Press enter or click to view image in full size

3 min read17 hours ago

–

*Authors: *

,

We are thrilled to announce that Gemini 3 Flash is now available within AlloyDB for PostgreSQL. Gemini 3 Flash is a high-speed, lightweight model within the Gemini 3 family, designed to balance efficiency with the advanced reasoning and multimodal capabilities of the 3.0 series. Released today as a successor to Gemini 2.5 Flash, it emphasizes low-latency and cost-effectiveness, making it the primary choice for high-throughput applications like real-time chatbots, rapid prototyping, and “vibe coding”.

While Gemini 3 Pro handles the most intensive logic-heavy tasks, Gemini 3 Flash provides the “thinking” capabilities necessary for complex agentic workflows, such as multi-step tool calling and long-context processing, while maintaining the speed and affordability required for mass-scale deployment.

Unlocking the Power of Gemini 3.0 Flash with AI Functions

You can unlock the power of Gemini 3 Flash in AlloyDB with just a few simple steps. By leveraging native AI functions such as AI.IF, AI.GENERATE and AI.RANK, you can access the new model — which has demonstrated a boost in accuracy compared to Gemini 2.5 Flash.

Use Case: Sentiment Analysis

Consider a scenario where an inventory agent must decide which products to feature on a homepage based on the last hour of customer feedback. Instead of building a complex sentiment classifier from scratch, we simply use the ai.if function. This acts as a cognitive filter: it processes the tone of the text to answer, *‘Does this review show a positive sentiment?’. *This allows the agent to seamlessly filter the product_reviews table and retrieve a distinct list of top products that customers love, combining standard SQL precision (e.g., user filters for product category = ‘Electronics’ ) with the reasoning abilities of Gemini.

This integration allows AlloyDB to handle the heavy lifting of qualitative analysis. We can instantly isolate high-performing products in the ‘Electronics’ category, merging plain-text filtering of PostgreSQL with the intelligence of the 3.0-series model. Here’s a sample SQL query showcasing this.

SELECT product_nameFROM product_reviewsWHERE  ai.if(    prompt => 'Does this review show a positive sentiment ? Review: ' || product_review,    model_id => 'gemini-3-flash-preview')  AND product_category = 'Electronics';

Give This a Spin Yourself!

Step1: First, register the model with AlloyDB if you haven’t already. Make sure to replace <project_name> with your project.

CALLgoogle_ml.create_model(model_id => 'gemini-3-flash-preview',model_request_url => 'https://aiplatform.googleapis.com/v1/projects/<project_name>/locations/global/publishers/google/models/gemini-3-flash-preview:generateContent',model_qualified_name => 'gemini-3-flash-preview',model_provider => 'google',model_type => 'llm',model_auth_type => 'alloydb_service_agent_iam');

Step 2: Create the product_reviews table and add some data to it.

CREATE TABLE product_reviews (product_name VARCHAR(100),product_category VARCHAR(50),product_review TEXT);INSERT INTO product_reviews (product_name, product_category, product_review) VALUES('Wireless Headphones', 'Electronics', 'These headphones are amazing! Great sound quality and comfortable to wear.'),('Smartwatch', 'Electronics', 'The battery life is terrible. Have to charge it every day.'),('Espresso Machine', 'Home & Kitchen', 'Broke down after a few uses. Waste of money.'),('4K Smart TV', 'Electronics', 'Amazing picture quality! Love watching movies on this TV.'),('Portable Bluetooth Speaker', 'Electronics', 'Great sound for such a small speaker. Perfect for travel.');

Step 3: Run a query combining the power of plain text search (product_category = ‘Electronics’) and the intelligence of Gemini 3.0 Flash (positive sentiment identification).

SELECT product_nameFROM product_reviewsWHERE  ai.if(    prompt => 'Does this review show a positive sentiment ? Review: ' || product_review,    model_id => 'gemini-3-flash-preview')  AND product_category = 'Electronics';+ - - - - - - - - - - - - - - +| product_name                || - - - - - - - - - - - - - - || Wireless Headphones         || 4K Smart TV                 || Portable Bluetooth Speaker  |+ - - - - - - - - - - - - - - +

Voila! You just brought the world knowledge of Gemini Flash 3.0 to AlloyDB.

**Want to learn more? **Stay tuned for a deep-dive follow up blog on ‘SQL in the Gemini Era with AlloyDB’ — where we will walk through more practical examples of how to bring the power of Gemini 3.0 to AlloyDB through AI Functions.

Ready to get started? Spin up a free trial AlloyDB instance today and start building with Gemini 3.0 Flash. Explore how you can leverage AlloyDB AI’s features to supercharge your application.