ChatBot Microsoft Bot framework facebook
▌Introduction
After we have
a bot, we would like to analyze the messages from user and reply to the users
with meaningful messages.
Here we will
integrate Microsoft Cognitive Service: Language
Understanding Intelligent Service (LUIS) to support understanding the human messages.
▋Related articles
▌Environment
▋MS Bot
Framework v3.0
▋Visual
Studio 2017
▋Azure
Cognitive service
▌Microsoft Cognitive
Service : LUIS
▋Buy key
After create the Cognitive service on Azure, we can get
two keys on it. Copy one of them for later using.
▋Register LUIS App
PS. If you don’t have one, click [Buy key on Azure].
Create a new App.
Complete the form and choose the right key.
▋Set Intent and Entity
Here comes the funniest step!
We are going to create Intent (What scenario of the
message) and Entity (What type of words belong to) for our LUIS App.
The following easy sample will help you understand how to
use Intents and Entities.
▋Entities
I added three custom
entities, Behavior, Something and Count.
▋Intents
After adding the Intent, we will add Utterances within it.
For example, “I want to eat three apples”
Now click on the word to set the mapping entity
on it.
Final result …
Keep creating more Utterances in order to train LUIS
later, such as
“I want to have 4 pies”
“I want to watch 1 movie”
…
Save it when the entities are set.
▋Train and Test
Go to [Train & Test], train our LUIS App with the
previous settings on Intent and Entities.
We will do a test after the training is done.
Send a message “I want to buy two hotdogs”, and we find
that LUIS returns the Top scoring intent as Scenario1.
The result about Intent matches our expectation!
How about the Entities? Did they match as well?
Okay, if you want to see the detail, which is JSON
format, of what LUIS returns, we have to publish
our LUIS App.
Go to [Publish App] and publish it. Then back to [Train
& Test] and make another test with [Enable
published model].
This time the Published
version results are included, and we can see the returned JSON by clicking Raw JSON view.
In the JSON, the entities are parsed correctly.
But if the entities are not correct or empty, that means
we have to create more Utterances for training the LUIS model.
Another test way is using the endpoint url.
We completed the LUIS settings, we are going to update
our messaging endpoint codes to support LUIS.
▌Implement
▋Install packages
▋Authentication
▋ID & Key factory
public static class LuisAppFactory
{
static
LuisAppFactory()
{
AppId
= ConfigurationManager.AppSettings["LuisAppId"];
AppKey = ConfigurationManager.AppSettings["LuisAppKey"];
MinScore = 0;
}
public static string AppId { get; private set; }
public static string AppKey { get; private set; }
}
|
▋Web.config
<appSettings>
<add key="MicrosoftAppId" value="" />
<add key="MicrosoftAppPassword" value="" />
<add key="LuisAppId" value="XXXXXX" />
<add key="LuisAppKey" value="YYYYY" />
</appSettings>
|
▋Get LUIS result
▋Call LUIS Api and get LUIS result
private async Task<LuisResult>
getLuisResult(Activity activity)
{
//Create LUIS Client
var lc = new
Microsoft.Cognitive.LUIS.LuisClient(LuisAppFactory.AppId, LuisAppFactory.AppKey);
//Call Luis API
var ret = await
lc.Predict(activity.Text);
return ret; //回傳得分最高的Intent
}
|
▋Web API - ApiController
[HttpPost]
[Route("api/Msg/Hook/")]
public async Task<HttpResponseMessage> Hook([FromBody]Activity activity)
{
var connector = new ConnectorClient(new Uri(activity.ServiceUrl));
var reply =
activity.CreateReply();
if (activity.Type == ActivityTypes.Message)
{
var replyMsg = string.Empty;
//Get the LUIS result
var ret = await this.getLuisResult(activity);
//Get the Intent with top score
Intent topIntent =
ret.Intents.OrderByDescending(x => x.Score).Take(1).FirstOrDefault();
//Set reply message
replyMsg
= ReplyMsgFactory.CreateIntentReplyMsg(ret, topIntent);
reply
= activity.CreateReply(replyMsg);
await
connector.Conversations.ReplyToActivityAsync(reply);
var response = new HttpResponseMessage(HttpStatusCode.OK);
return response;
}
}
|
The above codes will send the client’s message to LUIS
Api and get the response JSON with Intents and Entities.
In the next step, we will use the TOP Intent to decide
what message should Bot returns.
▋Reply strategy
The following reply strategy will reply the total cost
when an user want to buy something.
public static string
CreateIntentReplyMsg(LuisResult ret, Intent topIntent)
{
//Convert Entities to IDictionary
IDictionary<string, string> entities =
new Dictionary<string, string>();
foreach (var item in
ret.Entities) //Entities
{
item.Value.Each(ent =>
{
entities.Add(new KeyValuePair<string, string>(ent.Name, ent.Value));
});
}
string behavior =
entities.Where(x => x.Key.Equals("Behavior", StringComparison.OrdinalIgnoreCase)).FirstOrDefault().Value;
string count =
entities.Where(x => x.Key.Equals("Count", StringComparison.OrdinalIgnoreCase)).FirstOrDefault().Value;
string something =
entities.Where(x => x.Key.Equals("Something", StringComparison.OrdinalIgnoreCase)).FirstOrDefault().Value;
//The follwoing strategy is just for test,
//Create your own strategy here
int cnt = int.Parse(count);
if (behavior == "buy" && (something == "apple" || something == "apples"))
{
var sum = cnt *
50;
return $"The {something} cost ${sum.ToString()}!";
}
else {
return $"Sorry! Dunno!";
}
}
|
▋Test
▌Summary
This tutorial shows the basic concept and usage of LUIS.
The frameworks are easy to use, what matters is what
service you can provide and the reply precision. So the unit test and scenario
test are very important for developing a ChatBot business.
Have fun talking to you Bot!
