Amazon Bedrock Knowledge Bases

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.

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.

In this post, we’ll explore how to leverage Amazon 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.

Amazon 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 Amazon Bedrock Knowledge Bases.

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

Untold Studios is a tech-driven, leading creative studio specializing in high-end visual effects and animation. This post details how we used Amazon Bedrock to create an AI assistant (Untold Assistant), providing artists with a straightforward way to access our internal resources through a natural language interface integrated directly into their existing Slack workflow.

Aetion is a leading provider of decision-grade real-world evidence software to biopharma, payors, and regulatory agencies. In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets.

This post discusses the adoption and evaluation of Amazon Nova foundation models by Trellix, a leading company delivering cybersecurity’s broadest AI-powered platform to over 53,000 customers worldwide.

The post showcases how generative AI can be used to logic, reason, and orchestrate integrations using a fictitious business process. It demonstrates strategies and techniques for orchestrating Amazon Bedrock agents and action groups to seamlessly integrate generative AI with existing business systems, enabling efficient data access and unlocking the full potential of generative AI.

The solution presented in this post demonstrates a powerful pattern for accelerating video and audio review workflows while maintaining human oversight. By combining the power of AI models in Amazon Bedrock with human expertise, you can create tools that not only boost productivity but also maintain the critical element of human judgment in important decision-making processes.

By leveraging the generative AI capabilities and tooling of Amazon Bedrock, you can create an intelligent nerve center that connects diverse data sources, converts data into actionable insights, and creates a comprehensive plan to mitigate supply chain risks. This post walks through how Amazon Bedrock Flows connects your business systems, monitors medical device shortages, and provides mitigation strategies based on knowledge from Amazon Bedrock Knowledge Bases or data stored in Amazon S3 directly. You’ll learn how to create a system that stays ahead of supply chain risks.

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.

In this post, we propose a solution using DigitalDhan, a generative AI-based solution to automate customer onboarding and digital lending. The proposed solution uses Amazon Bedrock Agents to automate services related to KYC verification, credit and risk assessment, and notification. Financial institutions can use this solution to help automate the customer onboarding, KYC verification, credit decisioning, credit underwriting, and notification processes.