Create a knowledge mining solution with Azure AI Search

Fundamentally, Azure AI Search is a cloud service for indexing and searching data. However, its use of AI skills to extract insights from multiple formats of data and the ability to integrate it with other AI services, including Azure AI Vision and Azure AI Document Intelligence make it a powerful platform for building digital asset management and knowledge mining solutions.

Indexers, indexes, and skills

At the heart of an Azure AI Search solution is an indexer, which defines a repeatable process to:

1. Ingest data from a source, such as an Azure Storage container of documents or a database.

2. Crack documents to extract their contents - for example, retrieving the text and image data in a PDF document.

3. Apply a sequence of tasks to retrieve information from the data and generate a hierarchy of fields for the index. Some fields are core attributes of the source data (for example document file names and last saved dates), while others are generated by using AI skills. For example:

   • Using Azure AI Vision services to generate tags and captions for images.
   • Using Azure AI Language services to derive fields for sentiment or named entities.
   • Using Azure AI Document Intelligence to extract field values from forms.

4. Persisting the extracted fields as an index.

   

The resulting index can be used to enable users to search for information in the extracted fields based on keywords and filtering criteria.

Persisting extracted data to a knowledge store

As well as creating a searchable index, Azure AI Search can persist the extracted data assets to a knowledge store in Azure Storage.

The indexer can save the following kinds of asset in a knowledge store:

• Tables of field values.

• Images extracted from documents.

• JSON documents representing data structures; which can be complex hierarchies of fields and values.