Tag: Generative AI

In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.

In this post, we look at a step-by-step implementation for using the custom document enrichment (CDE) feature within an Amazon Q Business application to process standalone image files. We walk you through an AWS Lambda function configured within CDE to process various image file types, and showcase an example scenario of how this integration enhances Amazon Q Business’s ability to provide comprehensive insights.

In this post, we explore how to use Amazon Q Developer CLI with the AWS Diagram MCP and the AWS Documentation MCP servers to create sophisticated architecture diagrams that follow AWS best practices. We discuss techniques for basic diagrams and real-world diagrams, with detailed examples and step-by-step instructions.

In this post, we demonstrate how agentic workflow patterns such as Retrieval Augmented Generation (RAG), multi-tool orchestration, and conditional routing with LangGraph enable end-to-end solutions that artificial intelligence and machine learning (AI/ML) developers and enterprise architects can adopt and extend. We walk through an example of a financial management AI assistant that can provide quantitative research and grounded financial advice by analyzing both the earnings call (audio) and the presentation slides (images), along with relevant financial data feeds.

In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.

This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.

In this post, we demonstrate how organizations can enhance their incident management capabilities by integrating PagerDuty Advance, an innovative set of agentic and generative AI capabilities that automate response workflows and provide real-time insights into operational health, with Amazon Q Business. We show how to configure PagerDuty Advance as a data accessor for Amazon Q indexes, so you can search and access enterprise knowledge across multiple systems during incident response.

With the increasing adoption of Amazon Bedrock, optimizing costs is a must to help keep the expenses associated with deploying and running generative AI applications manageable and aligned with your organization’s budget. In this post, you’ll learn about strategic cost optimization techniques while using Amazon Bedrock.

In this series of posts, you will learn how to build intelligent AI voice agents using Pipecat, an open-source framework for voice and multimodal conversational AI agents, with foundation models on Amazon Bedrock. It includes high-level reference architectures, best practices and code samples to guide your implementation.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.