Category: HAQM Bedrock

Organizations are increasingly seeking to modernize their database infrastructure by migrating from legacy database engines such as Microsoft SQL Server and Oracle to more cost-effective and scalable open source alternatives such as PostgreSQL. This transition not only reduces licensing costs but also unlocks the flexibility and innovation offered by PostgreSQL’s rich feature set. In this post, we demonstrate how to convert and test database code from Microsoft SQL Server and Oracle to PostgreSQL using the generative AI capabilities of HAQM Bedrock.

Healthcare providers manage an ever-growing volume of patient data and medication information to help ensure safe, effective treatment. Although traditional database systems excel at storing patient records, they require complex queries to access information. By adding generative AI capabilities, healthcare providers can now use natural language to search patient records and verify medication safety, rather than writing complex database queries. In this post, I show you a solution that uses HAQM Bedrock and HAQM DynamoDB to create an AI agent that helps healthcare providers quickly identify potential drug interactions by validating new prescriptions against a patient’s current medication records.

In this post, we describe a solution to integrate generative AI applications with relational databases like HAQM Aurora PostgreSQL-Compatible Edition using RDS Data API (Data API) for simplified database interactions, HAQM Bedrock for AI model access, HAQM Bedrock Agents for task automation and HAQM Bedrock Knowledge Bases for context information retrieval.

Text-to-SQL can automatically transform analytical questions into executable SQL code for enhanced data accessibility and streamlined data exploration, from analyzing sales data and monitoring performance metrics to assessing customer feedback. In this post, we explore how to use HAQM Relational Database Service (HAQM RDS) for PostgreSQL and HAQM Bedrock to build a generative AI text-to-SQL chatbot application using Retrieval Augmented Generation (RAG). We’ll also see how we can use HAQM MemoryDB with vector search to provide semantic caching to further accelerate this solution.

Authorization systems are a critical component of modern applications, yet traditional approaches like role-based access control (RBAC) and attribute-based access control (ABAC) struggle to meet the complex access control requirements of today’s enterprises. In this post, we introduce a relationship-based access control (ReBAC) as an alternative for enterprise scale authorization. We explore how the proposed […]

In this post, we demonstrate how to use generative AI and HAQM Bedrock to transform natural language questions into graph queries to run against a knowledge graph. We explore the generation of queries written in the SPARQL query language, a well-known language for querying a graph whose data is represented as Resource Description Framework (RDF).

In this post, we present an approach to using natural language processing (NLP) to query an HAQM Aurora PostgreSQL-Compatible Edition database. The solution presented in this post assumes that an organization has an Aurora PostgreSQL database. We create a web application framework using Flask for the user to interact with the database. JavaScript and Python code act as the interface between the web framework, HAQM Bedrock, and the database.

In this post, we discuss the fully managed approach using HAQM Bedrock Knowledge Bases to simplify the integration of the data source with your generative AI application using Aurora. HAQM Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and HAQM available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using HAQM Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.

Iterate.ai is an enterprise AI platform company delivering innovative AI solutions to industries such as retail, finance, healthcare, and quick-service restaurants. Among its standout offerings is Frontline, a workforce management platform powered by AI, designed to support and empower Frontline workers. Available on both the Apple App Store and Google Play, Frontline uses advanced AI tools to streamline operational efficiency and enhance communication among dispersed workforces. In this post, we give an overview of durable semantic caching in HAQM MemoryDB, and share how Iterate used this functionality to accelerate and cost-optimize Frontline.