Category: HAQM Bedrock Knowledge Bases

In this post, we explore how Infosys developed Infosys Event AI to unlock the insights generated from events and conferences. Through its suite of features—including real-time transcription, intelligent summaries, and an interactive chat assistant—Infosys Event AI makes event knowledge accessible and provides an immersive engagement solution for the attendees, during and after the event.

For this post, we implement a RAG architecture with HAQM Bedrock Knowledge Bases using a custom connector and topics built with HAQM Managed Streaming for Apache Kafka (HAQM MSK) for a user who may be interested to understand stock price trends.

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using HAQM 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 HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

This post demonstrates how HAQM 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 HAQM 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 HAQM Bedrock, AWS Lambda, and HAQM CloudWatch to create an AI assistant for effective incident management. This solution also uses HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents. The solution uses the power of HAQM 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 HAQM 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 HAQM Bedrock Guardrails.

This post focuses on RAG evaluation with HAQM 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 HAQM Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.