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Elevate RAG for numerical analysis using Amazon Bedrock Knowledge Bases

AWS Machine Learning - AI

Amazon Bedrock Knowledge Bases 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 Knowledge Base provide a resolution to this issue.

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Enhance your media search experience using Amazon Q Business and Amazon Transcribe

AWS Machine Learning - AI

Mediasearch Q Business supercharges the way you consume media files by using them as part of the knowledge base 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.

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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning - AI

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. With just a few configuration steps, you can dramatically expand your chatbot’s knowledge base and capabilities, all while maintaining a streamlined UI.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

Additionally, you can access device historical data or device metrics. Additionally, you can access device historical data or device metrics. The device metrics are stored in an Athena DB named "iot_ops_glue_db" in a table named "iot_device_metrics". The AI assistant interprets the user’s text input.

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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning - AI

Our internal AI sales assistant, powered by Amazon Q Business , will be available across every modality and seamlessly integrate with systems such as internal knowledge bases, customer relationship management (CRM), and more. As new models become available on Amazon Bedrock, we have a structured evaluation process in place.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning - AI

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 knowledge bases of documents. To understand these limitations, let’s consider again the example of deciding where to invest based on financial reports.

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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning - AI

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 knowledge base, see our new LMA solution. Transcripts are then stored in the project’s S3 bucket under /transcriptions/TranscribeOutput/.