Quick Start

Install the library

At the folder where is your node project, install the basic library, that will install the core and basic plugins for working in backend.

npm i @nlpjs/basic

Create the code

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/01.basic Then you can create a file index.js with this content:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  const dock = await dockStart({ use: ['Basic']});
  const nlp = dock.get('nlp');
  nlp.addLanguage('en');
  // Adds the utterances and intents for the NLP
  nlp.addDocument('en', 'goodbye for now', 'greetings.bye');
  nlp.addDocument('en', 'bye bye take care', 'greetings.bye');
  nlp.addDocument('en', 'okay see you later', 'greetings.bye');
  nlp.addDocument('en', 'bye for now', 'greetings.bye');
  nlp.addDocument('en', 'i must go', 'greetings.bye');
  nlp.addDocument('en', 'hello', 'greetings.hello');
  nlp.addDocument('en', 'hi', 'greetings.hello');
  nlp.addDocument('en', 'howdy', 'greetings.hello');
  
  // Train also the NLG
  nlp.addAnswer('en', 'greetings.bye', 'Till next time');
  nlp.addAnswer('en', 'greetings.bye', 'see you soon!');
  nlp.addAnswer('en', 'greetings.hello', 'Hey there!');
  nlp.addAnswer('en', 'greetings.hello', 'Greetings!');  await nlp.train();
  const response = await nlp.process('en', 'I should go now');
  console.log(response);
})();

Extracting the corpus into a file

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/02.filecorpus You can put the corpus as json files. The format of the json is:

{
  "name": "Name of the corpus",
  "locale": "en-US",
  "data": [
    {
      "intent": "agent.birthday",
      "utterances": [
        "when is your birthday",
        "when do you celebrate your birthday",
        "when were you born",
        "when do you have birthday",
        "date of your birthday"
      ],
      "answers": [
        "Wait, are you planning a party for me? It's today! My birthday is today!",
        "I'm young. I'm not sure of my birth date",
        "I don't know my birth date. Most virtual agents are young, though, like me."
      ]
    },
    ...
  ]
}

So the new code will be:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  const dock = await dockStart({ use: ['Basic']});
  const nlp = dock.get('nlp');
  await nlp.addCorpus('./corpus-en.json');
  await nlp.train();
  const response = await nlp.process('en', 'Who are you');
  console.log(response);
})();

Extracting the configuration into a file

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/03.config Now we can remove things that are configuration into a file. Add a conf.json file with this content:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json"
      ]
    }
  },
  "use": ["Basic"]
}

And the new code will be:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  const dock = await dockStart();
  const nlp = dock.get('nlp');
  await nlp.train();
  const response = await nlp.process('en', 'Who are you');
  console.log(response);
})();

As you can see now we don't need to provide the plugins to dockStart, neither to add the corpus manually.

Creating your first pipeline

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/04.firstpipeline Now create a pipelines.md file with this content:

# default

## main
nlp.train

And remove the nlp.train() from the code:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  const dock = await dockStart();
  const nlp = dock.get('nlp');
  const response = await nlp.process('en', 'Who are you');
  console.log(response);
})();

We are defining a pipeline called main so it will be executed after loading the configuration and mounting the plugins, so the train process will be executed automatically in the dockStart process.

Adding your first connector

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/05.consoleconnector Now modify the conf.json to use also the plugin ConsoleConnector:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json"
      ]
    }
  },
  "use": ["Basic", "ConsoleConnector"]
}

And at the index.js you will only need the dockStart:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  await dockStart();
})();

Now when you execute you can talk with your bot in the terminal, read the corpus to know what to ask to your bot

Extending your bot with the pipeline

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/06.javascriptpipelines Now we will do two modifications: First one, we want that when our chatbot starts it should write "Say something!" in console, so we can change the pipeline main

# default

## main
nlp.train
console.say "Say something!"

Also, we want that if we write "quit" in console, the process ends. For ending the process, the console plugin has a method called exit. And we want to write the code in the pipeline in javascript, so we add this to the pipelines.md file:

## console.hear
// compiler=javascript
if (message === 'quit') {
  return console.exit();
}
nlp.process();
this.say();

To explain the pipeline better:

The name of the pipeline is console.hear because is the name of the event that the console connector will raise when it hears something. If this event does not exists in the pipeline, then will do default things. The code inside the pipeline will be executed with the input that this method receives.
The line "// compiler=javascript" is telling what compiler to use for the pipelines. The default compiler is very simple, but you can write your pipelines also in javascript or python (adding the corresponding plugin).
"nlp.process()" is calling the process method of the plugin nlp. Notable here: this method returns a promise but you don't need to put the await in the pipelines, the await is automatically done. Also, no arguments are being provided: when the pipeline javascript compiler finds a method where we do not pass any argument, by default it adds the current input of the pipeline.
"this.say()" as the console.hear is executed by the console plugin, this refers to this plugin. As in the "nlp.process" we are not providing arguments, so the input is automatically provided.

The index.js file will be:

const { dockStart } = require('@nlpjs/basic');

(async () => {
  await dockStart();
})();

Adding Multilanguage

You can have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/07.multilanguage Now we want to add a corpus in spanish. First at all we must install the spanish language plugin:

npm i @nlpjs/lang-es

Then add the LangEs plugin in the configuration, and of course the corpus to the corpora:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    }
  },
  "use": ["Basic", "LangEs", "ConsoleConnector"]
}

And add an spanish corpus. In the example, the spanish corpus does not have answers, and the default behaviour of ConsoleConnector is to do an echo. To show the intent and the score in console, we can add set debug to true in the console settings, modifying the configuration:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    },
    "console": {
      "debug": true
    }
  },
  "use": ["Basic", "LangEs", "ConsoleConnector"]
}

Now when you talk with the chatbot you can ask questions from the english corpus or from the spanish corpus. The NLP process automatically will identify the language and send the utterance to the correct trained model.

Adding API and WebChat

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/08.webchat First you will need an Api Server to serve the web. For this you can install the plugin ExpressApiServer that will create an api server using Express.

npm i @nlpjs/express-api-server

The internal name of the plugin is "api-server". Also you will have to configure the plugin to provide the port, and to set that it will serve a bot using webchat (Microsoft Webchat CDN). This should be the content of the conf.json:

{
  "settings": {
    "nlp": {
      "corpora": [
        "../corpora/corpus50en.json",
        "../corpora/corpus50es.json"
      ]
    },
    "api-server": { "port": 3000, "serveBot": true }
  },
  "use": ["Basic", "LangEs", "ConsoleConnector", "ExpressApiServer", "DirectlineConnector"]
}

Now if you start the application, and in your browser navigate to http://localhost:3000, you will see an empty chat saying "impossible to connect".

So lets add the Directline Connector, that will create an API like the Microsoft Directline, but in your local exposed with your API server. To do this install the DirectlineConnector plugin:

npm i @nlpjs/directline-connector

Restart your application and navigate again to http://localhost:3000 and you'll be able to chat with your bot.

Using Microsoft Bot Framework

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/09.microsoftbot There is a Microsoft Bot Framework Connector. First install the library:

npm i @nlpjs/msbf-connector

Then use the plugin adding it in your conf.json:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    },
    "console": {
      "debug": true
    },
    "api-server": {
      "port": 3000,
      "serveBot": true
    }
  },
  "use": ["Basic", "LangEs", "ConsoleConnector", "ExpressApiServer", "DirectlineConnector", "MsbfConnector"]
}

Now start you app and use Microsoft Bot Framework Emulator https://aka.ms/botemulator The endpoint will be http://localhost:3000/default/api/messages

Why is added /default to the api path? Because is the name of the container: NLP.js is built so you can have different container with different names running at the same time in the application, allowing you to build a chatbot exposed to the same channel in different ways, i.e., you can have a chatbot sharing corpus, models, etc. but with different behaviour by channel or by country or any other setting.

If you want the api to be exposed in /api/messages you can set the settings of msfb in the _conf.json:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    },
    "console": {
      "debug": true
    },
    "api-server": {
      "port": 3000,
      "serveBot": true
    },
    "msbf": {
      "apiPath": "",
      "messagesPath": "/api/messages"
    }
  },
  "use": ["Basic", "LangEs", "ConsoleConnector", "ExpressApiServer", "DirectlineConnector", "MsbfConnector"]
}

How to add the Microsoft Application ID and the Microsoft Bot Password? You have 3 different ways:

Providing it at the settings:

    "msbf": {
      "apiPath": "",
      "messagesPath": "/api/messages",
      "appId": "<YOUR MICROSOFT BOT APP ID>",
      "appPassword": "<YOUR MICROSOFT BOT PASSWORD>"
    }

Define the environment variables MSBF_BOT_APP_ID and MSBF_BOT_APP_PASSWORD and will be loaded if there are no appId and appPassword in the plugin settings
You can define those environment variables in a .env file, the .env file is automatically loaded at the dockStart process if exists without installing dotenv.

Recognizing the bot name and the channel

With the last code, try this sentence in console, web and Microsoft emulator: "where am I". You'll notice that the answer is something like: "you're talking from console, app is default channel is console" This happens because the answers to this intents are like that:

      "answers": [
        { "answer": "you're talking from console, app is {{ app }} channel is {{ channel }}", "opts": "channel==='console'" },
        { "answer": "you're talking from directline, app is {{ app }} channel is {{ channel }}", "opts": "channel==='directline'" },
        { "answer": "you're talking from microsoft emulator, app is {{ app }} channel is {{ channel }}", "opts": "channel==='msbf-emulator'" }
      ]

Here we are mixing two things:

The context variables: {{ app }} and {{ channel }} will be replaced by the context variables app (bot name) and channel (channel name).
Opts: the opts of an answer are the conditions to return this answer, and can be any condition in javascript format.

One bot per connector

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/10.threebots

In the conf.json what you defined until here was the configuration of the default container. But containers can have child containers, with their own configuration and plugins, but they can access the plugins and resources of their parent containers. Put this in your conf.json

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    },
    "api-server": {
      "port": 3000,
      "serveBot": true
    }
  },
  "childs": {
    "bot1": {
      "settings": {
        "console": {
          "debug": true
        },
        "use": ["ConsoleConnector"]
      }
    },
    "bot2": {
      "use": ["DirectlineConnector"]
    },
    "bot3": {
      "settings": {
        "msbf": {
          "apiPath": "",
          "messagesPath": "/api/messages"
        }
      },
      "use": ["MsbfConnector"]
    }
  },
  "use": ["Basic", "LangEs", "ExpressApiServer"]
}

This will create 3 child containers where each container represents a bot: bot1 in Console, bot2 in Webchat and Directline and bot3 with the Microsoft Bot Framework Connector. As the ExpressApiServer plugin and configuration are in the default container, then bot2 and bot3 use this express configuration.

The problem now is that when you execute the app it crash because in the pipelines we're trying to use the console plugin from default container, but this plugin is at bot1. So replace the pipelines.md with this content:

# default

## main
nlp.train

# bot1

# main
console.say "Say something!"

## console.hear
// compiler=javascript
if (message === 'quit') {
  return console.exit();
}
nlp.process();
this.say();

As you can see the main title of the pipelines is the name of the container, that way if you use the same plugin in two different containers, they can behave diferently.

Now you can repeat the "where am I" utterance, and you will notice something different: app is no longer default and is replaced with bot1, bot2 or bot3.

Different port for Microsoft Bot Framework and Webchat

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/11.differentports

In the last example the ExpressApiServer plugin is at the default container, but we can move it into bot2 and bot3 with a different configuration:

{
  "settings": {
    "nlp": {
      "corpora": [
        "./corpus-en.json",
        "./corpus-es.json"
      ]
    }
  },
  "childs": {
    "bot1": {
      "settings": {
        "console": {
          "debug": true
        },
        "use": ["ConsoleConnector"]
      }
    },
    "bot2": {
      "settings": {
        "api-server": {
          "port": 3000,
          "serveBot": true
        }
      },
      "use": ["ExpressApiServer", "DirectlineConnector"]
    },
    "bot3": {
      "settings": {
        "msbf": {
          "apiPath": "",
          "messagesPath": "/api/messages"
        },
        "api-server": {
          "port": 4000,
          "serveBot": false
        }
      },
      "use": ["ExpressApiServer", "MsbfConnector"]
    }
  },
  "use": ["Basic", "LangEs"]
}

Important! The plugins are loaded in order. As DirectlineConnector and MsbfConnector needs an api-server, then the ExpressApiServer should be before those.

Adding logic to an intent

You have the code of this example here: https://github.com/jesus-seijas-sp/nlpjs-examples/tree/master/01.quickstart/12.onintent

Supose that you want to have an intent for telling jokes about Chuck Norris, and you know that a service that returns random Chuck Norris jokes exists: http://api.icndb.com/jokes/random

First you need to add the intent to the corpus:

    {
      "intent": "joke.chucknorris",
      "utterances": [
        "tell me a chuck norris fact",
        "tell me a joke about chuck norris",
        "say a chuck norris joke",
        "some chuck norris fact"
      ]
    },

Then add this to the pipelines.md at default section:

## onIntent(joke.chucknorris)
// compiler=javascript
const something = request.get('http://api.icndb.com/jokes/random');
if (something && something.value && something.value.joke) {
  input.answer = something.value.joke;
}

Explanation: the onIntent() is called when an intent is recognized, so you can react doing things and modifying the input as you want, from classifications, entities and of course the answer. We set the compilar to javascript. As we have a plugin request that is for do requests, then we call the API to retrieve an answer, and set it into the input.

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quickstart.md

quickstart.md

Quick Start

Install the library

Create the code

Extracting the corpus into a file

Extracting the configuration into a file

Creating your first pipeline

Adding your first connector

Extending your bot with the pipeline

Adding Multilanguage

Adding API and WebChat

Using Microsoft Bot Framework

Recognizing the bot name and the channel

One bot per connector

Different port for Microsoft Bot Framework and Webchat

Adding logic to an intent

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quickstart.md

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Quick Start

Install the library

Create the code

Extracting the corpus into a file

Extracting the configuration into a file

Creating your first pipeline

Adding your first connector

Extending your bot with the pipeline

Adding Multilanguage

Adding API and WebChat

Using Microsoft Bot Framework

Recognizing the bot name and the channel

One bot per connector

Different port for Microsoft Bot Framework and Webchat

Adding logic to an intent