This repo provides an example of how to use Gemini, Google's largest and most capable AI model, to analyze your BigQuery data. BigQuery and Remote Functions can be used to analyze images and text input using the Gemini API in Vertex AI on Google Cloud. The following instructions should help you get started.

Calling Gemini using SQL

This repo allows you to pass requests to Gemini using SQL and get the results back like you would for any other BigQuery query. This approach has several advantages:

It allows users who are familiar with SQL to leverage the power of Gemini without needing to write additional code
You can more easily analyze a large batch of data rather than having to make individual requests for each image or text prompt
You don't need to export your data from BigQuery before you can analyze it with Gemini

About this demo

We've created a Terraform module that deploys all the necessary resources to call the Gemini API in Vertex AI using SQL in BigQuery.

After the module is deployed, you will have access to two BigQuery Remote Functions:

Analyze images and text (multimodal input) using gemini_bq_demo_image: This remote function takes an image from GCS as an input and prompts the Gemini model to create a brief description of the image
Analyze text using gemini_bq_demo_text: This remote function takes text in a BigQuery table and uses the text directly as prompts for the Gemini model to return a response

You will also have access to two BigQuery stored procedures which allow you to easy test each remote function using real image and text data:

image_query_remote_function_sp: This SQL query uses the gemini_bq_demo_image remote function to send a list of image URIs stored in an object table (deployed as part of the module) along with a prompt to Gemini
text_query_remote_function_sp: This SQL query uses the gemini_bq_demo_text remote function to send a sample BigQuery table with pre-written text prompts (deployed as part of the module) to Gemini

Getting Started

Deploying the infrastructure

Note: Though using a new Google Cloud project for this example is not a requirement, it might be easiest to use a new Google Cloud project for this. This makes cleanup much easier, as you can delete the whole project to ensure all assets are removed and it ensures no potential conflicts with existing resources. You can also remove resources by running terraform destroy after you deploy the resources, but it will also disable the associated APIs.

1. Clone this repo in Cloud Shell

You'll need to set your Google Cloud project in Cloud Shell, clone this repo locally first, and set the working directory to this folder using the following commands.

gcloud config set project <PROJECT ID>
git clone https://github.com/GoogleCloudPlatform/generative-ai/
cd ./generative-ai/gemini/use-cases/applying-llms-to-data/using-gemini-with-bigquery-remote-functions

2. Enable the Cloud Resource Manager API

Check to make sure the Cloud Resource Manager API is enabled

3. Initialize Terraform

First, initialize Terraform by running

terraform init

4. Review resources

Review the resources that are defined in the configuration:

terraform plan

5. Deploy the Terraform script

terraform apply

When you're prompted to perform the actions, enter yes. Terraform will prompt you to provide your project ID and region. This sample has been tested using region us-central1. Terraform will display messages showing the progress of the deployment.

After all the resources are created, Terraform displays the following message:

Apply complete!

The Terraform output also lists the following additional information that you'll need:

The link to open the BigQuery editor to invoke the image_query_remote_function_sp stored procedure that analyzes the sample images provided
The link to open the BigQuery editor to invoke the text_query_remote_function_sp stored procedure that analyzes the sample text prompts provided

If you need to see your Terraform outputs again, simply enter terraform output into your command line.

Analyzing sample data with your new deployment

Now that the resources have been deployed, your BigQuery Remote Functions are ready to be used in SQL queries.

1. Analyze text and images (multimodal)

Gemini will analyze the sample images when you invoke the image_query_remote_function_sp stored procedure. Simply click Invoke stored procedure for the stored procedure, then click Run for the resulting query to get the image descriptions generated by Gemini through your Remote Function.

2. Analyze text only

Gemini will analyze the prewritten text prompts when you invoke the text_query_remote_function_sp stored procedure. Simply click Invoke stored procedure, then click Run in the resulting query to get the responses generated by Gemini through your Remote Function.

How it works

Architecture Diagrams

Image analysis

Sample images are uploaded to a Cloud Storage bucket and a GCS object table is created in BigQuery
Cloud Workflows creates a stored procedure in BigQuery that contains the sample query, which references the object table created in step 1 to pass images to the remote function for analysis using the Gemini API in Vertex AI
The stored procedure from step 2 is used to invoke the Cloud Function through a BigQuery connection
The Cloud Function analyzes the sample images by passing them to the Gemini API in Vertex AI (step 5) to get a brief description of the sample images and returns results from the Gemini API in Vertex AI as query results

Text analysis

Cloud Workflows creates a stored procedure in BigQuery that contains the sample query and provisions the sample_text_prompts table, which contains sample text prompts to describe various landmarks. The sample query passes these prompts to the remote function for analysis using the Gemini API in Vertex AI
The stored procedure from step 2 is used to invoke the Cloud Function through a BigQuery connection
The Cloud Function analyzes the text input from the sample text by passing them to the Gemini API in Vertex AI (step 4) to get generate a text response to each prompt and returns results from the Gemini API in Vertex AI as query results

Pricing Estimation

Installing this demo (by running terraform apply in the Cloud Shell CLI) and running the stored procedures for text and image analysis 4 times each day will cost approximately $0.06 USD per month. See the Google Cloud Pricing Calculator for more details. The total monthly cost will vary as your usage varies, including how often you deploy and teardown this demo.

NOTE: Charges for Multimodal model consumption in Vertex AI take effect on January 15, 2024. We estimate running this demo 4 times per day will increase the total cost by $3.73 USD per month. See the Vertex AI pricing page for Generative AI for full details. Below is a breakdown of this estimate:

Text analysis
- $0.02/month for Text Input
- $0.23/month for Text Output
Image analysis
- $3.30/month for Image Input
- $0.18/month for Text Output

Keep in mind that generative AI models like Gemini are non-deterministic, so associated costs will vary based on the output length and cannot be definitively estimated.

Make it your own

You can adapt this demo for your own use case! Check out the instructions below for image and text analysis.

Analyze images

You can get started analyzing any images you have uploaded to Cloud Storage by:

Create a Cloud Storage object table

If your bucket is stored in the same region that you deployed this demo, you should be able to reuse the existing BigQuery connection. If not, you will likely have to create a new one.
Modify the context variable on line 40 of the Cloud Function

You can do this by editing the gemini-bq-demo-image that was deployed. Click the Edit button at the top of the Function Details page, then click Next to see the in-line editor. Change the value of the context variable to provide tell Gemini what it should do with your images, then click Deploy.

Check out this sample notebook for inspiration and ideas of what you can ask Gemini to do with an image.
Update the image_query_remote_function_sp stored procedure and run

Update line 5 of the stored procedure to reference the object table you created in step 1 and run the query.

Analyze text

You can get started by analyzing your own text inputs without having to modify the existing Cloud Function. Simply replace the text_prompt in line 3 of the text_query_remote_function_sp stored procedure with whatever text you want to analyze. You can enter a single string of text or you can reference a column of text prompts from a BigQuery table. If you are using a column from a BigQuery table, be sure to update the table reference on line 5 of the stored procedure.

Clean up

After you are finished with the demo, you can delete all the resources you created with the following steps:

Update the variables.tf file

Change the default value for the force_destroy variable from false to true. Change the default value for the deletion_protection variable from false to true.
Run terraform apply

This applies the changes you made in step 1 to your resources so they can be easily deleted.
Delete the BigQuery dataset

Run the following command in your CLI to delete the BigQuery dataset created:
```
    bq rm -r -f -d gemini_demo
```
Run terraform destroy

This deletes all the remaining resources you created.