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Explore Assistant Backend

Overview

This Terraform configuration establishes a backend for the Looker Explore Assistant on Google Cloud Platform (GCP), facilitating interaction with the Gemini Pro model of Vertex AI. The setup supports two options: a Cloud Function backend and a BigQuery backend, each acting as a proxy/relay for running content through the model.

The Explore Assistant also uses a set of examples to improve the quality of its answers. We store those examples in BigQuery. Please see the comparisons below when deciding which deployment approach to use.

What backend should I use?

Here we list the reasons and tradeoffs of each deployment approach in an effort to scope the right backend deployment approach based on individual preferences and existing setups.

Regardless of Backend:

  • Any Looker database connection can be used for fetching the actual data returned from the natural language query url
  • They implement the same API, as in no Looker Credentials are stored in the backends and the arguments are the same (ie. model parameters and a prompt)
  • By default both approaches fetch examples from a BigQuery table out of simplicity. For Cloud Functions you can modify this React Hook and change the connection_name on line 18 to point to the non BQ database connection in Looker that houses your example prompts/training data.

For Cloud Function/Run:

  • Generally speaking, this approach is recommended for folks who want more development control on the backend
  • Your programming language of choice can be used
  • Workflows for custom codeflow like using custom models, combining models to improve results, fetching from external datastores, etc. are supported
  • An HTTPS endpoint will be made available that can be leveraged external to Looker (ie. external applications with a custom web app)
  • The endpoint needs to be public for Looker to reach it (To Note: the repo implements a signature on the request for security. Otherwise putting the endpoint behind a Load Balancer or API Proxy is recommended. Keep in mind that Looker Extensions however, when not embedded are only accessible by authenticated Looker users.)

For BigQuery:

  • The BigQuery backend is a prototype backend. The cloud function backend will provide better performance
  • Generally speaking, this approach will be easier for users already familiar with Looker
  • Invoking the LLM with custom prompts is all done through SQL
  • BigQuery's Service Account or User Oauth Authentication can be used
  • BigQuery however will serve as a pass through to the Vertex API
  • Looker & BigQuery query limits will apply to this approach

Prerequisites

  • Terraform installed on your machine.
  • Access to a GCP account with permission to create and manage resources.
  • A GCP project where the resources will be deployed.

Configuration and Deployment

We are using terraform to setup the backend. By default, we will store the state locally. You can also host the terraform state inside the project itself by using a remote backend. The configuration is passed on the command line since we want to use the project-id in the bucket name. Since the project-ids are globally unique, so will the storage bucket name.

To use the remote backend you can run ./init.sh remote instead of terraform init. This will create the bucket in the project, and setup the terraform project to use it as a backend.

Cloud Function Backend

  • The Cloud Function backend is a production backend and will provide better performance
  • First create a file that will contain the LOOKER_AUTH_TOKEN and place it at the root. This will be used by the cloud function locally, as well as the extension framework app. The value of this token will uploaded to the GCP project as secret to be used by the Cloud Function.

If in the /explore-assistant-backend cd back to root (ie. cd ..) and run the following command:

openssl rand -base64 32 > .vertex_cf_auth_token

From the /explore-assistant-backend directory run the following.

To deploy the Cloud Function backend (Production):

cd terraform 
export TF_VAR_project_id=(PASTE BQ PROJECT ID HERE)
export TF_VAR_use_bigquery_backend=0
export TF_VAR_use_cloud_function_backend=1
export TF_VAR_looker_auth_token=$(cat ../../.vertex_cf_auth_token)
terraform init
terraform plan
terraform apply

BigQuery Backend

To deploy the BigQuery backend (Prototype):

cd terraform 
export TF_VAR_project_id=(PASTE BQ PROJECT ID HERE)
export TF_VAR_use_bigquery_backend=1
export TF_VAR_use_cloud_function_backend=0
terraform init
terraform plan
terraform apply

You will have to wait 1-2 minutes for the APIs to turn on. You will also have to wait a couple of minutes for the service account for the BigQuery connection to appear.

If you use the defaults, you can test whether everything is working by running:

    SELECT ml_generate_text_llm_result AS generated_content
    FROM
    ML.GENERATE_TEXT(
        MODEL `explore_assistant.explore_assistant_llm`,
        (
          SELECT "hi" as prompt
        ),
        STRUCT(
        0.05 AS temperature,
        1024 AS max_output_tokens,
        0.98 AS top_p,
        TRUE AS flatten_json_output,
        1 AS top_k)
      )

Also, as part of the BigQuery backend setup, we create the Service Account that can be used to connect Looker to the BigQuery dataset to fetch the examples and use the model. You can follow the instructions for creating the connection in Looker here (https://cloud.google.com/looker/docs/db-config-google-bigquery#authentication_with_bigquery_service_accounts). You should be able to pickup the instructions on step 5.

Deployment Notes

  • Changes to the code in explore-assistant-cloud-function will result in a zip file with a new hash. This hash is added to the environment variables for the cloud function, and a new hash will trigger the redeployment of the cloud function.

Resources Created

  • Google Cloud Functions or Cloud Run services, based on the selected backend.
  • Google BigQuery dataset and table to store the examples
  • Google BigQuery connection and gemini pro model, if using the BigQuery backend.
  • Necessary IAM roles and permissions for the Looker Explore Assistant to operate.
  • Storage buckets for deploying cloud functions or storing data.
  • Artifact Registry for storing Docker images, if required.

Cleaning Up

To remove all resources created by this Terraform configuration, run:

terraform destroy

Note: This will delete all resources and data. Ensure you have backups if needed.

Support

For issues, questions, or contributions, please open an issue in the GitHub repository where this configuration is hosted.