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Multi-tenant vector search with Amazon Aurora PostgreSQL and Amazon Bedrock Knowledge Bases
- This article explores the fully managed approach using Amazon Bedrock Knowledge Bases to integrate data sources and generative AI applications with Amazon Aurora PostgreSQL.
- It discusses a multi-tenant use case involving home survey data stored in an Amazon S3 bucket and the retrieval augmented generation (RAG) approach for response generation.
- Amazon Bedrock Knowledge Bases simplifies the ingestion of data into an Aurora PostgreSQL-compatible vector store using a low-code approach.
- Steps include configuring Aurora PostgreSQL for vector stores, ingesting data, and enforcing multi-tenant data isolation.
- Querying vector data, leveraging RAG for enhanced prompts, and metadata filtering for tenant data isolation are highlighted.
- Best practices for scaling, performance optimization, and deploying multi-tenant vector stores are outlined.
- Fully-managed features like Amazon Bedrock streamline pipeline management for building efficient generative AI applications.
- The article concludes by emphasizing the importance of tenant data isolation in a multi-tenant pooled data model.
- Authors Josh Hart and Nihilson Gnanadason provide insights into building and modernizing SaaS applications on AWS.
- Readers are encouraged to try both self-managed and fully managed approaches and share feedback for further development.
- Recommendations for cleaning up resources to avoid additional charges and a call to action to explore the approaches are included.
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