Remove Knowledge Base Remove Lambda Remove Scalability
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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. Keep this blank if you decide not to use an existing knowledge base.

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

AWS Machine Learning - AI

Amazon Bedrock Agents coordinates interactions between foundation models (FMs), knowledge bases, and user conversations. The agents also automatically call APIs to perform actions and access knowledge bases to provide additional information. The documents are chunked into smaller segments for more effective processing.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

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 knowledge base integration.

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Access control for vector stores using metadata filtering with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

In November 2023, we announced Knowledge Bases for Amazon Bedrock as generally available. Knowledge bases 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.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

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.

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Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications

AWS Machine Learning - AI

Knowledge Bases 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.

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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

It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledge bases, 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.