Semantic Modeling and Text-to-SQL Pipelines in Snowflake for AI Retrieval

Abstract

The paper will discuss the creation of Text-to-SQL pipelines and a semantic model in Snowflake to simplify the acquisition of AI-based data retrieval by businesses. The system can convert natural language queries into SQL queries using Snowflake Cortex, enabling users to access data without technical skills. Semantic modeling, hybrid search, and RAG are adopted together in the approach in a bid to enhance accuracy and relevance. We refer to pipeline design, workflow, performance, and governance, and demonstrate how the queries based on AI can be run quickly, securely, and in compliance. Real-life examples of financial, operational, and customer analytics indicate that timely decision-making and reduced manual labor can be achieved. This article puts Snowflake into focus as a powerful solution with respect to AI-based enterprise analytics.