-scope", name="language-model-api-keys", # The backend type is set to DATABRICKS since we are using Databricks Secret Scopes to store our secrets backend_type="DATABRICKS", # The initial_manage_principal gives the permission to manage this secret scope. # Giving "users" permission means all users in the Databricks workspace can manage the secrets in this scope. initial_manage_principal="users", ) # Use the secret in your code. Please replace "<your-api-key>" with your actual API key. api_key_secret = databricks.Secret("language-model-api-key-secret", scope=secret_scope.name, # Reference the Secret Scope we just created. key="gpt3-api-key", string_value="<your-api-key>", # The secret value, in this case, the API key. ) # We can export the secret scope name to easily reference it in the future. pulumi.export("secret_scope_name", secret_scope.name)

    In this program:

    • We create a Secret Scope named language-model-api-keys-scope using databricks.SecretScope.
    • We then create a secret within that scope to store the API key for our Large Language Model.
    • Remember to replace <your-api-key> with the actual API key that you have obtained from the Large Language Model provider.

    Remember not to store actual secret values in your source code. I have used "<your-api-key>" as a placeholder. A best practice is to supply this type of sensitive information at runtime through a secure channel or use Pulumi's own configuration system, which encrypts secrets.

    As a final note, ensure that you have the appropriate rights and have authenticated to your Databricks instance, as administering secrets requires administrative privileges.

    For more information on Pulumi and Databricks: