Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications | Amazon Web Services
Amazon Bedrock Managed Knowledge Base is a managed service for building enterprise generative AI applications over proprietary data. It abstracts storage, retrieval, embeddings, reranking, and model selection while adding native connectors, automated parsing, and agentic retrieval. The result is a faster way to create scalable, accurate RAG-based agents without maintaining the underlying infrastructure. ## Enterprise Knowledge Base Challenges - Enterprise data is distributed across systems with different formats, permissions, and access controls. - RAG accuracy requires ongoing experimentation with parsing, chunking, embedding models, and retrieval behavior. - Organizations must support either massive knowledge bases containing millions of documents or thousands of smaller ones while controlling cost and enforcing security. - These infrastructure tasks divert developers from building application functionality. ## Managed RAG Infrastructure - Managed Knowledge Base combines storage, retrieval, embeddings, reranking, and foundation model selection into one managed primitive. - The service automatically selects and manages default embedding, reranking, and foundation models. - It can scale end-to-end RAG pipelines with only a few lines of code. - Through Amazon Bedrock AgentCore Gateway, it is available as a pre-built target with automatically generated role-based permissions, observability, and evaluation metrics. ## Native Data Connectors - Six built-in connectors ingest enterprise content and permissions directly from: - Amazon S3 - SharePoint - Confluence - Web Crawler - Google Drive - OneDrive - Connectors eliminate the need to build and maintain application-specific ingestion logic. - IAM roles are created automatically, with the option to customize permissions. ## Smart Parsing Smart Parsing automatically chooses ingestion and parsing techniques based on the source and content type. - Connector-specific models preserve important structure: - Web Crawler retains HTML structure, embedded images, and tables. - SharePoint preserves document hierarchies and relationships. - Multimodal processing detects document content types, identifies bounding boxes, and uses foundation models for extraction and captions. - Optimized chunking uses document structure and content type to balance retrieval quality and performance. - Developers can rely on defaults or customize chunking strategies for advanced use cases. ## Agentic Retriever Agentic Retriever is designed for complex questions requiring multi-step reasoning and retrieval. - It decomposes a query into a sequence of subquestions. - It performs multihop retrieval within one knowledge base or across multiple knowledge bases. - It evaluates intermediate results and stops once sufficient relevant passages have been found. - For example, it can connect a team’s cloud budget with an expense policy governing annual prepayments—something a single retrieval step might miss. - Retrieved context can then support more accurate, grounded responses from enterprise agents. ## Getting Started - Create a Managed Knowledge Base from the Amazon Bedrock AgentCore or Amazon Bedrock console. - Choose **Create Managed KB** and select **Unstructured Vector Store KB**. - Select a supported data connector and accept the optimized defaults. - After synchronization, connect the knowledge base to an agent or expose it as a tool for a foundation model. Managed Knowledge Base is best suited to teams that want production-ready enterprise RAG without assembling and operating every component themselves, while retaining customization options for specialized accuracy or governance requirements.
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