Category: HAQM Bedrock

In this post, you’ll learn how to use OpenSearch Service and HAQM Bedrock to build AI-powered search and generative AI applications. You’ll learn about how AI-powered search systems employ foundation models (FMs) to capture and search context and meaning across text, images, audio, and video, delivering more accurate results to users. You’ll learn how generative AI systems use these search results to create original responses to questions, supporting interactive conversations between humans and machines.

By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on HAQM Bedrock and your data documentation.

HAQM Redshift has enhanced its Redshift ML feature to support integration of large language models (LLMs). As part of these enhancements, Redshift now enables native integration with HAQM Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in HAQM Redshift, helping you to build generative AI applications quickly. This powerful combination enables customers to harness the transformative capabilities of LLMs and seamlessly incorporate them into their analytical workflows.

In this post, we demonstrate the critical role of metadata in text-to-SQL generation through an example implemented for HAQM Athena using HAQM Bedrock. We discuss the challenges in maintaining the metadata as well as ways to overcome those challenges and enrich the metadata.

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset. This post shows how to integrate HAQM Bedrock with the AWS Serverless Data Analytics Pipeline architecture using HAQM EventBridge, AWS Step Functions, and AWS Lambda to automate a wide range of data enrichment tasks in a cost-effective and scalable manner.

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and HAQM Kinesis Data Streams for processing streaming data and HAQM Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.

In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like HAQM Redshift and HAQM OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from HAQM Redshift, we use the Anthropic Claude 2.0 on HAQM Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from HAQM OpenSearch Service, we chunk, and convert the source data chunks to vectors using HAQM Titan Text Embeddings model.

In March 2024, we announced the general availability of the generative artificial intelligence (AI) generated data descriptions in HAQM DataZone. In this post, we share what we heard from our customers that led us to add the AI-generated data descriptions and discuss specific customer use cases addressed by this capability. We also detail how the […]

In pursuit of a more efficient and customer-centric support system, organizations are deploying cutting-edge generative AI applications. These applications are designed to excel in four critical areas: multi-lingual support, sentiment analysis, personally identifiable information (PII) detection, and conversational search capabilities. Customers worldwide can now engage with the applications in their preferred language, and the applications […]