This feature is in Private Preview. To try it, reach out to your Databricks contact.
Looking for a different RAG Studio doc? Go to the RAG documentation index
RAG Studio includes multiple environments to help you manage the lifecycle of your application. Up until now, these tutorials have worked in the RAG Studio development and Reviewers
In this tutorial, you will deploy a version of your application to the
End Users environment.. Read the Environments for more details about how and why environments work.
If you did not already run this command in Initialize a RAG Application, run the following command to initialize these
Environments. This command takes about 10 minutes to run.
See Infrastructure and Unity Catalog assets created by RAG Studio for details of what is created in your Workspace and Unity Catalog schema.
Run the following command to deploy the version to the End Users
Environment. This command takes about 10 minutes to run.
./rag deploy-chain -v 1 -e end_users
In the console, you will see output similar to below. Open the URL in your web browser to open the
💬 Review UI. You can share this URL with your
🧠 Expert Users.
...truncated for clarity of docs... ======= Task deploy_chain_task: Your Review UI is now available. Open the Review UI here: https://<workspace-url>/ml/review/model/catalog.schema.rag_studio_databricks-docs-bot/version/1/environment/end_users
If you want
👤 End Usersto use the
💬 Review UI, add permissions to the deployed version.
Give the Databricks user you wish to grant access
the MLflow Experiment
the Model Serving endpoint
the Unity Catalog Model
🚧 Roadmap 🚧 Support for adding any corporate SSO to access the
💬 Review UIe.g., no requirements for a Databricks account.
Now, every time a
👤 End Userschats with your RAG Application, the
🗂️ Request Logand
👍 Assessment & Evaluation Results Logwill be populated.