Build enterprise search for agents with Amazon Bedrock Managed Knowledge Base

Building scalable enterprise knowledge bases for AI agents and generative applications has traditionally been complex, requiring integration of multiple components such as connectors, parsers, vector stores, and retrieval logic. Amazon Bedrock's new Managed Knowledge Base simplifies this process by providing a fully managed, scalable solution that handles data ingestion, parsing, storage, and retrieval seamlessly.

The service supports native connectors for popular enterprise data sources like Amazon S3, SharePoint, Confluence, Google Drive, and OneDrive, with real-time access control and security features. It automatically processes various document formats, including PDFs, PPTX, DOCX, audio, and video, splitting content into segments suitable for retrieval.

Setup involves just three API calls to create a knowledge base, add a data source, and start ingestion, with defaults that enable quick deployment. For more advanced customization, users can control embedding models, chunking strategies, and reranking.

The retrieval capabilities include direct lookup and agentic retrieval, which decomposes complex queries into sub-queries for multi-hop reasoning across multiple knowledge bases. This allows for sophisticated question answering, research, and comparison tasks.

Designed for production, the service integrates with AWS AgentCore Gateway, enabling secure, scalable, and observable deployment of knowledge bases as tools for MCP-compatible agents. It offers comprehensive metrics and real-time trace streaming for full observability.

Amazon Bedrock's Managed Knowledge Base is available in multiple regions, with straightforward pricing based on usage, including storage, API calls, and multi-hop retrievals. It aims to reduce infrastructure overhead, accelerate deployment, and improve security and compliance for enterprise AI applications.