A comprehensive guide to implementing RAG (Retrieval Augmented Generation) using Snowflake's Cortex Search service to query unstructured documents like PDFs. The tutorial walks through creating a search service that automatically handles document parsing, text chunking, and embedding generation without manual vector database setup. It demonstrates building an AI agent with Snowflake Intelligence that can answer natural language questions about document contents, with features like email distribution and cost monitoring. The solution supports multilingual documents and requires minimal code—just two queries to set up the entire pipeline.
Table of contents
Unstructured dataWelcome to RAG modelsSnowflake trial accountWe begin to create the RAG application in Snowflake.Document preprocessingActivate the Cortex Search serviceCreate the Cortex Search serviceGet Gabriel Jiménez’s stories in your inboxIn summarySnowflake IntelligenceAdditional toolsEmail distributionIn summaryCost controlConclusionSort: