Cortex AISQL reimagines SQL into an AI query language for multimodal data, bringing powerful AI capabilities directly into Snowflake's SQL engine. It enables users to build scalable AI pipelines across text, images, and audio (coming soon) using familiar SQL commands. With native support for multimodal data through a new FILE datatype, Cortex AISQL seamlessly integrates AI operators with traditional SQL primitives like AI_FILTER and AGGREGATE, allowing analysts to process diverse data types more efficiently and cost-effectively while maintaining enterprise-grade security and governance.
What is Cortex AISQL?
Cortex AISQL bridges the traditional divide between structured and unstructured data analysis, eliminating the need for separate tools and specialized skills.
It delivers three key benefits:
- Simplicity through familiar SQL syntax that transforms any analyst into an AI engineer without complex coding
- High-performance processing through deep integration with Snowflake's query engine, offering 30%+ faster query runtime
- Cost efficiency with up to 60% savings compared to traditional AI implementations
By unifying all data types in a single platform with zero setup required, Cortex AISQL democratizes AI-powered analytics across the enterprise.
Use Cases
Cortex AISQL benefits organizations across industries dealing with diverse data types including:
- Financial services: Automate corporate action processing by filtering news feeds and joining with internal holdings
- Retail and e-commerce: Detect product quality issues by analyzing customer reviews and identifying concerning patterns
- Healthcare: Accelerate medical research by bridging unstructured clinical notes, transcripts and images with structured patient records
- Legal: Streamline contract analysis and compliance monitoring
- Media: Optimize content and target advertising through multimodal data analysis
Business analysts can extract insights without AI expertise, data engineers can build simpler pipelines, and data scientists can create richer feature sets, all using familiar SQL.
Prerequisites
- Access to a Snowflake account with ACCOUNTADMIN role
What You Will Learn
You'll learn how to use powerful operators of Cortex AISQL to analyze multimodal data within Snowflake using natural language.
- AI_COMPLETE: Generate AI-powered text completions or descriptions for various inputs including text and images
- AI_FILTER: Semantic filtering
- AI_AGG: Aggregate insights across multiple rows
- AI_CLASSIFY: Text and image classification
What You Will Build
Snowflake Notebook that helps you get started with using Cortex AISQL with multimodal data
Step 1. In Snowsight, create a SQL Worksheet and open setup.sql to execute all statements in order from top to bottom.
Step 2. Download sample images files and use Snowsight » Data » Add Data » Load files into a Stage to upload them to @DASH_DB.DASH_SCHEMA.DASH_IMAGE_FILES stage created in step 1.
Step 3. In Snowsight, create a SQL Worksheet and open images.sql to execute all statements in order from top to bottom.
Step 4. Click on cortex_aisql.ipynb to download the Notebook from GitHub. (NOTE: Do NOT right-click to download.)
Step 5. In Snowsight:
- On the left hand navigation menu, click on Projects » Notebooks
- On the top right, click on Notebook down arrow and select Import .ipynb file from the dropdown menu
- Select cortex_aisql.ipynb file you downloaded in the step above
- In the Create Notebook popup
- For Notebook location, select
DASH_DBandDASH_SCHEMA - For Python environment, select
Run on warehouse - For Query warehouse, select
DASH_WH_S - For Notebook warehouse, select default
SYSTEM$STREAMLIT_NOTEBOOK_WH - Click on Create button
- For Notebook location, select
Step 6. Open Notebook
- Click on Start button on top right
Here's the code walkthrough of the cortex_aisql.ipynb notebook that you downloaded and imported into your Snowflake account.
Import_Libraries
Import libraries required for running cells in the notebook.
AI_COMPLETE
Identify customer issues across text and image data using AI_COMPLETE() and see how the SQL operators work seamlessly across all modalities.
- Text: Emails
- Images: Screenshots
Consolidated_Data
Notice that native FILE datatype allows for consolidating all data formats into one table.
AI_FILTER
Semantically "JOIN" customer issues with existing solutions using JOIN ... ON AI_FILTER()
AI_AGG
Get aggregated insights across multiple rows using AI_AGG()
AI_CLASSIFY
Classification of labels that can be used in downstream applications using AI_CLASSIFY(). For example, to train ML models.
Congratulations! You've successfully created a Snowflake Notebook that helps you get started with using Cortex AISQL with multimodal data.
What You Learned
You've learned how to use powerful operators of Cortex AISQL to analyze multimodal data within Snowflake using natural language.
- AI_COMPLETE: Generate AI-powered text completions or descriptions for various inputs including text and images
- AI_FILTER: Semantic filtering
- AI_AGG: Aggregate insights across multiple rows
- AI_CLASSIFY: Text and image classification