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Hierarchical (and other) Indexes using LlamaIndex for RAG Content Enrichment

Salmon Run

At our weekly This Week in Machine Learning (TWIML) meetings, (our leader and facilitataor) Darin Plutchok pointed out a LinkedIn blog post on Semantic Chunking that has been recently implemented in the LangChain framework.

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Best AI Plugins for WordPress (2023)

Flipboard

WordLift WordLift automates search engine optimization by scanning a website’s content, enriching it with structured data or schema markup, and submitting it to Google. Akismet uses machine learning techniques to continuously enhance its performance after being installed on a WordPress site.

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Build well-architected IDP solutions with a custom lens – Part 3: Reliability

AWS Machine Learning Blog

Although the stages in an IDP workflow may vary and be influenced by use case and business requirements, the stages of data capture, document classification, text extraction, content enrichment, review and validation, and consumption are typically parts of IDP workflow. His focus is natural language processing and computer vision.

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Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning Blog

Common stages include data capture, document classification, document text extraction, content enrichment, document review and validation , and data consumption. Tim Condello is a senior artificial intelligence (AI) and machine learning (ML) specialist solutions architect at Amazon Web Services (AWS).

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. In this post, we describe a use case for custom content enrichment for insurance providers.

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