Learn how to configure Azure AI Document Intelligence to extract key details like case numbers and client names from legal documents stored in Azure Blob storage for seamless search and analysis.
Table of Contents
Question
Your organization, Nutex Inc., has a large repository of historical legal documents stored in Azure Blob storage. To make it easier for users to search and analyze these documents, you decide to build and deploy an Azure AI Document Intelligence custom skill. The goal is to automatically extract key information from the documents, such as case numbers, client names, and dates, and make it searchable.
You have created an Azure function in Azure Function App to host the custom skill. What should you do next?
A. Configure the deployed function.
B. Add the function to the AI Search skillset.
C. Test the function.
D. Publish the function.
Answer
A. Configure the deployed function.
Explanation
After creating the Azure function to host the custom skill, the next crucial step is to configure the deployed function. This involves setting the necessary input and output bindings and specifying the correct Azure Cognitive Search endpoint. Configuration ensures the function is prepared to handle requests from the Cognitive Search indexer and return enriched data.
To build and deploy a custom skillset for Document Intelligence, you would perform the following steps:
- Create an Azure function to host the custom skill.
- Configure the deployed function.
- Publish the function.
- Test the function.
- Add the created function to the skillset.
Adding the function to the AI Search skillset is not the next step in the given scenario. Once a custom skill has been deployed and configured, it needs to be integrated into the Azure Cognitive Search pipeline by adding it to a skillset. The skillset defines how the custom skill processes data and enriches content before indexing it. However, this step is performed after the function has been published and tested.
Publishing the function is not the next step in the given scenario. Publishing a function makes it accessible for use by external applications, including Azure Cognitive Search. You would publish the function after configuring the deployed function.
Testing the function is not the next step in the given scenario. Testing the function ensures that it processes the data correctly and integrates well with Azure Cognitive Search. Testing involves sending sample data to the function and verifying its output. However, testing should occur after the function is fully configured and published.
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