Getting Started with Multimodal Analysis on Snowflake Cortex AI

In this quickstart, you'll learn how to build an end-to-end application for multimodal analysis using AI models through Snowflake Cortex AI. This application uses AI_COMPLETE with models like Claude 4 Sonnet and Pixtral-large to extract insights, detect emotions, and generate descriptions from images, plus uses AI_TRANSCRIBE to transcribe audio with speaker identification - all within the Snowflake ecosystem.

Note: AI_COMPLETE multimodal capability and AI_TRANSCRIBE are currently in Public Preview.

What You'll Learn

Setting up a Snowflake environment for multimodal processing
Creating storage structures for image and audio data
Using AI_COMPLETE to analyze images with AI models
Implementing audio transcription with AI_TRANSCRIBE

What You'll Build

A multimodal analysis system that enables users to:

Upload and store images and audio files in Snowflake
Extract detailed insights from images using AI models
Identify scenes, objects, text, and emotions in images
Transcribe audio with speaker identification and precise timestamps
Generate custom descriptions based on specific prompts
Process media files individually
Combine image analysis with audio transcription for comprehensive content understanding

Prerequisites

Snowflake account in a supported region for AI functions
Account must have these features enabled:

To set up your Snowflake environment for multimodal analysis:

Download the setup.sql file
Open a new worksheet in Snowflake
Paste the contents of setup.sql or upload and run the file
The script will create:
- A new database and schema for your project
- Image and audio storage stages

Upload Media Files

After running the setup script:

Download the data.zip and unzip for sample images and audio files
For images:
- Navigate to Data > Databases > MULTIMODAL_ANALYSIS > MEDIA > IMAGES > Stages
- Click "Upload Files" button in top right
- Select your image files
For audio files:
- Navigate to Data > Databases > MULTIMODAL_ANALYSIS > MEDIA > AUDIO > Stages
- Click "Upload Files" button in top right
- Select your audio files
Verify upload success:

-- Check uploaded images
LS @MULTIMODAL_ANALYSIS.MEDIA.IMAGES;

-- Check uploaded audio files
LS @MULTIMODAL_ANALYSIS.MEDIA.AUDIO;

You should see your uploaded files listed with their sizes.

Let's create a notebook to explore multimodal analysis techniques:

Navigate to Projects > Notebooks in Snowflake
Click "+ Notebook" button in the top right
To import the existing notebook:
- Click the dropdown arrow next to "+ Notebook"
- Select "Import .ipynb" from the dropdown menu
- Upload the multimodal_analysis_notebook.ipynb file
In the Create Notebook popup:
- Select your MULTIMODAL_ANALYSIS database and MEDIA schema
- Choose an appropriate warehouse
- Click "Create" to finish the import

The notebook includes:

Setup code for connecting to your Snowflake environment
Functions for analyzing images with different AI models
Audio transcription with various modes (text, word-level, speaker identification)
Example analysis with various prompt types
Comparison between Claude 4 Sonnet and Pixtral-large models for analyzing images

Congratulations! You've successfully built an end-to-end multimodal analysis system using AI models via Snowflake Cortex. This solution allows you to extract valuable insights from both images and audio content, perform transcription with speaker identification, detect emotions, analyze scenes, and generate rich descriptions - all within the Snowflake environment using AI_COMPLETE and AI_TRANSCRIBE functions.

The combination of visual and audio analysis capabilities opens up powerful possibilities for content understanding, customer experience analysis, compliance monitoring, and automated content processing workflows.

What You Learned

How to set up Snowflake for multimodal content storage and processing
How to use AI_COMPLETE with AI models like Claude 4 Sonnet and Pixtral-large for comprehensive image analysis
How to implement audio transcription with AI_TRANSCRIBE including speaker identification and timestamps
How to create custom prompts for specialized analysis tasks
How to implement batch processing for multiple media files
How to combine image and audio analysis for enhanced content understanding