Self-managed multi-tenant vector search with Amazon Aurora PostgreSQL

This article discusses how to implement a self-managed multi-tenant vector search solution using Amazon Aurora PostgreSQL and pgvector extension for generative AI applications. The key highlights include:

• Using pgvector extension to create a vector store in Aurora PostgreSQL
• Implementing a custom ingestion pipeline to convert documents into vector embeddings
• Utilizing Amazon Bedrock for embedding and text generation models
• Enforcing multi-tenant data isolation through PostgreSQL row-level security
• Implementing Retrieval Augmented Generation (RAG) workflow for semantic search

The solution demonstrates how organizations can build scalable, secure vector search capabilities for generative AI applications, with a focus on tenant data isolation and flexible vector storage.