strategy

The article discusses the implementation of a multi-tenant vector store using Amazon Aurora PostgreSQL-Compatible Edition and Amazon Bedrock Knowledge Bases. It outlines a scenario where users request home surveys, and surveyors update findings stored in an Amazon S3 bucket. The process involves converting documents into vector embeddings for enhanced natural language queries through the Retrieval Augmented Generation (RAG) technique. Key steps include: 1. Ingesting data from S3 into Amazon Bedrock Knowledge Bases. 2. Using an embeddings model to convert documents into vector embeddings. 3. Storing vector embeddings, data chunks, and metadata in Aurora with pgvector. 4. Submitting natural language queries that are transformed into embeddings for retrieval from the vector store. 5. Forwarding relevant documents to a large language model for response generation. To set up the vector store, prerequisites include access to an Amazon Aurora PostgreSQL-compatible database, Amazon S3 for document storage, and Amazon Bedrock for managing knowledge bases. The article provides SQL commands for creating the necessary schema, vector table, and index in Aurora, as well as instructions for ingesting data and ensuring multi-tenant data isolation through metadata filtering. Best practices for deploying a multi-tenant vector store include optimizing chunk size, evaluating embedding models, and monitoring query performance. The article emphasizes the benefits of fully managed solutions to reduce operational complexity.