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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

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Efficient metadata storage with Amazon DynamoDB – To support quick and efficient data retrieval, document metadata is stored in Amazon DynamoDB. This extracted text is then available for further analysis and the creation of metadata, adding layout-based structure and meaning to the raw data.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

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Along with each document slice, we store the metadata associated with it using an internal Metadata API, which provides document characteristics like document type, jurisdiction, version number, and effective dates. This process has been implemented as a periodic job to keep the vector database updated with new documents.

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Build agentic systems with CrewAI and Amazon Bedrock

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Consider a software development use case AI agents can generate, evaluate, and improve code, shifting software engineers focus from routine coding to more complex design challenges. Agentic systems, on the other hand, are designed to bridge this gap by combining the flexibility of context-aware systems with domain knowledge.

LLM 177
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

It also enables economies of scale with development velocity given that over 75 engineers at Octus already use AWS services for application development. Lambda functions process the event payload containing document location, perform format validation, and prepare content for extraction.

DevOps 98
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From concept to reality: Navigating the Journey of RAG from proof of concept to production

AWS Machine Learning Blog

You can use metadata filtering to narrow down search results by specifying inclusion and exclusion criteria. Enterprise Solutions Architect at AWS, experienced in Software Engineering, Enterprise Architecture, and AI/ML. However, in many situations, you might need to retrieve documents with specific attributes or content.

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A vision-language approach for foundational UI understanding

Google Research AI blog

Posted by Yang Li, Research Scientist, and Gang Li, Software Engineer, Google Research The computational understanding of user interfaces (UI) is a key step towards achieving intelligent UI behaviors. This metadata has given previous models advantages over their vision-only counterparts. their types, text content and positions).

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How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

AWS Machine Learning Blog

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption. Outside of work, he enjoys playing lawn tennis and reading books.