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In this new era of emerging AI technologies, we have the opportunity to build AI-powered assistants tailored to specific business requirements. Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management.
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.
As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This time efficiency translates to significant cost savings and optimized resource allocation in the review process.
Access to car manuals and technical documentation helps the agent provide additional context for curated guidance, enhancing the quality of customer interactions. Amazon Bedrock Agents coordinates interactions between foundation models (FMs), knowledgebases, and user conversations.
They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data.
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This is where the integration of cutting-edge technologies, such as audio-to-text translation and large language models (LLMs), holds the potential to revolutionize the way patients receive, process, and act on vital medical information.
Our strength lies in our dynamic team of experts and our cutting-edge technology, which, when combined, can deliver solutions of any scale. Their approach emphasizes cost-effectiveness, client satisfaction, and adaptable technological solutions that can grow with a client's business needs.
As AI technology continues to evolve, the capabilities of generative AI agents are expected to expand, offering even more opportunities for customers to gain a competitive edge. These managed agents play conductor, orchestrating interactions between FMs, API integrations, user conversations, and knowledge sources loaded with your data.
It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledgebases, agents, flows, evaluation, and guardrails. The Lambda function performs the actions by calling the JIRA API or database with the required parameters provided from the agent.
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.
Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry, empowering clients to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks. The user can pick the two documents that they want to compare.
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.
Some of the challenges in capturing and accessing event knowledge include: Knowledge from events and workshops is often lost due to inadequate capture methods, with traditional note-taking being incomplete and subjective. The below diagram shows the live-stream acquisition and real-time transcription.
In this post, we provide a step-by-step guide with the building blocks needed for creating a Streamlit application to process and review invoices from multiple vendors. Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python.
We will walk you through deploying and testing these major components of the solution: An AWS CloudFormation stack to set up an Amazon Bedrock knowledgebase, where you store the content used by the solution to answer questions. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledgebase.
Amazon Bedrock Agents enables this functionality by orchestrating foundation models (FMs) with data sources, applications, and user inputs to complete goal-oriented tasks through API integration and knowledgebase augmentation. In the first flow, a Lambda-based action is taken, and in the second, the agent uses an MCP server.
Error retrieval and context gathering The Amazon Bedrock agent forwards these details to an action group that invokes the first AWS Lambda function (see the following Lambda function code ). This contextual information is then sent back to the first Lambda function. Provide the troubleshooting steps to the user.
Diagram analysis and query generation : The Amazon Bedrock agent forwards the architecture diagram location to an action group that invokes an AWS Lambda. An AWS account with the appropriate IAM permissions to create Amazon Bedrock agents and knowledgebases, Lambda functions, and IAM roles.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. 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.
This post assesses two primary approaches for developing AI assistants: using managed services such as Agents for Amazon Bedrock , and employing open source technologies like LangChain. It uses the provided conversation history, action groups, and knowledgebases to understand the context and determine the necessary tasks.
This standardization is made possible by using advanced prompts in conjunction with KnowledgeBases for Amazon Bedrock , which stores information on organization-specific Terraform modules. In parallel, the AVM layer invokes a Lambda function to generate Terraform code. For creating lambda function, please follow instructions.
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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.
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 result is improved accuracy in FM responses, with reduced hallucinations due to grounding in verified data.
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.
This could be Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda , AWS SDK , Amazon SageMaker notebooks, or your workstation if you are doing a quick proof of concept. We aim to target and simplify them using generative AI with Amazon Bedrock. CUR data stored in an S3 bucket. For instructions, see Creating Cost and Usage Reports.
Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. The Lambda function associated with the Amazon Lex chatbot contains the logic and business rules required to process the user’s intent. A Lambda layer for Amazon Bedrock Boto3, LangChain, and pdfrw libraries.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock KnowledgeBases. Whether it’s troubleshooting a technical issue or providing industry insights, your chatbot becomes a more versatile and valuable resource for users.
To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledgebases of documents. In Part 1, we review the RAG design pattern and its limitations on analytical questions.
Our internal AI sales assistant, powered by Amazon Q Business , will be available across every modality and seamlessly integrate with systems such as internal knowledgebases, customer relationship management (CRM), and more. For example, “Cross-reference generated figures with golden source business data.”
Further, the FAQ feature in Amazon Kendra complements the broader retrieval capabilities of the service, allowing the RAG system to seamlessly switch between providing prewritten FAQ responses and dynamically generating responses by querying the larger knowledgebase. I can help you with queries based on the documents provided.
Before introducing the details of the new capabilities, let’s review how prompts are typically developed, managed, and used in a generative AI application. Use the available nodes to implement conditions, code hooks with AWS Lambda functions, or integrations with AI services such as Amazon Lex , among many other options to be added soon.
Mediasearch Q Business supercharges the way you consume media files by using them as part of the knowledgebase used by Amazon Q Business to generate reliable answers to user questions. For more information, see the pricing pages for Amazon Q Business , Amazon Kendra , Amazon Transcribe , Lambda , DynamoDB , and EventBridge.
These customers are choosing AWS because we are focused on doing what we’ve always done—taking complex and expensive technology that can transform customer experiences and businesses and democratizing it for customers of all sizes and technical abilities. As model sizes and complexity have grown, so has SageMaker’s scope.
Integration with AWS Services : Bedrock models seamlessly integrate with other AWS services, such as AWS Lambda, Amazon S3, and Amazon SageMaker. Let’s try to implement the Python AWS Lambda function that utilizes the AWS Bedrock’s Titan service to generate text based on an input prompt through a YAML-based cloud formation script.
You can securely integrate and deploy generative AI capabilities into your applications using services such as AWS Lambda , enabling seamless data management, monitoring, and compliance (for more details, see Monitoring and observability ). This is illustrated in the following diagram. Where to start?
At Amazon and AWS, we are always finding innovative ways to build inclusive technology. Chatbots are no longer a niche technology. This is possible due to the Amazon Cognito identity pool , which acts as a mediator between your application user and IAM services. In this post, we discuss voice-guided applications.
The Lambda function spins up an Amazon Bedrock batch processing endpoint and passes the S3 file location. The second Lambda function performs the following tasks: It monitors the batch processing job on Amazon Bedrock. Amazon Bedrock batch processes this single JSONL file, where each row contains input parameters and prompts.
This technology allows for automated responses, with only complex cases requiring manual review by a human, streamlining operations and enhancing overall productivity. The combination of retrieval augmented generation (RAG) and knowledgebases enhances automated response accuracy.
AI for IT operations (AIOps) is the application of AI and machine learning (ML) technologies to automate and enhance IT operations. 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.
The Amazon Bedrock agent forwards the details to an action group that invokes a Lambda function. Upon completion, the action group (Lambda function) sends the information back to the Amazon Bedrock agent, which then displays the status to the user. This gives your agent access to required services, such as Lambda.
Application controller layer (LLM orchestrator Lambda function) The application controller layer is usually vulnerable to risks such as LLM01:2025 Prompt Injection, LLM05:2025 Improper Output Handling, and LLM 02:2025 Sensitive Information Disclosure.
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