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In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledgebases at scale. By tracking failed jobs, potential data loss or corruption can be mitigated, maintaining the reliability and completeness of the knowledgebase.
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. These indexed documents provide a comprehensive knowledgebase that the AI agents consult to inform their responses.
KnowledgeBases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). In the following sections, we demonstrate how to create a knowledgebase with guardrails.
This post explores the new enterprise-grade features for KnowledgeBases on Amazon Bedrock and how they align with the AWS Well-Architected Framework. AWS Well-Architected design principles RAG-based applications built using KnowledgeBases for Amazon Bedrock can greatly benefit from following the AWS Well-Architected Framework.
With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. The following diagram depicts a high-level RAG architecture.
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.
At AWS re:Invent 2023, we announced the general availability of KnowledgeBases for Amazon Bedrock. With a knowledgebase, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).
The solution presented in this post takes approximately 15–30 minutes to deploy and consists of the following key components: Amazon OpenSearch Service Serverless maintains three indexes : the inventory index, the compatible parts index, and the owner manuals index. The following diagram illustrates the workflow of the agent.
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. Create new generative AI-powered intent in Amazon Lex using the built-in QnAIntent and point the knowledgebase.
An end-to-end RAG solution involves several components, including a knowledgebase, a retrieval system, and a generation system. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using KnowledgeBases for Amazon Bedrock. txt,md,html,doc/docx,csv,xls/.xlsx,pdf).
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.
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. KnowledgeBases for Amazon Bedrock provides fully managed RAG to supply the agent with access to your data.
The Lambda function interacts with Amazon Bedrock through its runtime APIs, using either the RetrieveAndGenerate API that connects to a knowledgebase, or the Converse API to chat directly with an LLM available on Amazon Bedrock. If you don’t have an existing knowledgebase, refer to Create an Amazon Bedrock knowledgebase.
In this solution, audio files stored in mp3 format are first uploaded to Amazon Simple Storage Service (Amazon S3) storage. Included with Amazon Bedrock is KnowledgeBases for Amazon Bedrock. With KnowledgeBases for Amazon Bedrock, we first set up a vector database on AWS.
Knowledgebase integration Incorporates up-to-date WAFR documentation and cloud best practices using Amazon Bedrock KnowledgeBases , providing accurate and context-aware evaluations. Brijesh specializes in AI/ML solutions and has experience with serverless architectures.
Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like KnowledgeBases, Guardrails, and Agents.
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. However, some components may incur additional usage-based costs.
When users pose questions through the natural language interface, the chat agent determines whether to query the structured data in Amazon Athena through the Amazon Bedrock IDE function, search the Amazon Bedrock knowledgebase, or combine both sources for comprehensive insights.
The complexity of developing and deploying an end-to-end RAG solution involves several components, including a knowledgebase, retrieval system, and generative language model. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using KnowledgeBases for Amazon Bedrock.
Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.
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. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledgebase. Choose your new knowledgebase to open it.
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.
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.
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.
The LLM generated text, and the IR system retrieves relevant information from a knowledgebase. We also use Vector Engine for Amazon OpenSearch Serverless (currently in preview) as the vector data store to store embeddings. An OpenSearch Serverless collection. Store the document embedding in OpenSearch Serverless.
API Gateway is serverless and hence automatically scales with traffic. The advantage of using Application Load Balancer is that it can seamlessly route the request to virtually any managed, serverless or self-hosted component and can also scale well. It’s serverless so you don’t have to manage the infrastructure.
Voice-based assistants like Alexa demonstrate how we are entering an era of conversational interfaces. We explore how to build a fully serverless, voice-based contextual chatbot tailored for individuals who need it. All the services that we use are serverless and fully managed by AWS. We discuss this later in the post.
Furthermore, by integrating a knowledgebase containing organizational data, policies, and domain-specific information, the generative AI models can deliver more contextual, accurate, and relevant insights from the call transcripts.
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 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.
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.
Built using Amazon Bedrock KnowledgeBases , Amazon Lex , and Amazon Connect , with WhatsApp as the channel, our solution provides users with a familiar and convenient interface. The solution’s scalability quickly accommodates growing data volumes and user queries thanks to AWS serverless offerings.
KnowledgeBases for Amazon Bedrock fully manages this experience by connecting to your private data sources, including Amazon Aurora , Amazon OpenSearch Serverless , MongoDB, Pinecone, and Redis Enterprise Cloud. More knowledgebase updates can be found in the News Blog. Read more about MemoryDB in the News Blog.
By using Amazon Bedrock Agents , action groups , and Amazon Bedrock KnowledgeBases , we demonstrate how to build a migration assistant application that rapidly generates migration plans, R-dispositions, and cost estimates for applications migrating to AWS. Choose Create knowledgebase and enter a name and optional description.
They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledgebases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. We use Amazon Bedrock Agents with two knowledgebases for this assistant.
Solution overview The policy documents reside in Amazon Simple Storage Service (Amazon S3) storage. During the solution design process, Verisk also considered using Amazon Bedrock KnowledgeBases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless.
Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like KnowledgeBases, Guardrails, and Agents.
With Bedrock’s serverless experience, one can get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into applications using the AWS tools without having to manage any infrastructure. The VitechIQ user experience can be split into two process flows: document repository, and knowledge retrieval.
To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. Scaling ground truth generation with a pipeline To automate ground truth generation, we provide a serverless batch pipeline architecture, shown in the following figure.
However, you can also use knowledgebases in Amazon Bedrock to build RAG solutions quickly. Using the Titan-Text-Embeddings model on Amazon Bedrock , convert the metadata into embeddings and store it in an Amazon OpenSearch Serverless vector store , which serves as our knowledgebase in our RAG framework.
The entire conversation in this use case, starting with generative AI and then bringing in human agents who take over, is logged so that the interaction can be used as part of the knowledgebase. We built the RAG solution as detailed in the following GitHub repo and used SageMaker documentation as the knowledgebase.
It provides a modular and flexible framework for combining LLMs with other components, such as knowledgebases, retrieval systems, and other AI tools, to create powerful and customizable applications. There was no monitoring, load balancing, auto-scaling, or persistent storage at the time.
After the profile is converted into text that explains the profile, a RAG framework is launched using Amazon Bedrock KnowledgeBases to retrieve related industry insights (articles, pain points, and so on). Building your knowledgebase for the industry insights document is the final prerequisite.
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