Demo 1: Intelligent Data Operations with RAG, Sentiment Analysis, and Media Processing

This comprehensive demo showcases advanced AI-powered database capabilities through four key areas: Retrieval-Augmented Generation (RAG), native generative AI functions, sentiment analysis, and real-time media processing.

What This Demo Covers

RAG (Retrieval-Augmented Generation)

Demonstrates three distinct use cases for conversational database queries:

• Data Understanding: Ask natural language questions about your data inventory (e.g., "How many campaigns do we have?")
• Intelligent Analysis: Get AI-powered recommendations based on data patterns (e.g., finding the best campaign for specific demographics or identifying candidates with relevant skills)
• Creative Content Generation: Transform existing data into new formats like sales pitches, directly from campaign content

Native GenAI Functions

Shows how generative AI can be embedded directly into SQL workflows:

• Summarization: Automatically condense long-form content from multimedia sources
• Content Generation: Create product descriptions or marketing copy on-the-fly using database fields

Sentiment Analysis

Extract emotional insights from customer feedback by analyzing text sentiment directly within SQL queries, enabling real-time customer experience monitoring.

Data Visualization

Quick aggregation queries that prepare data for visual representation, such as category-based sales summaries.

Real-Time Media Analysis

Process and extract text from images and multimedia files as they're added to the database, with timestamp tracking for the latest content.

Key Takeaway

This demo illustrates how AI capabilities can be natively integrated into database operations, eliminating the need for external processing pipelines and enabling sophisticated analysis through simple SQL queries.

RAG

Different exercises showing all the possibilities or use cases with native rag.

RAG - Understanding of the data

select RAG('How many campaigns do we have in the table?',['campaigns']);

RAG - Analysis of the data

select RAG('What would be the best campaign for men over 30',['campaigns']);

select RAG('Who has experience with Distributed Systems',['articles'])

RAG - Creative

select RAG('Based on the content on the campaign Tech Gadgets Launch can you write a sales pitch',['campaigns']);

genAI functions

Here are few examples on how genAI is used natively in SQL databases

select id , summarize(multimedia_extracted_text) from articles limit 1;

select generate('Can you please create a product description for:'||product) from products

Sentiment Analysis

select id, comment , sentiment(comment) from customer_feedback

Visualization

select category, sum(price) from product_purchases group by category

Media Analysis in realtime

select file_name , multimedia_extracted_text , created_at from articles where file_type = 'image/jpeg' order by created_at desc limit 1