Amazon Bedrock lets developers connect to foundational AI models and build knowledge bases that link applications to data sources like text, images, and videos. This enables rapid prototyping for virtual assistants, agent workflows, and content creation. The core process involves converting data into numerical vectors through embedding, then storing them in a vector database.

The workflow uses Amazon OpenSearch Service as the vector store. After an S3 bucket holds source documents, Bedrock's knowledge base feature handles the vector creation and storage. OpenSearch organizes these vectors into an index for efficient search and retrieval. When a query is made, it's converted into a vector to find the closest matches in the database.

A demonstration shows creating an S3 bucket, configuring a Bedrock knowledge base with an embedding model, and selecting OpenSearch Serverless for storage. The system then syncs the data, creating an index in OpenSearch. Users can test the setup by asking questions; Bedrock retrieves relevant vector chunks and generates a precise response, citing the original source documents for verification.