%pip install dbdemos
import dbdemos
dbdemos.install('llm-rag-chatbot')
Try this demo in your workspace!
Run the following in your notebook:
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LLM Chatbot With Retrieval Augmented Generation (RAG) and DBRX

Deploy your Chatbot on Databricks AI with RAG, DBRX, Vector Search & Mosaic AI Agent Evaluation
00-RAG-LLM-RAG-Introduction
Introduction notebook.
01-first-step
01-First-Step-RAG-On-Databricks
Quickstart: deploy your RAG in 10 min. Start here!
chain
Your full chain used to build your chatbot.
02-simple-app
01-Data-Preparation-and-Index
Prepare doc chunks and build your Vector Search Index
02-Deploy-RAG-Chatbot-Model
Leverage Foundation Model to perform RAG and answer customer questions.
03-Deploy-Frontend-Lakehouse-App
Leverage Lakehouse App to deploy your front app.
03-advanced-app
01-PDF-Advanced-Data-Preparation
Ingestion unstructured data and create a self-managed vector search index
02-Advanced-Chatbot-Chain
Advanced langchain chain, working with chat history.
03-Offline-Evaluation
Evaluate your chatbot with an offline dataset.
04-Online-Evaluation
Log your endpoint payload as a Delta table.
config
Setup your database and model endpoint.