This lab demonstrates to a how to build a question-answering chatbot that uses stateless, retrieval augmented generation to provide answers to your questions about AWS Classroom courses.
Objectives:
By the end of this lab, you should be able to do the following:
- Explain how retrieval augmented generation can be used to improve the output produced by Generative AI applications.
- Deploy a Lex chatbot powered by a large language model.
- Connect Langchain to a model launched in Amazon SageMaker.
Prerequisites:
To complete this lab it is recommended that you have a technical understanding of:
- Amazon SageMaker
- Amazon Kendra
- Amazon Lex
Being familiar with FLAN and LLMs will be a benefit
Audience:
- Solutions Architect
- Data Engineer
- Data Scientist
- Developer
Outline:
Task 1: Deploy a large language model (LLM)
Task 2: Add an Amazon Kendra data source
Task 3: Create an Amazon Lex V2 chatbot
Task 4: Query your large language model endpoint
Task 5: Implement a RAG workflow
Task 6: Deploy a web app with Cloudformation
