Jaa

Deploy an agent for generative AI application

  • Artikkeli

Important

This feature is in Public Preview.

This article shows how to deploy your AI agent using the deploy() function from the Agent Framework Python API.

Requirements

  • MLflow 2.13.1 or above to deploy agents using the the deploy() API from databricks.agents.

  • Register an AI agent to Unity Catalog. See Register the agent to Unity Catalog.

  • Deploying agents from outside a Databricks notebook requires databricks-agents SDK version 0.12.0 or above.

  • Install the the databricks-agents SDK.

    %pip install databricks-agents
dbutils.library.restartPython()

Deploy an agent using deploy()

The deploy() function does the following:

  • Creates CPU model serving endpoints for your agent that can be integrated into your user-facing application.
  • Enables the Review App for your agent. The Review App lets stakeholders chat with the agent and give feedback using the Review App UI.
  • Logs every request to the Review App or REST API to an inference table. The data logged includes query requests, responses, and intermediate trace data from MLflow Tracing.
  • Creates a feedback model with the same catalog and schema as the agent you are trying to deploy. This feedback model is the mechanism that makes it possible to accept feedback from the Review App and log it to an inference table. This model is served in the same CPU model serving endpoint as your deployed agent. Because this serving endpoint has inference tables enabled, it is possible to log feedback from the Review App to an inference table.

Note

Deployments can take up to 15 minutes to complete. Raw JSON payloads take 10 - 30 minutes to arrive, and the formatted logs are processed from the raw payloads about every hour.


from databricks.agents import deploy
from mlflow.utils import databricks_utils as du

deployment = deploy(model_fqn, uc_model_info.version)

# query_endpoint is the URL that can be used to make queries to the app
deployment.query_endpoint

# Copy deployment.rag_app_url to browser and start interacting with your RAG application.
deployment.rag_app_url

Agent-enhanced inference tables

The deploy() creates three inference tables for each deployment to log requests and responses to and from the agent serving endpoint. Users can expect the data to be in the payload table within an hour of interacting with their deployment.

Payload request logs and assessment logs might take longer to populate, but are ultimately derived from the raw payload table. You can extract request and assessment logs from the payload table yourself. Deletions and updates to the payload table are not reflected in the payload request logs or the payload assessment logs.

Note

If you have Azure Storage Firewall enabled, reach out to your Databricks account team to enable inference tables for your endpoints.

Table Example Unity Catalog table name What is in each table
Payload {catalog_name}.{schema_name}.{model_name}_payload Raw JSON request and response payloads
Payload request logs {catalog_name}.{schema_name}.{model_name}_payload_request_logs Formatted request and responses, MLflow traces
Payload assessment logs {catalog_name}.{schema_name}.{model_name}_payload_assessment_logs Formatted feedback, as provided in the Review App, for each request

The following shows the schema for the request logs table.

Column name Type Description
client_request_id String Client request ID, usually null.
databricks_request_id String Databricks request ID.
date Date Date of request.
timestamp_ms Long Timestamp in milliseconds.
timestamp Timestamp Timestamp of the request.
status_code Integer Status code of endpoint.
execution_time_ms Long Total execution milliseconds.
conversation_id String Conversation id extracted from request logs.
request String The last user query from the user’s conversation. This is extracted from the RAG request.
response String The last response to the user. This is extracted from the RAG request.
request_raw String String representation of request.
response_raw String String representation of response.
trace String String representation of trace extracted from the databricks_options of response Struct.
sampling_fraction Double Sampling fraction.
request_metadata Map[String, String] A map of metadata related to the model serving endpoint associated with the request. This map contains the endpoint name, model name, and model version used for your endpoint.
schema_version String Integer for the schema version.

The following is the schema for the assessment logs table.

Column name Type Description
request_id String Databricks request ID.
step_id String Derived from retrieval assessment.
source Struct A struct field containing the information on who created the assessment.
timestamp Timestamp Timestamp of request.
text_assessment Struct A struct field containing the data for any feedback on the agent’s responses from the review app.
retrieval_assessment Struct A struct field containing the data for any feedback on the documents retrieved for a response.

Authentication for dependent resources

AI agents often need to authenticate to other resources to complete tasks. For example, an agent may need to access a Vector Search index to query unstructured data.

Your agent can use one of the following methods to authenticate to dependent resources when you serve it behind a Model Serving endpoint:

  1. Automatic authentication passthrough: Declare Databricks resource dependencies for your agent during logging. Databricks can automatically provision, rotate, and manage short-lived credentials when your agent is deployed to securely access resources. Databricks recommends using automatic authentication passthrough where possible.
  2. Manual authentication: Manually specify long-lived credentials during agent deployment. Use manual authentication for Databricks resources that do not support automatic authentication passthrough, or for external API access.

Automatic authentication passthrough

Model Serving supports automatic authentication passthrough for the most common types of Databricks resources used by agents.

To enable automatic authentication passthrough, you must specify dependencies during agent logging.

Then, when you serve the agent behind an endpoint, Databricks performs the following steps:

  1. Permission verification: Databricks verifies that the endpoint creator can access all dependencies specified during agent logging.

  2. Service principal creation and grants: A service principal is created for the agent model version and is automatically granted read access to agent resources.

    Note

    The system-generated service principal does not appear in API or UI listings. If the agent model version is removed from the endpoint, the service principal is also deleted.

  3. Credential provisioning and rotation: Short-lived credentials (an M2M OAuth token) for the service principal are injected into the endpoint, allowing agent code to access Databricks resources. Databricks also rotates the credentials, ensuring that your agent has continued, secure access to dependent resources.

This authentication behavior is similar to the “Run as owner” behavior for Databricks dashboards - downstream resources like Unity Catalog tables are accessed using the credentials of a service principal with least-privilege access to dependent resources.

The following table lists the Databricks resources that support automatic authentication passthrough and the permissions the endpoint creator must have when deploying the agent.

Note

Unity Catalog resources also require USE SCHEMA on the parent schema and USE CATALOG on the parent catalog.

Resource type Permission
SQL Warehouse Use Endpoint
Model Serving endpoint Can Query
Unity Catalog Function EXECUTE
Genie space Can Run
Vector Search index Can Use
Unity Catalog Table SELECT

Manual authentication

You can also manually provide credentials using secrets-based environment variables. Manual authentication can be helpful in the following scenarios:

  • The dependent resource does not support automatic authentication passthrough.
  • The agent is accessing an external resource or API.
  • The agent needs to use credentials other than those of the agent deployer.

For example, to use the Databricks SDK in your agent to access other dependent resources, you can set the environment variables described in Databricks client unified authentication.

Get deployed applications

The following shows how to get your deployed agents.

from databricks.agents import list_deployments, get_deployments

# Get the deployment for specific model_fqn and version
deployment = get_deployments(model_name=model_fqn, model_version=model_version.version)

deployments = list_deployments()
# Print all the current deployments
deployments

Provide feedback on a deployed agent (experimental)

When you deploy your agent with agents.deploy(), agent framework also creates and deploys a “feedback” model version within the same endpoint, which you can query to provide feedback on your agent application. Feedback entries appear as request rows within the inference table associated with your agent serving endpoint.

Note that this behavior is experimental: Databricks may provide a first-class API for providing feedback on a deployed agent in the future, and future functionality may require migrating to this API.

Limitations of this API include:

  • The feedback API lacks input validation - it always responds successfully, even if passed invalid input.
  • The feedback API requires passing in the Databricks-generated request_id of the agent endpoint request on which you wish to provide feedback. To get the databricks_request_id, include {"databricks_options": {"return_trace": True}} in your original request to the agent serving endpoint. The agent endpoint response will then include the databricks_request_id associated with the request so that you can pass that request ID back to the feedback API when providing feedback on the agent response.
  • Feedback is collected using inference tables. See inference table limitations.

The following example request provides feedback on the agent endpoint named “your-agent-endpoint-name”, and assumes that the DATABRICKS_TOKEN environment variable is set to a Databricks REST API token.

curl \
  -u token:$DATABRICKS_TOKEN \
  -X POST \
  -H "Content-Type: application/json" \
  -d '
      {
          "dataframe_records": [
              {
                  "source": {
                      "id": "user@company.com",
                      "type": "human"
                  },
                  "request_id": "573d4a61-4adb-41bd-96db-0ec8cebc3744",
                  "text_assessments": [
                      {
                          "ratings": {
                              "answer_correct": {
                                  "value": "positive"
                              },
                              "accurate": {
                                  "value": "positive"
                              }
                          },
                          "free_text_comment": "The answer used the provided context to talk about Delta Live Tables"
                      }
                  ],
                  "retrieval_assessments": [
                      {
                          "ratings": {
                              "groundedness": {
                                  "value": "positive"
                              }
                          }
                      }
                  ]
              }
          ]
      }' \
https://<workspace-host>.databricks.com/serving-endpoints/<your-agent-endpoint-name>/served-models/feedback/invocations

You can pass additional or different key-value pairs in the text_assessments.ratings and retrieval_assessments.ratings fields to provide different types of feedback. In the example, the feedback payload indicates that the agent’s response to the request with ID 573d4a61-4adb-41bd-96db-0ec8cebc3744 was correct, accurate, and grounded in context fetched by a retriever tool.

Additional resources