Building and Querying Knowledge Bases

In the previous lesson, you evaluated when to use retrieval-augmented generation vs. fine-tuning. That decision framework now becomes concrete. Amazon Bedrock Knowledge Bases is a fully managed RAG service that handles every stage of the pipeline you studied conceptually, from ingesting your proprietary documents to retrieving relevant passages and generating grounded responses. Instead of integrating separate services for document ingestion, vector storage, and retrieval orchestration, you configure a knowledge base for Amazon Bedrock, and Bedrock manages ingestion, embedding, storage integration, and retrieval at runtime.

A Bedrock knowledge base is built from three core components working together. The data source connects to where your documents live, whether that is an S3 bucket, a Confluence workspace, SharePoint, Salesforce, or a web crawl target. The vector store holds the indexed embeddings and can be a managed OpenSearch Serverless collection provisioned automatically by Bedrock, or a customer-managed store such as Aurora PostgreSQL, Redis Enterprise, MongoDB Atlas, or Pinecone. The embedding model converts document chunks into dense vector representations that enable semantic search. Think of these three components as the supply chain of your RAG system: the data source supplies raw material, the embedding model transforms it, and the vector store warehouses the finished product for rapid retrieval.

This lesson walks through each stage of that pipeline, from ingestion configuration through query execution to sync management, giving you the ability to design and operate a production knowledge base end to end.

The following diagram illustrates the full architecture, from data sources through ingestion to the query-time retrieval and generation flow:

The document ingestion pipeline

Before a knowledge base can answer questions, your documents must pass through a four-stage ingestion process that transforms raw files into searchable vector representations.

• Loading reads source files from the configured data source and supports formats including PDF, DOCX, HTML, Markdown, and CSV.

• Chunking splits each loaded document into smaller, semantically meaningful segments that become the atomic units of retrieval.

• Embedding converts each chunk into a ...