AWS Database Blog
Category: Generative AI
Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI
In this post, we explore how you can achieve a patient 360-degree view using Amazon Neptune and generative AI, and use it to strengthen your organization’s research and breakthroughs. By consolidating information from multiple sources such as electronic health records (EHRs), lab reports, prescriptions, and medical histories into a single location, healthcare providers can gain a better understanding of a patient’s health.
How Iterate.ai uses Amazon MemoryDB to accelerate and cost-optimize their workforce management conversational AI agent
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 Amazon MemoryDB, and share how Iterate used this functionality to accelerate and cost-optimize Frontline.
Amazon DynamoDB data models for generative AI chatbots
Amazon DynamoDB is ideal for storing chat history and metadata due to its scalability and low latency. DynamoDB can efficiently store chat history, allowing quick access to past interactions. User-specific metadata, such as preferences and session information, can be stored to personalize responses and manage active sessions, enhancing the overall chatbot experience.In this post, we explore how to design an optimal schema for chatbots, whether you’re building a small proof of concept application or deploying a large-scale production system.
Use a DAO to govern LLM training data, Part 4: MetaMask authentication
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In Part 3, we set up Amazon API Gateway and deployed AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job, creating a seamless data flow from IPFS to the knowledge base. In this post, we demonstrate how to configure MetaMask authentication, create a frontend interface, and test the solution.
Use a DAO to govern LLM training data, Part 3: From IPFS to the knowledge base
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia testnet using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In this post, we set up Amazon API Gateway and deploy AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job.
Use a DAO to govern LLM training data, Part 2: The smart contract
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, specifically focusing on the ingestion of training data. In this post, we focus on the writing and deployment of the Ethereum smart contract that contains the outcome of the DAO decisions.
Use a DAO to govern LLM training data, Part 1: Retrieval Augmented Generation
Blockchain and generative AI are two technical fields that have received a lot of attention in the recent years. There is an emerging set of use cases that can benefit from these two technologies. In this four-part series, we build a solution that governs the training data ingestion process of an AI model, using a smart contract and serverless components. We guide you through the different steps to build the solution. In this post, we review the overall architecture of the solution, and set up a large language model (LLM) knowledge base.
How Apollo Tyres built their tyre genealogy solution using Amazon Neptune and Amazon Bedrock
This is a joint post co-authored with Shailender Gupta, Global Head of Data Engineering, Reporting and Analytics at Apollo Tyres Apollo Tyres, headquartered in Gurgaon, India, is a prominent global tyre manufacturer with production facilities in India and Europe. The company has a widespread presence, selling tyres to consumers and industrial customers across over 100 […]
Analyzing PL/SQL and T-SQL code using Amazon Bedrock
In this post, we use the Anthropic Claude3 Sonnet large language model (LLM) on Amazon Bedrock to provide a detailed breakdown of the complex PL/SQL and T-SQL code, making it more understandable and comprehensible for developers who are new to a code base or working with unfamiliar code, because it helps them understand the logic and flow of the code more effectively.
Improve speed and reduce cost for generative AI workloads with a persistent semantic cache in Amazon MemoryDB
In this post, we present the concepts needed to use a persistent semantic cache in MemoryDB with Knowledge Bases for Amazon Bedrock, and the steps to create a chatbot application that uses the cache. We use MemoryDB as the caching layer for this use case because it delivers the fastest vector search performance at the highest recall rates among popular vector databases on AWS. We use Knowledge Bases for Amazon Bedrock as a vector database because it implements and maintains the RAG functionality for our application without the need of writing additional code.