Category: Amazon Bedrock

Running pgvector on Amazon Aurora PostgreSQL gives you a production-grade vector store on a database you already know, backed by the operational tooling, high availability, and scaling behaviour of Amazon Aurora. Production traffic does introduce a predictable set of operational considerations: query latency as the corpus grows, recall on filtered vector searches, memory headroom during index builds, and connection behaviour under load. This post is scoped to the database operations that keep the RAG retrieval layer healthy. In this post, we cover the operational practices that keep a pgvector workload healthy once you depend on it: choosing the right index and distance function, scaling with quantization and partitioning, managing Hierarchical Navigable Small World (HNSW) churn, sizing for memory-resident operation, and the observability signals that catch problems early.

In this post, you learn how to build a generative AI–powered migration assistant that helps automate portions of the last mile of code conversion. Using Anthropic’s Claude Sonnet 4.6 on Amazon Bedrock, the Strands Agents framework, and the AWS Knowledge MCP Server, you can automate the conversion and validation of PL/SQL objects against Amazon Aurora PostgreSQL-Compatible Edition. The assistant reads the AWS DMS SC assessment CSV, fetches live PL/SQL source from Oracle, converts each object, deploys the result to Aurora PostgreSQL through AWS Lambda, and runs automated tests, in a single pipeline.

In this post, we show how to build an Amazon Aurora DSQL database agent that other AI agents can discover and query through natural language using the A2A protocol. You’ll walk through how to build and deploy this using Amazon Bedrock AgentCore capabilities, including AgentCore Runtime for hosting, AgentCore Gateway for tool access via MCP, and the Strands Agents SDK for agent logic.

In this post, we walk through building an agent that investigates blocking and deadlocks on Amazon RDS for SQL Server — two issues that directly impact application performance, cause transaction failures, and lead to user-facing timeouts. Using the Strands Agents framework, we convert the T-SQL queries DBAs already use for these investigations into agent tools, combine them into a single agent, and deploy it to AgentCore Runtime.

In this post, we walk through the steps to set up the custom migration assistant agent and migrate a PostgreSQL database to Aurora DSQL. We demonstrate how to use natural language prompts to analyze database schemas, generate compatibility reports, apply converted schemas, and manage data replication through AWS DMS. As of this writing, AWS DMS does not support Aurora DSQL as target endpoint. To address this, our solution uses Amazon Simple Storage Service (Amazon S3) and AWS Lambda functions as a bridge to load data into Aurora DSQL.

In this post, we talk about the benefits of caching in generative AI applications. We also elaborated on a few implementation strategies that can help you create and maintain an effective cache for your application.

This post shows how to build a semantic cache using vector search on Amazon ElastiCache for Valkey. As detailed in the Impact section of this post, our experiments with semantic caching reduced LLM inference cost by up to 86 percent and improved average end-to-end latency for queries by up to 88 percent.

In this post, we walk through the steps of integrating Oracle Database@AWS (ODB@AWS) with Amazon Bedrock for by creating a RAG assistant application using an Amazon Titan embedding model in Amazon Bedrock and vectors stored in Oracle AI Database 26ai.

In this post, we explore an artificial intelligence and machine learning (AI/ML)-powered database monitoring tool for PostgreSQL, using a self-managed or managed database service such as Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL.

In this post, I show you how to build a serverless workflow orchestration engine that uses Amazon DynamoDB and AWS Lambda. The complete implementation is available in a GitHub repository, which includes two fully functional examples that you can deploy and run immediately to see the orchestration engine in action.