Category: Amazon Bedrock Knowledge Bases

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using Amazon Bedrock and CrewAI. We demonstrate how to build a multi-agent system that can automatically summarize new regulations, assess their impact on operations, and provide prescriptive technical guidance. You’ll learn how to use Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

This post demonstrates how Amazon Bedrock Knowledge Bases can help you scale your data management effectively while maintaining proper access controls on different management levels.

In this post, we explore practical strategies for using Constitutional AI to produce compliant content efficiently and effectively using Amazon Bedrock and LangGraph to build ConstitutionalChain for rapid content creation in highly regulated industries like finance and healthcare

In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.

In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. By extracting and structuring the key information from the source materials, the models can create a searchable knowledge base that allows you to quickly locate the data, formulas, and visualizations you need to support your work.

This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock, AWS Lambda, and Amazon CloudWatch to create an AI assistant for effective incident management. This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents. The solution uses the power of Amazon Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.

Healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. LLMs lack the ability to seamlessly access and synthesize data from these diverse and distributed sources. This limits their potential to provide comprehensive and well-informed insights for healthcare applications. In this blog post, we will explore how Mistral LLM on Amazon Bedrock can address these challenges and enable the development of intelligent healthcare agents with LLM function calling capabilities, while maintaining robust data security and privacy through Amazon Bedrock Guardrails.

This post focuses on RAG evaluation with Amazon Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. By the end of this post, you will understand how the latest Amazon Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.

In this post, we demonstrate how Octus migrated its flagship product, CreditAI, to Amazon Bedrock, transforming how investment professionals access and analyze credit intelligence. We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate, and Amazon OpenSearch Service.

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