Ingesting data from structured tables in a database into Vectara allows for powerful semantic search, question-answering, and conversational AI applications using Large Language Models (LLMs). The process involves designing a "document construction plan" to translate each entity of interest in the database into a Vectara JSON document. This plan considers how to create metadata fields to support filtering and how to construct text by creating artificial sentences from one or more columns and their values. Ingesting data from Snowflake into Vectara can be achieved using the Python connector, and the process can be parallelized and sped up with tools like Ray. Once the data is ingested, users can ask questions about the data, such as "What is the best museum for kids?" or "Which neighborhood has the best Tapas places?", and receive relevant responses. This approach enables businesses to tap into their critical data in entirely new ways, increasing productivity, sales conversions, or improved user engagement.