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Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic. Keep this blank if you decide not to use an existing knowledgebase.
Amazon Bedrock Agents coordinates interactions between foundation models (FMs), knowledgebases, and user conversations. The agents also automatically call APIs to perform actions and access knowledgebases to provide additional information. The documents are chunked into smaller segments for more effective processing.
Whether youre an experienced AWS developer or just getting started with cloud development, youll discover how to use AI-powered coding assistants to tackle common challenges such as complex service configurations, infrastructure as code (IaC) implementation, and knowledgebase integration.
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.
Organizations need to prioritize their generative AI spending based on business impact and criticality while maintaining cost transparency across customer and user segments. This visibility is essential for setting accurate pricing for generative AI offerings, implementing chargebacks, and establishing usage-based billing models.
KnowledgeBases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG) workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows, pushing the boundaries for what you can do in your RAG workflows.
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.
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.
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.
We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This scalability allows for more frequent and comprehensive reviews.
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.
Included with Amazon Bedrock is KnowledgeBases for Amazon Bedrock. As a fully managed service, KnowledgeBases for Amazon Bedrock makes it straightforward to set up a Retrieval Augmented Generation (RAG) workflow. With KnowledgeBases for Amazon Bedrock, we first set up a vector database on AWS.
The map functionality in Step Functions uses arrays to execute multiple tasks concurrently, significantly improving performance and scalability for workflows that involve repetitive operations. Furthermore, our solutions are designed to be scalable, ensuring that they can grow alongside your business.
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.
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.
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 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.
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.
With the rise of AI, you also need a knowledgebase. These knowledgebases can be hosted in OpenSearch. For this reason, I developed a Lambda function that would stop the pipeline when no messages are in the queue. The truth is it is easy, but it all depends on how much you care about the data you are ingesting.
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.
The Asure team was manually analyzing thousands of call transcripts to uncover themes and trends, a process that lacked scalability. Staying ahead in this competitive landscape demands agile, scalable, and intelligent solutions that can adapt to changing demands.
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. Cost efficiency is achieved through minimized development resources and lower operational costs compared to maintaining custom knowledge management systems.
Solution overview This solution uses the Amazon Bedrock KnowledgeBases chat with document feature to analyze and extract key details from your invoices, without needing a knowledgebase. We use Anthropic’s Claude 3 Sonnet model in Amazon Bedrock and Streamlit for building the application front-end.
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. With just a few configuration steps, you can dramatically expand your chatbot’s knowledgebase and capabilities, all while maintaining a streamlined UI.
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. Vaibhav Singh is a Product Innovation Analyst at Verisk, based out of New Jersey. Tarik Makota is a Sr.
For a generative AI powered Live Meeting Assistant that creates post call summaries, but also provides live transcripts, translations, and contextual assistance based on your own company knowledgebase, see our new LMA solution. Transcripts are then stored in the project’s S3 bucket under /transcriptions/TranscribeOutput/.
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. To understand these limitations, let’s consider again the example of deciding where to invest based on financial reports.
To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. The serverless batch pipeline architecture we presented offers a scalable solution for automating this process across large enterprise knowledgebases.
Architecture The solution uses Amazon API Gateway , AWS Lambda , Amazon RDS, Amazon Bedrock, and Anthropic Claude 3 Sonnet on Amazon Bedrock to implement the backend of the application. Technical Account Manager with AWS based out of New York. User authentication and authorization is done using Amazon Cognito. Sukhomoy Basak is a Sr.
We use AWS Lambda as our orchestration function responsible for interacting with various data sources, LLMs and error correction based on the user query. The unified, scalable pipeline we developed allows the PGA TOUR to scale to their full history of data, some of which goes back to the 1800s.
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.
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.
AWS Bedrock offers several key benefits: Scalability : AWS Bedrock models can scale to meet the demands of large and complex applications. Integration with AWS Services : Bedrock models seamlessly integrate with other AWS services, such as AWS Lambda, Amazon S3, and Amazon SageMaker.
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 ).
We demonstrate the process of integrating Anthropic Claude’s advanced natural language processing capabilities with the serverless architecture of Amazon Bedrock, enabling the deployment of a highly scalable and cost-effective solution.
The framework underpins our entire platform and forms our KnowledgeBase to ensure your cloud infrastructure is the most resilient, secure and efficient for your needs. Not only does it involve recovery from failure or service disruptions, but it also includes the issue of capacity management and scalability.
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 combination of retrieval augmented generation (RAG) and knowledgebases enhances automated response accuracy. The combination of retrieval-based and generation-based models in RAG allows for accessing databases and generating accurate and contextually relevant responses.
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 KnowledgeBases and Amazon Bedrock Agents.
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.
The knowledgebase contains loan-related documents to respond to loan-related queries. The loan handler AWS Lambda function uses the information in the KYC documents to check the credit score and internal risk score. The notification Lambda function emails information about the loan application to the customer.
Traditional SaaS solutions are designed for horizontal scalability and general applicability, which makes them suitable for managing repetitive tasks across diverse sectors, but they often lack domain-specific intelligence and the flexibility to address unique challenges in dynamic environments.
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