AWS Bedrock Knowledge Base Tutorial: RAG with OpenSearch & Titan Embeddings

Description In this part of our GenAI series, we dive deep into the heart of the RAG pipeline: AWS Bedrock Knowledge Bases. You will learn how to automate the heavy lifting of Retrieval-Augmented Generation by connecting Amazon S3 to a vector database without writing complex ingestion scripts. In this video, we cover: Knowledge Base Creation: Setting up Bedrock in the Mumbai region (ap-south-1). Data Ingestion: Connecting S3 buckets and configuring parsing strategies. Chunking Logic: How to split documents into 300-token chunks for optimal retrieval. Vector Database Setup: Automatically provisioning Amazon OpenSearch Serverless. Titan Embeddings v2: Using Amazon's latest embedding models to convert text into numerical formats. The Sync Process: A look behind the scenes at how Bedrock parses, chunks, and stores data. App Integration: Locating and using your Knowledge Base ID in your .env file. This setup simplifies the entire RAG process, handling everything from document storage to similarity searching behind the scenes. 🛠 Tech Stack: Amazon Bedrock Amazon OpenSearch Serverless Amazon Titan Text Embeddings v2 Amazon S3