The qna maker service is being retired on the october 31, 2025 (extended from march 31, 2025) A newer version of the question and answering capability is now available as part of azure ai language. Use of the qna program is straightforward Followed by a relevance clause, and click the q/a button for evaluation The qna program can evaluate many queries at the same time It ignores any text not preceded by q:.
It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (kb) of information. The begin update () and endupdate () methods reduce the number of renders in cases where extra rendering can negatively affect performance See also jquery call methods The input file would be a series of relevance queries, one per line, including the q I’m using this method from a scheduled task to run a query and take some actions based on the query output. But potentially you could use the qna.exe to evaluate queries inside of a file, and redirect the output to some other file
To see just how well qna maker is, we can instantly click on the save and train button to train a model on our data Once that finishes we can click on the test button to give the model a test. In this post, app dev manager patrick king explains how to integrate a qna maker knowledgebase into a client application Qna maker is an azure cognitive service that enables you to ask questions and get answers from a knowledgebase built from your own documentation.
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