Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering

This article discusses multi-tenancy strategies for Retrieval Augmented Generation (RAG) applications using Amazon Bedrock Knowledge Bases, focusing on data segregation and access control within a single knowledge base.

• Organizations can use S3 folder structures and metadata filtering to efficiently manage multiple customer or business unit data
• Metadata filtering allows precise control over data access, ensuring each customer/unit only sees their own documents
• Supported file formats include text, Markdown, HTML, Word documents, CSV, and Excel spreadsheets
• Field-specific chunking enables granular control over data retrieval and improves query efficiency
• Multiple vector database integrations are possible, including OpenSearch Serverless, Aurora PostgreSQL, and Pinecone

The approach allows organizations to consolidate data sources, optimize costs, and maintain strict data privacy and access controls across different business segments.