Category: HAQM Bedrock

Today, HAQM Web Services (AWS) announced the general availability of HAQM Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in HAQM Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in HAQM Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in HAQM Bedrock Knowledge Bases.

In this post, we explore how to use LangGraph and Mistral models on HAQM Bedrock to create a powerful multi-agent system that can handle sophisticated workflows through collaborative problem-solving. This integration enables the creation of AI agents that can work together to solve complex problems, mimicking humanlike reasoning and collaboration.

In this post, we’ll explore how to leverage HAQM Bedrock, LlamaIndex, and RAGAS to enhance your RAG implementations. You’ll learn practical techniques to evaluate and optimize your AI systems, enabling more accurate, context-aware responses that align with your organization’s specific needs.

In this post, we explain how BMW uses generative AI to speed up the root cause analysis of incidents in complex and distributed systems in the cloud such as BMW’s Connected Vehicle backend serving 23 million vehicles. Read on to learn how the solution, collaboratively pioneered by AWS and BMW, uses HAQM Bedrock Agents and HAQM CloudWatch logs and metrics to find root causes quicker. This post is intended for cloud solution architects and developers interested in speeding up their incident workflows.

In this post, we explore a generative AI solution leveraging HAQM Bedrock to streamline the WAFR process. We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This solution automates portions of the WAFR report creation, helping solutions architects improve the efficiency and thoroughness of architectural assessments while supporting their decision-making process.

HAQM Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search. In this post, we discuss using metadata filters with HAQM Bedrock Knowledge Bases.

In this post, we walk through how to discover, deploy, and use the Mistral AI Pixtral 12B model for a variety of real-world vision use cases.

In this post, we show how Anthropic’s Claude 3.5 Sonnet in HAQM Bedrock can be used for a variety of business-related cognitive tasks, such as problem-solving, critical thinking and ideation—to help augment human thinking and improve decision-making among knowledge workers to accelerate innovation.

In this post, we demonstrate how to implement this evaluation framework using HAQM Bedrock, compare the performance of different generator models, including Anthropic’s Claude and HAQM Nova on HAQM Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

Pattern is a leader in ecommerce acceleration, helping brands navigate the complexities of selling on marketplaces and achieve profitable growth through a combination of proprietary technology and on-demand expertise. In this post, we share how Pattern uses AWS services to process trillions of data points to deliver actionable insights, optimizing product listings across multiple services.