Retrieval Augmented Generation (RAG) Done Right: Database Data

Retrieval Augmented Generation (RAG) pipelines are becoming a prevalent method for implementing question-answering and chatbot applications using Large Language Models (LLMs) with structured or semi-structured data from sources such as databases like Snowflake, Redshift, or document databases like MongoDB. This blog post focuses on utilizing structured data within relational databases to build RAG applications, enabling new ways of interaction through question answering, chatbots, or summarization. The example provided involves using data from Airbnb listings and reviews in Barcelona, demonstrating how to ingest this data into Vectara for semantic search and conversational AI. The process involves creating a "document construction plan" to translate database entities into JSON documents for Vectara, which includes metadata for filtering and constructed text sections from database columns. The strategy allows users to extract valuable insights from user-generated content, enhancing applications like Airbnb by offering qualitative information in property searches. The post concludes by providing access to full code examples for data ingestion and querying, encouraging users to explore Vectara with their own data.