A knowledge base chatbot using RAG on CockroachDB | CockroachDB for AI/ML

Description

RAG enhances Large Language Models (LLMs) by grounding their responses in verified, domain-specific data, thus improving accuracy and reliability, which is often required in many applications, including customer support, content generation, knowledge base queries, and real-time information retrieval. CockroachDB is an ideal foundation for RAG applications, given its ability to store source data, metadata, and vector data all in the same place. By bringing vector capabilities into a distributed SQL database, you can easily implement AI use cases, simplify database management, and gain the scalability, resilience, and familiarity of a proven distributed SQL database. A knowledge base chatbot using RAG on CockroachDB | CockroachDB for AI/ML 00:00 Introduction 00:36 Why CockroachDB is a great fit for RAG 01:07 What is a vector data type? | Chatbot demo 01:33 Does CockroachDB support vector data types | Chatbot demo 02:08 How do you use vector indexes to improve search performance? | Chatbot demo 03:13 Looking at the schema 04:53 How to fix a changefeeds error | Chatbot demo 05:35 Looking at executed statements 06:46 Closing notes