Write MQL with Gemini assistance

Write MQL with Gemini assistance

Preview — Gemini in Firestore

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

This document describes how you can use Gemini Code Assist to get AI-powered assistance in Firestore to generate MQL queries using natural language prompts.

Learn how and when Gemini for Google Cloud uses your data.

Note: Coding assistance is part of Gemini Code Assist and is available at no charge until it's included in Gemini Code Assist Standard edition. This change will be communicated at a later date. At that time, you will need to acquire a Gemini Code Assist Standard edition license to continue to use coding assistance in Firestore with MongoDB compatibility Studio.

Before you begin

1. Optional: Set up Gemini Code Assist.

2. To complete the tasks in this document, ensure that you have the necessary Identity and Access Management (IAM) permissions.

Required roles

To get the permissions that you need to complete the tasks in this document, ask your administrator to grant you the Gemini for Google Cloud User (roles/cloudaicompanion.user) IAM role on the project. For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Generate MQL queries using natural language prompts

As an early-stage technology, Gemini for Google Cloud products can generate output that seems plausible but is factually incorrect. We recommend that you validate all output from Gemini for Google Cloud products before you use it. For more information, see Gemini for Google Cloud and responsible AI.

You can give Gemini natural language comments (or prompts) to generate queries that are based on your schema. For example, you can prompt Gemini to generate MQL in response to the following prompts:

• "How many popular books with publication year 1960?"
• "Create a sample collection of popular books."

Note: When you enter a prompt for Gemini in Firestore, the database runs a $sample operation to retrieve 10 random documents to provide information about the database schema. You are charged for the read units for this operation.

To generate MQL in Firestore with Gemini assistance, follow these steps:

1. In the Google Cloud console, go to the Firestore Databases page.

   Go to Databases

2. Select an Firestore with MongoDB compatibility database from the list. The Firestore Studio opens.

3. In a new or empty query editor, click the Generate MQL button. Otherwise, click Help me code.

4. Enter a prompt to use to generate a query. To improve accuracy, select a collection for context in the drop-down.

5. Review the generated MQL and take any of the following actions:

   • To accept MQL generated by Gemini, click Insert. You can continue to edit the MQL in the editor. Click Run to run you query.
   • To edit your prompt, click Edit.

What's next

• Read Gemini for Google Cloud overview.
• Learn how Gemini uses your data.