Build multi-tenant agentic chat applications on enterprise data with Amazon Bedrock Managed Knowledge Base
Learn how to build a multi-tenant agentic document chat application on Amazon Bedrock Managed Knowledge Base, where users upload documents and immediately ask grounded questions. This post covers the ingestion and retrieval flows, the asynchronous indexing lifecycle, per-user data isolation, and best practices for operating the solution at scale.
The article discusses how to build a multi-tenant agentic chat application on enterprise data using Amazon Bedrock Managed Knowledge Base. Multi-tenant agentic chat assistants are becoming increasingly common among large-scale customers, with document chat being the most requested feature. The conversational interface is easy to create, but the underlying multi-tenant agentic retrieval system is more challenging.
Each tenant's documents must remain isolated from others, with verified identity enforcement rather than relying on client-provided values for isolation. Agentic retrieval adds complexity, as the agent must break down questions into sub-queries and perform multiple retrievals while maintaining tenant isolation. Building, securing, and operating this infrastructure can be a significant undertaking for teams.
Amazon Bedrock Managed Knowledge Base simplifies this process by handling ingestion, storage, embedding, and ranking, eliminating the need for infrastructure provisioning or capacity monitoring. It also provides built-in agentic retrieval, which uses iterative planning and multi-hop retrievals to answer complex questions while respecting access permissions at each step.
The article outlines the architecture for a multi-tenant agentic document chat application using Amazon Bedrock Knowledge Bases. The application handles two main data flows: document ingestion and conversational retrieval. User uploads and status updates occur asynchronously, with files up to 6MB sent inline and larger files uploaded to Amazon S3.
A custom connector ingests user uploads directly into the knowledge base, becoming retrievable within seconds. The application uses Amazon API Gateway, AWS Lambda, Amazon Cognito for authentication, Amazon SQS for decoupling uploads from ingestion, Amazon DynamoDB for tracking indexing status, and Amazon S3 for staging larger files and hosting the single-page application.
By separating concerns and leveraging Amazon Bedrock's capabilities, the solution provides a straightforward-to-operate multi-tenant document chat experience with grounded responses and per-user isolation.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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