Looker GenAI Extension

This repository provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). This extension allows users to leverage the power of LLMs to enhance data exploration and analysis within Looker.

Note: For the Looker Explore Assistant, visit https://github.com/looker-open-source/looker-explore-assistant/.

Features

The Looker GenAI Extension offers two key functionalities:

1. Generative Explore:

Ask natural language questions about your data in Looker Explores.
The LLM will automatically generate explores with the appropriate fields, filters, sorts, pivots, and limits.
Visualize results using a variety of charts and dashboards.

2. Generative Insights on Dashboards:

Analyze data from a Looker dashboard by asking natural language questions.
The LLM considers all data from the dashboard tiles for context-aware insights.

Architecture

The solution leverages the following components:

2.1 Generative Explore Workflow

The extension supports multiple LLM integration options:

BQML Remote Models: (Default) Uses native BigQuery ML integration for simple and quick deployment.
BQML Remote UDF with Vertex AI: (Recommended) Uses Google Cloud Functions with Vertex AI for greater flexibility and production-ready scenarios.
Custom Fine Tune Model: (Optional) Enables training a customized fine-tuned model for tailored responses.

Workflow for BQML Remote Models:

Workflow for BQML Remote UDF with Vertex AI:

Workflow for Custom Fine Tune Model:

2.2 Generative Insights on Dashboards Workflow

Deployment

3. Deploy Infrastructure with Terraform

This section guides you through deploying the necessary infrastructure using Terraform.

Clone the Repository:

cloudshell_open --repo_url "https://github.com/looker-open-source/extension-gen-ai" --page "shell" --open_workspace "deployment/terraform" --force_new_clone

Alternatively, open directly in Cloud Shell:

Set Project ID:
```
gcloud config set project PROJECT-ID
```
IAM Roles:
- Ensure the following IAM roles are assigned at the project level:
  - roles/browser
  - roles/cloudfunctions.developer
  - roles/iam.serviceAccountUser
  - roles/storage.admin
  - roles/bigquery.user
  - roles/bigquery.connectionAdmin
  - roles/resourcemanager.projectIamAdmin
  - roles/iam.serviceAccountCreator
For more detailed IAM information, see deployment/terraform/iam-issues.md.
Create Terraform State Buckets:
```
sh scripts/create-state-bucket.sh
```
Initialize Terraform Modules:
```
terraform init
```

Deploy Resources:

terraform apply -var="project_id=YOUR_PROJECT_ID"

4. Deploy the Looker Extension

Create Looker Project:
- Log into Looker and create a new project named looker-genai.
- Use "Blank Project" as the "Starting Point."
Copy Extension Files:
- Drag and drop the following files from the looker-project-structure folder into your Looker project:
  - manifest.lkml
  - looker-genai.model
  - bundle.js
Configure BigQuery Connection:
- Modify looker-genai.model to include a Looker connection to BigQuery.
- You can either create a new connection or use an existing one. If using an existing connection, ensure the service account has the necessary IAM permissions.
Connect to Git:
- Set up a Git repository and connect your Looker project to it.
Commit and Deploy:
- Commit your changes and deploy them to production.
Project Permissions:
- Grant the project permission to use the selected BigQuery connection.
Service Account Permissions:
- Verify that the service account associated with the connection has permission to access the llm dataset in your GCP project.
Test and Debug:
- Test the extension and use the browser's Web Developer Console to troubleshoot any errors.
- Review the explore_logs table in BigQuery to monitor queries.

5. Using and Configuring the Extension

5.1. Saving Example Prompts

Store example prompts in the llm.explore_prompts table:

INSERT INTO `llm.explore_prompts` 
VALUES("Top 3 brands in sales", "What are the top 3 brands that had the most sales price in the last 4 months?", "thelook.order_items", "explore")

Values:

name of example
prompt
model.explore (LookML explore name)
type (explore or dashboard)

5.2. Configuring User Settings

Settings are managed in the llm.settings table. You can adjust these settings in the "Developer Settings" tab of the extension.

Console Log Level: Controls the verbosity of logs.
Use Native BQML or Remote UDF: Choose between native BigQuery ML functions or custom remote UDFs.
Custom Prompt: Optionally set a custom prompt for your user ID.

Modify Settings with SQL:

Change settings for all users:

UPDATE `llm.settings` SET config = (SELECT config from `llm.settings` WHERE userId = "YOUR_USER_ID") WHERE True

Change settings for the default user:

UPDATE `llm.settings` SET config = (SELECT config from `llm.settings` WHERE userId = "YOUR_USER_ID") WHERE userId IS NULL

6. Developing the Looker Extension Environment

6.1. Install Dependencies

yarn install

6.2. Start the Development Server

yarn develop

The development server will run at https://localhost:8080/bundle.js.

6.3. Build for Production

yarn build

This will generate the dist/bundle.js file. Replace the URL in your LookML manifest with the production bundle.js.

Advanced: Custom Fine-Tuned Model

This section describes how to train and deploy a custom fine-tuned model using the provided Terraform scripts.

Infrastructure Setup:
- The provided Terraform code sets up Vertex AI, Cloud Functions, and BigQuery resources.
- It also includes the necessary IAM permissions.

Fine-Tuning:

Execute the Cloud Workflow:

gcloud workflows execute fine_tuning_model

Update BigQuery Endpoint:
- Modify the BigQuery endpoint to point to your custom fine-tuned model.

Note: The code for fine-tuned model integration is currently in progress and needs to be refactored for optimal use.

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