Azure – Building Multimodal Generative Experiences. Part 2

Azure – Building Multimodal Generative Experiences. Part 2
Manjunatha Sai Uppu

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Create a composed Document Intelligence Model

  • Composed models in Azure AI document intelligence enable users to submit a form when they don’t know which is the best model to use.
  • Composed Models
    • when you have forms with unusual or unique formats, you can create and train your own custom models in Azure AI Document Intelligence.
    • You can create custom model of 2 types (custom template model and custom neural models) refer to previous post to know more about them.
    • Once you have created a set of custom models, you must assemble them into a composed model. you can do this on the Azure AI Studio.
    • Custom model Compatibility
    • Custom template models are responsible with other custom template models across 3.0 and 2.1 API versions
    • Custom neural models are composable with other custom neural models.
    • Custom neural models can’t be composed with custom template models.
    • Custom models

Build a document intelligence custom skill for azure search.

  • If you integrate AI Search with an Azure AI Document intelligence solution, you can enrich your index with fields that your Azure AI Document Intelligence models are trained to extract.
  • Azure AI Search is a search service hosted in Azure that can index content on your permises or in a cloud location.
  • There are 5 stages in Indexing process

    • Document Cracking. In document cracking, the indexer opens the content files and extracts their content.
    • Field Mappings. Fields such as titles, names, dates, and more are extracted from the content. You can use field mappings to control how they’re stored in the index.
    • Skillset Execution. In the optional skillset execution stage, custom AI processing is done on the content to enrich the final index.
    • Output field mappings. If you’re using a custom skillset, its output is mapped to index fields in this stage.
    • Push to index. The results of the indexing process are stored in the index in Azure AI Search.
  • AI Search Skillset

    • Key Phrase extraction
    • Language Detection
    • Merge
    • Sentiment
    • Translation
    • Image Analysis
    • Optical character recognition
  • we can use custom skills too and they can be used for 2 reasons

  • The list of built-in skills doesn’t include the type of AI Enrichment you need.

  • you want to train your own model to analyze the data

  • 2 types of custom skills that you can create

    • Azure Machine Learning Custom Skills
    • Custom Web API Skills
      Refer to this link for building an Azure AI Document Intelligence Custom Skill

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