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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

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. in the GitHub repository you cloned to your local machine during deployment.

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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning - AI

The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information. The Lambda function processes the OpenSearch Service results and formats them for the Amazon Bedrock agent.

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How BQA streamlines education quality reporting using Amazon Bedrock

AWS Machine Learning - AI

The text extraction AWS Lambda function is invoked by the SQS queue, processing each queued file and using Amazon Textract to extract text from the documents. The text summarization Lambda function is invoked by this new queue containing the extracted text.

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Revolutionize trip planning with Amazon Bedrock and Amazon Location Service

AWS Machine Learning - AI

If an image is uploaded, it is stored in Amazon Simple Storage Service (Amazon S3) , and a custom AWS Lambda function will use a machine learning model deployed on Amazon SageMaker to analyze the image to extract a list of place names and the similarity score of each place name.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

AWS Cloud Development Kit (AWS CDK) Delivers AWS CDK knowledge with tools for implementing best practices, security configurations with cdk-nag , Powertools for AWS Lambda integration, and specialized constructs for generative AI services.

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Build a gen AI–powered financial assistant with Amazon Bedrock multi-agent collaboration

AWS Machine Learning - AI

Because of this flexible, composable pattern, customers can construct efficient networks of interconnected agents that work seamlessly together. In the following sections, we demonstrate the step-by-step process of constructing this multi-agent system. import json import boto3 client = boto3.client('ses')

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Create generative AI agents that interact with your companies’ systems in a few clicks using Amazon Bedrock in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Lets look at an example solution for implementing a customer management agent: An agentic chat can be built with Amazon Bedrock chat applications, and integrated with functions that can be quickly built with other AWS services such as AWS Lambda and Amazon API Gateway. The agent has the capability to: Provide a brief customer overview.