Amazon Web Services

In this video, Derek demonstrates how to build a similarity search engine using AWS AppSync and Amazon Bedrock. He walks through the six steps to create a semantic search, including generating embeddings, storing data in a vector database, and querying with AWS AppSync. The demo showcases building a product search application using Amazon product data, highlighting the integration of AWS services like Aurora PostgreSQL, ECS, and Lambda. This tutorial provides a practical guide for developers looking to implement advanced search capabilities using generative AI and retrieval augmented generation (RAG) techniques.

00:00 - Introduction
01:08 - RAG with Amazon Bedrock
02:16 - Using AWS AppSync
04:32 - Demo
15:00 - Next Steps

product-information
skills-and-how-to
featured
generative-ai
ai-ml
Show 8 more

Up Next

VideoThumbnail
6:45

Grindr's Next-Gen Chat System: Leveraging AWS for Massive Scale and Security

Nov 22, 2024
VideoThumbnail
15:58

Revolutionizing Business Intelligence: Generative AI Features in Amazon QuickSight

Nov 22, 2024
VideoThumbnail
1:01:07

Accelerate ML Model Delivery: Implementing End-to-End MLOps Solutions with Amazon SageMaker

Nov 22, 2024
VideoThumbnail
9:30

Deploying ASP.NET Core 6 Applications on AWS Elastic Beanstalk Linux: A Step-by-Step Guide for .NET Developers

Nov 22, 2024
VideoThumbnail
47:39

Simplifying Application Authorization: Amazon Verified Permissions at AWS re:Invent 2023

Nov 22, 2024