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Figure 3 displays the LUIS model configured for this scenario. The intents, entities and utterances identified in the previous step can all be created in the LUIS application. Use the Intents menu to add the intents and the Entities menu to create the entities.

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Note that there are two special kinds of entities:. Now, select the New utterances tab and create utterances for each of the conversations identified previously, and label them. For each utterance, identify or label the intent and the associated entities. Repeat this step to add other variations, such as when the policy type is Vehicle, Home or General.

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Also, consider variations where the same request could be made in a different way. This step should be repeated whenever new utterances are labeled or changes are made to existing ones. The publish dialog in the application also lets you test the model to ensure it works as expected. Figure 5 shows how to quickly test the LUIS model by passing in a conversation phrase and viewing the output in the browser.

For convenience, Figure 5 shows the snapshots from three different invocation of utterances and the corresponding results side by side.

Notice that the intent recommended by LUIS has the maximum probability value assigned to it, and appears first in the list. The entities that were extracted from the phrase are also returned in the response.

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Now that the LUIS model is ready, it can be used in a bot application. Download the Visual Studio Template from aka.

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Visit bit. The project created when using this template is a variant of an MVC project. The entry point to the project is MessageController. For each of the intents implemented in the LUIS model, a corresponding method is implemented that can be called directly when the bot application is running.

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The LuisIntent attribute is used on the method that provides the implementation when that intent is encountered in the user request. Figure 6 depicts how this is implemented in code.

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At this point, the implementation in each of the methods can be simple; I can simply return a message identifying which LuisIntent implementation was invoked. In MessageController. Run the bot application, launch the Bot Framework Channel Emulator, which can be downloaded from aka. Type in the utterances you trained the LUIS Model for earlier, and ensure that the response message indicates that the correct method mapped to the LUIS intent was invoked.

The SearchService. Search commands for the different scenarios are built using the Lucene query syntax, which is implemented in the PolicyInfoDialog. Here are some examples:. Figure 7 shows how to implement both property-based search and full-text search for documents. Note that you may want to refer to my previous article in order to relate to the property names and values in the Figure 7 code. Also, you can download the schema of all the tables used in the database accompanying this article using the links provided in later sections. You can format the text in the response message returned by the bot for style, to add hyperlinks or media content and so forth.

This helps make the bot application more user-friendly. For better experience we are moving to mobile autentication. Get your number verified.

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