AWS Database Blog

Category: Generative AI

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.

Power real-time vector search capabilities with Amazon MemoryDB

In today’s rapidly advancing world of generative artificial intelligence (AI), businesses across diverse industries are transforming customer experiences through the power of real-time search. By harnessing the untapped potential of unstructured data ranging from text to images and videos, organizations are able to redefine the standards of engagement and personalization. A key component of this […]

Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune

Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.

Key considerations when choosing a database for your generative AI applications

In this post, we explore the key factors to consider when selecting a database for your generative AI applications. We focus on high-level considerations and service characteristics that are relevant to fully managed databases with vector search capabilities currently available on AWS. We examine how these databases differ in terms of their behavior and performance, and provide guidance on how to make an informed decision based on your specific requirements.

Executive Conversations: Putting generative AI to work in omnichannel customer service with Prashant Singh, Chief Operating Officer at LeadSquared

Prashant Singh, Chief Operating Officer at LeadSquared, joins Pravin Mittal, Director of Engineering of Amazon Aurora, for a discussion on using generative artificial intelligence (AI) to scale their omnichannel customer service application while controlling costs. LeadSquared helps customers build truly connected, empowered, and self-reliant sales and service organizations, with the power of automation. This Executive […]

Build generative AI applications with Amazon Aurora and Amazon Bedrock Knowledge Bases

Amazon Bedrock is the easiest way to build and scale generative AI applications with foundational models (FMs). FMs are trained on vast quantities of data, allowing them to be used to answer questions on a variety of subjects. However, if you want to use an FM to answer questions about your private data that you […]

The convergence of AI and digital assets: A new dawn for financial infrastructure

The financial landscape has been in a constant state of evolution. From stock ticker machines to algorithmic trading systems, innovation has always been at the core of finance. Yet, among these transformative changes, the confluence of artificial intelligence (AI) and digital assets like cryptocurrencies, central bank digital currencies (CBDCs), and tokenized assets has the potential […]

The role of vector databases in generative AI applications

August, 2024: This post has been updated to reflect advances in technology and new features AWS released, to help you on your generative AI journey. Generative artificial intelligence (AI) has captured our imagination and is transforming industries with its ability to answer questions, write stories, create art, and generate code. AWS customers are increasingly asking […]