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Amazon Bedrock has recently launched two new capabilities to address these evaluation challenges: LLM-as-a-judge (LLMaaJ) under Amazon Bedrock Evaluations and a brand new RAG evaluation tool for Amazon Bedrock KnowledgeBases.
In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents , Amazon Bedrock KnowledgeBases , and Amazon Bedrock Guardrails. Solution overview This section outlines the architecture designed for an email support system using generative AI.
In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledgebases at scale. Solution overview The following architecture diagram represents the high-level design of a solution proven effective in production environments for AWS Support Engineering.
Building cloud infrastructure based on proven best practices promotes security, reliability and cost efficiency. To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This systematic approach leads to more reliable and standardized evaluations.
However, if you want to use an FM to answer questions about your private data that you have stored in your Amazon Simple Storage Service (Amazon S3) bucket, you need to use a technique known as Retrieval Augmented Generation (RAG) to provide relevant answers for your customers. The following diagram depicts a high-level RAG architecture.
One way to enable more contextual conversations is by linking the chatbot to internal knowledgebases and information systems. Integrating proprietary enterprise data from internal knowledgebases enables chatbots to contextualize their responses to each user’s individual needs and interests.
In November 2023, we announced KnowledgeBases for Amazon Bedrock as generally available. Knowledgebases allow Amazon Bedrock users to unlock the full potential of Retrieval Augmented Generation (RAG) by seamlessly integrating their company data into the language model’s generation process.
By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. In the following sections, we explain how to deploy this architecture.
Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledgebase without the involvement of live agents. The following diagram illustrates the solution architecture and workflow. Create an Amazon Lex bot.
KnowledgeBases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. If you want more control, KnowledgeBases lets you control the chunking strategy through a set of preconfigured options.
Amazon Bedrock KnowledgeBases is a fully managed capability that helps you implement the entire RAG workflow—from ingestion to retrieval and prompt augmentation—without having to build custom integrations to data sources and manage data flows. Latest innovations in Amazon Bedrock KnowledgeBase provide a resolution to this issue.
You can now use Agents for Amazon Bedrock and KnowledgeBases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.
However, to unlock the long-term success and viability of these AI-powered solutions, it is crucial to align them with well-established architectural principles. This post explores the new enterprise-grade features for KnowledgeBases on Amazon Bedrock and how they align with the AWS Well-Architected Framework.
The following diagram illustrates the conceptual architecture of an AI assistant with Amazon Bedrock IDE. Solution architecture The architecture in the preceding figure shows how Amazon Bedrock IDE orchestrates the data flow. Choose Create new KnowledgeBase and enter a name for your new knowledgebase.
We have built a custom observability solution that Amazon Bedrock users can quickly implement using just a few key building blocks and existing logs using FMs, Amazon Bedrock KnowledgeBases , Amazon Bedrock Guardrails , and Amazon Bedrock Agents. Additionally, you can choose what gets logged.
Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like KnowledgeBases, Guardrails, and Agents. The resulting distilled models, such as DeepSeek-R1-Distill-Llama-8B (from base model Llama-3.1-8B An S3 bucket prepared to store the custom model.
Traditionally, documents from portals, email, or scans are stored in Amazon Simple Storage Service (Amazon S3) , requiring custom logic to split multi-document packages. By converting unstructured document collections into searchable knowledgebases, organizations can seamlessly find, analyze, and use their data.
Amazon Bedrock KnowledgeBases provides foundation models (FMs) and agents in Amazon Bedrock contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. Amazon Bedrock KnowledgeBases offers a fully managed RAG experience.
When Amazon Q Business became generally available in April 2024, we quickly saw an opportunity to simplify our architecture, because the service was designed to meet the needs of our use caseto provide a conversational assistant that could tap into our vast (sales) domain-specific knowledgebases.
Seamless live stream acquisition The solution begins with an IP-enabled camera capturing the live event feed, as shown in the following section of the architecture diagram. MediaLive also extracts the audio-only output and stores it in an Amazon Simple Storage Service (Amazon S3) bucket, facilitating a subsequent postprocessing workflow.
Enterprises provide their developers, engineers, and architects with a range of knowledgebases and documents, such as usage guides, wikis, and tools. But these resources tend to become siloed over time and inaccessible across teams, resulting in reduced knowledge, duplication of work, and reduced productivity.
They offer fast inference, support agentic workflows with Amazon Bedrock KnowledgeBases and RAG, and allow fine-tuning for text and multi-modal data. To do so, we create a knowledgebase. Complete the following steps: On the Amazon Bedrock console, choose KnowledgeBases in the navigation pane. Choose Next.
It’s a fully serverless architecture that uses Amazon OpenSearch Serverless , which can run petabyte-scale workloads, without you having to manage the underlying infrastructure. The following diagram illustrates the solution architecture. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledgebase.
In this post, we explore how you can use Amazon Q Business , the AWS generative AI-powered assistant, to build a centralized knowledgebase for your organization, unifying structured and unstructured datasets from different sources to accelerate decision-making and drive productivity.
As Principal grew, its internal support knowledgebase considerably expanded. With QnABot, companies have the flexibility to tier questions and answers based on need, from static FAQs to generating answers on the fly based on documents, webpages, indexed data, operational manuals, and more.
Moreover, Amazon Bedrock offers integration with other AWS services like Amazon SageMaker , which streamlines the deployment process, and its scalable architecture makes sure the solution can adapt to increasing call volumes effortlessly. This is powered by the web app portion of the architecture diagram (provided in the next section).
Data source curation and authorization – The CCoE team created several Amazon Simple Storage Service (Amazon S3) buckets to store their curated content, including cloud governance best practices, patterns, and guidance. They set up a general bucket for all users and specific buckets tailored to each business unit’s needs.
With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts. AWS Landing Zone architecture in the context of cloud migration AWS Landing Zone can help you set up a secure, multi-account AWS environment based on AWS best practices.
This solution shows how Amazon Bedrock agents can be configured to accept cloud architecture diagrams, automatically analyze them, and generate Terraform or AWS CloudFormation templates. Solution overview Before we explore the deployment process, let’s walk through the key steps of the architecture as illustrated in Figure 1.
This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledgebase to provide personalized, context-aware responses tailored to your specific situation. These data sources provide contextual information and serve as a knowledgebase for the LLM.
In this post, we describe the development journey of the generative AI companion for Mozart, the data, the architecture, and the evaluation of the pipeline. Solution overview The policy documents reside in Amazon Simple Storage Service (Amazon S3) storage. The following diagram illustrates the solution architecture.
You can now use Agents for Amazon Bedrock and KnowledgeBases for Amazon Bedrock to build specialized agents and AI-powered assistants that run actions based on natural language input prompts and your organization’s data. Both the action groups and knowledgebase are optional and not required for the agent itself.
Depending on the use case and data isolation requirements, tenants can have a pooled knowledgebase or a siloed one and implement item-level isolation or resource level isolation for the data respectively. You can also bring your own customized models and deploy them to Amazon Bedrock for supported architectures.
If a user has a role configured with a specific guardrail requirement (using the bedrock:GuardrailIdentifier condition), they shouldnt use that same role to access services like Amazon Bedrock KnowledgeBases RetrieveAndGenerate or Amazon Bedrock Agents InvokeAgent. Satveer Khurpa is a Sr.
Although Amazon Q is a great way to get started with no code for business users, Amazon Bedrock KnowledgeBases offers more flexibility at the API level for generative AI developers; we explore both these solutions in the following sections. How do I keep my generative AI applications up to date with an ever-evolving knowledgebase?”
The AMP demonstrates how organizations can create a dynamic knowledgebase from website data, enhancing the chatbot’s ability to deliver context-rich, accurate responses. An overview of the RAG architecture with a vector database used to minimize hallucinations in the chatbot application.
Solution overview The following figure illustrates a sample architecture using Amazon Q Business plugins. Under API schema , for API schema source , select one of the following options: Select Select from Amazon S3 to select an existing API schema from an Amazon Simple Storage Service (Amazon S3) bucket.
It uses the provided conversation history, action groups, and knowledgebases to understand the context and determine the necessary tasks. This is based on the instructions that are interpreted by the assistant as per the system prompt and user’s input. Additionally, you can access device historical data or device metrics.
Enterprises that have adopted ServiceNow can improve their operations and boost user productivity by using Amazon Q Business for various use cases, including incident and knowledge management. ServiceNow Obtain a ServiceNow Personal Developer Instance or use a clean ServiceNow developer environment.
With KnowledgeBases for Amazon Bedrock , you can simplify the RAG development process to provide more accurate anomaly root cause analysis for plant workers. Solution overview The following diagram illustrates the solution architecture. Answers are generated through the Amazon Bedrock knowledgebase with a RAG approach.
An approach to product stewardship with generative AI Large language models (LLMs) are trained with vast amounts of information crawled from the internet, capturing considerable knowledge from multiple domains. However, their knowledge is static and tied to the data used during the pre-training phase. Guillermo Menéndez Corral is a Sr.
The Unsuccessful query responses and Customer feedback metrics help pinpoint gaps in the knowledgebase or areas where the system struggles to provide satisfactory answers. These logs can be delivered to multiple destinations, such as CloudWatch, Amazon Simple Storage Service (Amazon S3), or Amazon Data Firehose.
The following diagram illustrates the solution architecture. For knowledge retrieval, we use Amazon Bedrock KnowledgeBases , which integrates with Amazon Simple Storage Service (Amazon S3) for document storage, and Amazon OpenSearch Serverless for rapid and scalable search capabilities.
QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and KnowledgeBases for Amazon Bedrock , a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. If a knowledgebase ID is configured , the Bot Fulfillment Lambda function forwards the request to the knowledgebase.
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