Overview

This guide walks you through setting up the Gemini AI Column Description Generator in a new Google Cloud Platform (GCP) project. This tool automatically generates descriptions for database columns using Google's Gemini AI model integrated with BigQuery ML.

Step 1: Enable Required APIs

The following APIs need to be activated

1. BigQuery API - For data processing and ML operations
2. Vertex AI API - For Gemini AI model access gcloud services enable aiplatform.googleapis.com
3. Cloud Resource Manager API - For project management

Step 2: Set Up IAM Roles and Permissions

Required Roles for Script Execution

The service account or user running the script needs the following roles:

BigQuery Roles

• BigQuery Data Editor (roles/bigquery.dataEditor)
  • Create and modify datasets and tables
  • Execute queries and manage temporary tables
• BigQuery Job User (roles/bigquery.jobUser)
  • Run BigQuery jobs and queries
• BigQuery Connection User (roles/bigquery.connectionUser)
  • Use BigQuery connections for ML models

Vertex AI Roles

• Vertex AI User (roles/aiplatform.user)
  • Access Vertex AI services and models
• ML Developer (roles/ml.developer)
  • Use ML models in BigQuery ML

Assign Roles via Console

1. Go to IAM & Admin > IAM
2. Find your user account or service account
3. Click "Edit principal"
4. Add each required role listed above

Assign Roles via gcloud CLI

# Replace [USER_EMAIL] with the actual email
export USER_EMAIL="your-email@domain.com"
export PROJECT_ID="your-project-id"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="user:$USER_EMAIL" \
    --role="roles/bigquery.dataEditor"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="user:$USER_EMAIL" \
    --role="roles/bigquery.jobUser"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="user:$USER_EMAIL" \
    --role="roles/bigquery.connectionUser"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="user:$USER_EMAIL" \
    --role="roles/aiplatform.user"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="user:$USER_EMAIL" \
    --role="roles/ml.developer"

Step 3: Create BigQuery Datasets

Create Source Dataset

Create a dataset to hold your source tables (if not already existing):

CREATE SCHEMA IF NOT EXISTS `your-project-id.DM`
OPTIONS (
  description = "Data Mart - Source tables for analysis",
  location = "US"  -- Choose appropriate location
);

Create Output Dataset

Create a dataset for storing the AI-generated descriptions:

CREATE SCHEMA IF NOT EXISTS `your-project-id.TEST_GEMINI`
OPTIONS (
  description = "Gemini AI generated column descriptions",
  location = "US"  -- Must match source dataset location
);

Step 4: Set Up Vertex AI Connection

Create Vertex AI Connection in BigQuery (documentation)

1. Via BigQuery Console:

2. 
   

   • Go to BigQuery in Google Cloud Console

   • Click + Add data, and then use the search bar for data sources to search for Vertex AI. Click on the result for Vertex AI then click Vertex AI Models: BigQuery Federation.

   • Select Connection type as Vertex AI remote models, remote functions and BigLake (Cloud Resource) if not already selected and set Connection ID to vertex-ai-connection.

Get Connection Service Account

After creating the connection, note the service account email that's automatically created. You'll need to grant it permissions.

-- Get connection details
SELECT * FROM `your-project-id.us-central1.INFORMATION_SCHEMA.CONNECTIONS`
WHERE connection_name = 'vertex-ai-connection';

Grant Vertex AI Access to Connection Service Account

The connection service account needs access to Vertex AI:

# Get the service account email from the connection details above
export CONNECTION_SA="service-account-email@gcp-sa-bigquery-condel.iam.gserviceaccount.com"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$CONNECTION_SA" \
    --role="roles/aiplatform.user"

Step 5: Create Gemini AI Model in BigQuery

Create Remote Model

CREATE OR REPLACE MODEL `your-project-id.YOUR_DATASET.gemini_remote_model`
REMOTE WITH CONNECTION `your-project-id.europe-west1.vertex-ai-connection`
OPTIONS (
  ENDPOINT = 'gemini-2.0-flash'
);

Test Model Connection

-- Test the model with a simple query
SELECT ml_generate_text_llm_result
FROM ML.GENERATE_TEXT(
  MODEL `your-project-id.TEST_GEMINI.gemini_remote_model`,
  (SELECT "Test connection to Gemini AI" AS prompt),
  STRUCT(TRUE AS flatten_json_output)
);

Step 6: Configure and Deploy the Script

Update Configuration Variables

In the main script, update these variables with your project details:

-- Update these variables in the script
DECLARE dataset_name STRING DEFAULT 'your-project-id.DM';
DECLARE output_dataset STRING DEFAULT 'TEST_GEMINI';
DECLARE model_name STRING DEFAULT 'TEST_GEMINI.gemini_remote_model';
DECLARE sample_size INT64 DEFAULT 5;

Step 7: Execute the Script

1. Open BigQuery Console
2. Navigate to your project
3. Paste the complete script in the query editor: https://gitlab.syensqo.com/syensqo-connected-research/toolkit/toolkit/-/blob/master/SQL/gemini_column_description.sql?ref_type=heads
4. Update the configuration variables at the top
5. Run the script

Step 8: Verify Installation

Check Results

-- Verify the output table was created
SELECT 
  table_name,
  COUNT(*) as column_count
FROM `your-project-id.TEST_GEMINI.column_descriptions_with_ai`
GROUP BY table_name;

-- View sample results
SELECT * FROM `your-project-id.TEST_GEMINI.column_descriptions_with_ai` LIMIT 5;

Troubleshooting

Common Issues

1. "Permission denied" errors

   • Verify all required APIs are enabled
   • Check IAM roles are correctly assigned
   • Ensure connection service account has Vertex AI access

2. "Model not found" errors

   • Verify the Vertex AI connection was created successfully
   • Check the model name matches exactly
   • Ensure the connection region matches your dataset location

3. "No tables found" errors

   • Verify source dataset exists and contains tables
   • Check dataset names in the configuration variables
   • Ensure the service account has access to source datasets

4. Quota exceeded errors

   • Check Vertex AI quotas in the Cloud Console
   • Consider reducing sample_size or processing fewer tables at once

Monitoring and Costs

• Monitor usage: Go to Cloud Console > Vertex AI > Quotas to track API usage
• Cost estimation: Gemini AI pricing varies by model and token usage
• BigQuery costs: Query processing and storage costs apply

Security Best Practices

1. Use least privilege: Grant only the minimum required permissions
2. Service accounts: Consider using dedicated service accounts for production
3. Data access: Implement row-level security for sensitive datasets
4. Audit logging: Enable Cloud Audit Logs for compliance
5. Network security: Use VPC Service Controls for additional isolation

Next Steps

Once installed, you can:

• Schedule the script to run periodically using Cloud Scheduler
• Integrate with data catalog tools for metadata management
• Customize prompts for domain-specific descriptions
• Extend to other Vertex AI models for different use cases

Support

For issues specific to:

• BigQuery: BigQuery Documentation
• Vertex AI: Vertex AI Documentation
• IAM: IAM Documentation

• Summarize Text using SQL and LLMs in BigQuery ML