How to Build a Recipe Knowledge Base with Amazon S3 Vectors and Bedrock Agents

In this tutorial, I walk you through implementing Amazon S3 Vectors by creating a practical recipe knowledge base. You’ll learn how to set up an S3 vector bucket, create a vector index (dimension 2024), upload and chunk a cookbook PDF, and use cosine similarity for accurate recipe searches.

What you’ll learn in this video:

✅ How to create and configure an S3 Vector Bucket in supported AWS regions
✅ Why vector buckets are different from general-purpose S3 buckets
✅ How to build a Vector Index and why dimensions matter for similarity search
✅ How to upload and chunk a PDF into vector representations for efficient retrieval
✅ The role of cosine similarity in comparing queries with stored vectors
✅ How to connect an S3 Vector Bucket to AWS Bedrock Knowledge Base
✅ Steps to create a Bedrock Agent (YouTube Chefbot) using Claude 3.5 Haiku
✅ How to configure the agent to give accurate, friendly recipe responses
✅ A real demo showing how the agent retrieves ingredients and recipes from the knowledge base

Watch the part 01 here:    • Amazon S3 Vectors + Bedrock: GenAI Concept...  

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