> ## Documentation Index
> Fetch the complete documentation index at: https://explore-assistant.datadriven.works/llms.txt
> Use this file to discover all available pages before exploring further.

# Setup

> This document provides instructions for setting up the backend for the Looker Explore Assistant on Google Cloud Platform (GCP) using Terraform.

## 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](../explore-assistant-extension/src/hooks/useExampleData.ts) 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**:

* 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.

## Support

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