Building RAG Applications with Amazon Bedrock Knowledge Bases - Implementing Retrieval-Augmented Generation
Automatically index documents on S3 and unify search and generation with the RetrieveAndGenerate API. Covers chunking strategy selection and safety enforcement with Guardrails.
The RAG Pattern and Knowledge Bases Overview
RAG (Retrieval-Augmented Generation) is a pattern that augments large language model (LLM) responses with external knowledge. An LLM alone cannot answer questions about the latest information or organization-specific data that was not in its training set. RAG searches for documents relevant to the question and passes their content as context to the LLM, producing accurate, evidence-based answers. Bedrock Knowledge Bases provides this RAG pattern as a managed service, integrating document indexing, vector search, and LLM-powered answer generation.
Data Sources and Chunking Strategies
You specify an S3 bucket as the data source, and documents in PDF, HTML, Markdown, Word, CSV, and other formats are automatically indexed. Documents are split into chunks, each chunk is vectorized, and the vectors are stored in a vector store. The chunking strategy is a critical design decision that directly affects search accuracy. Fixed-size chunking splits text into equal segments of a specified character count; it is simple but may break context. Semantic chunking splits based on the semantic coherence of sentences, preserving context more effectively. Hierarchical chunking uses a two-tier structure of parent chunks (broad context) and child chunks (detailed information); searches match on child chunks while the LLM receives the broader parent chunk context, improving accuracy.
Using the APIs and Guardrails Integration
The RetrieveAndGenerate API accepts a question, performs a relevant document search, and generates an answer in a single API call. The response includes the generated answer along with citation information (S3 URI and relevant passage) for the source documents. The Retrieve API performs search only, returning retrieved chunks for custom processing before passing them to the LLM. Applying Guardrails to Knowledge Bases automatically enforces content filters (blocking inappropriate content), PII masking (automatic removal of personal information), and denied topics (defining topics the model should refuse to answer) during answer generation. For a systematic study of generative AI, related books (Amazon) are also helpful.
Knowledge Bases Pricing
Knowledge Bases pricing consists of document indexing (vectorization) and query charges. Vectorization is billed at the embedding model rate (approximately $0.00002 per 1,000 tokens for Titan Embeddings V2). Query charges include the embedding model cost plus the LLM cost for answer generation (approximately $0.00025 per 1,000 input tokens for Claude 3 Haiku). When using OpenSearch Serverless as the vector store, OCU (OpenSearch Compute Unit) hourly charges (approximately $0.24/hour per OCU) become the primary cost driver. You can choose Pinecone or Aurora PostgreSQL pgvector as alternative vector stores to optimize costs.
Summary
Bedrock Knowledge Bases is a managed service for implementing the RAG pattern. It automatically indexes documents on S3 and unifies search and generation through the RetrieveAndGenerate API. By optimizing your chunking strategy and applying Guardrails, you can build accurate and safe RAG applications.