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Building Applications Using Amazon Bedrock

Building Applications Using Amazon Bedrock

2h 9mAdvanced2024-03-19

Authors

Lee Assam

Lee Assam

Electrical and Software Engineer, University Instructor

Course details

Learn how to build generative AI applications using Amazon Bedrock. In this course, Lee Assam, a principal technologist as well as a university instructor, covers a series of typical use cases, including a semantic search using the RAG (Retrieval Augmented Generation) architecture via a conversational chatbot, text summarization, and content generation. Discover how to use the LangChain framework to build Generative AI applications. Get an introduction to Streamlit, which has become the de facto platform to easily prototype and deploy web-based data and Generative AI applications. Use vector stores like Knowledge Bases for Amazon Bedrock and Amazon Kendra to index documents and websites. Step through building a conversational chatbot and implementing a variety of application enhancements. The course uses Python for development, and when you finish this course, you will be able to build and deploy Generative AI applications using Amazon Bedrock, using the tools you learn here.

Skills covered

Amazon BedrockTelecommunicationsMachine LearningFull-Stack Web DevelopmentAmazonGenerative AIProjectArtificial Intelligence (AI)Web DevelopmentNetwork and System Administration

Concepts

0. Introduction

  • 01 - Building Amazon Bedrock applications
  • 02 - What you should know
  • 03 - AWS setup
  • 04 - Set up AWS credentials

1. Key Concepts

  • 05 - Amazon Bedrock
  • 06 - LangChain
  • 07 - Streamlit
  • 08 - Retrieval-augmented generation (RAG)
  • 09 - Model overviews

2. Conversational Chatbot

  • 10 - Use case overview
  • 11 - Architecture review
  • 12 - Setting up your knowledge base
  • 13 - Writing the code - Knowledge base interaction
  • 14 - Demo - Knowledge base interaction
  • 15 - Coding - Adding Streamlit integration - Part 1
  • 16 - Coding - Adding Streamlit integration - Part 2
  • 17 - Demo - Streamlit UI

3. Application Enhancements

  • 18 - Conversation history
  • 19 - Coding - Supporting conversation history
  • 20 - Demo - Chatbot conversation history
  • 21 - Introduction to Amazon Kendra
  • 22 - Setting up the Amazon Kendra index
  • 23 - Coding - Amazon Kendra integration
  • 24 - Setting permissions for Amazon Kendra
  • 25 - Demo - Amazon Kendra and Amazon Bedrock integration
  • 26 - Agents for Bedrock
  • 27 - Configuring the Amazon Bedrock agent
  • 28 - Reviewing project files
  • 29 - Testing the agent in the AWS console

Conclusion

  • 30 - Cleanup for Amazon Bedrock
  • 31 - Continuing your application development with Amazon Bedrock

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