Remove Data Ingestion Remove IDP Remove Large Language Models
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Build well-architected IDP solutions with a custom lens – Part 6: Sustainability

AWS Machine Learning Blog

An intelligent document processing (IDP) project typically combines optical character recognition (OCR) and natural language processing (NLP) to automatically read and understand documents. The IDP Well-Architected Custom Lens provides you with guidance on how to address common challenges in IDP workflows that we see in the field.

IDP 106
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Secure a generative AI assistant with OWASP Top 10 mitigation

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In this post, we show you an example of a generative AI assistant application and demonstrate how to assess its security posture using the OWASP Top 10 for Large Language Model Applications , as well as how to apply mitigations for common threats. Alternatively, you can choose to use a customer managed key.

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Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. This post focuses on the Cost Optimization pillar of the IDP solution.

IDP 99
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Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

AWS Machine Learning Blog

Amazon Q Business is a fully managed, secure, generative-AI powered enterprise chat assistant that enables natural language interactions with your organization’s data. By default, Amazon Q Business will only produce responses using the data you’re indexing. This behavior is aligned with the use cases related to our solution.

IDP 113
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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

Agent Creator is a no-code visual tool that empowers business users and application developers to create sophisticated large language model (LLM) powered applications and agents without programming expertise. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying.

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Discover insights from your Amazon Aurora PostgreSQL database using the Amazon Q Business connector

AWS Machine Learning Blog

Next, you need to index this data to make it available for a Retrieval Augmented Generation (RAG) approach, where relevant passages are delivered with high accuracy to a large language model (LLM). When the data source state is Active , choose Sync now.