Create chat bot

[goodwin74]

How can I create a training model for a chat bot?
There are for example questions and answers for the user. How to train the model so that it can answer questions and the answers to one question can be different?

[4cyber]

If we're talking about a simple chatbot, you can use the @nlpjs library with the brain.js neural network.

create a class to initialize the brain.js model

const { CorpusLookup }  = require('@nlpjs/utils');
const { TokenizerEn }   = require('@nlpjs/lang-en');
const { NeuralNetwork } = require('brain.js');
const { StemmerEn }     = require('@nlpjs/lang-en');
const corpus            = require('./data/traindata.json');
const fs                = require("fs");

class BrainTrainer {
    constructor() {
        this.settings = {
            log: (str) => console.log(str),
            logPeriod: 10,
            errorThresh: 0.00005
        };
        this.tokenizer  = new TokenizerEn();
        this.stemmer    = new StemmerEn();
        this.lookups    = new CorpusLookup(corpus, this.stemmer);
        //initialize and import the model
        this.net        = new NeuralNetwork();

        if (fs.existsSync('model.json'))    this.import()
        else                                this.train()
    }
    
    train() {
        this.net.train(this.lookups.trainVectors, this.settings);
        this.export()
        return this.test()
    }

    test() {
        let total = 0;
        let good = 0;
        corpus.data.forEach(item => {
            item.tests.forEach(test => {
                const classifications = this.process(test);
                total += 1;
                if (classifications[0].intent === item.intent) {
                    good += 1;
                }
            });
        });
            
        const perc = good * 100 / total;
        return { good, total, perc } 
    }

    process(utterance) {
        const vector = this.lookups.inputToVector(utterance);
        const output = this.net.run(vector);
        return this.lookups.vectorToClassifications(output);
    }

    export() {
        fs.writeFileSync(
            "model.json",
            JSON.stringify(this.net.toJSON()),
            "utf-8"
        )
    }

    import() {
        this.net.fromJSON(
            JSON.parse(
                fs.readFileSync(
                    "model.json",
                    "utf-8"
                )
            )
        )
    }
}

module.exports = BrainTrainer;

The process return the intent with the score.
initialize your app like this

const BrainTrainer  = require('./brain-trainer');
const NerizedClass  = require("./elab")
const Nerized       = new NerizedClass()
const brain         = new BrainTrainer()

const readline = require('readline').createInterface({
    input: process.stdin,
    output: process.stdout
});

console.clear();

const prompt = async() => {
    readline.question('User> ', async input => {
        const res = brain.process(input.toLowerCase());
        console.log(res[0])
        if (typeof Nerized[res[0].intent] != "undefined") {
            const search = await Nerized[res[0].intent](input.toLowerCase());
            console.log(search)
        } else {
            console.log("Bot> No intent founds.")
        }
        
        prompt();
    });
}

prompt()

you can create some express api to run the process inside the brain-trainer
Make your training data like this

{
    "name": "CorpusFile",
    "locale": "en-EN",
    "data": [
      {
        "id": 1,
        "intent": "hello",
        "utterances": [
          "Hi",
          "Good morning",
          "Hello"
        ],
        "tests": [
          "Hello Bot!",
          "Hey, how are you?",
          "Good morning"
        ]
      }, ecc
    ]
}

If you don't have the model.json trained the class will train based of this file and export that.
If you use an express api you can use like .get(/train... to use the brain.train().
Now you need to create the Ner, to extract the content.
elab.js

const { Ner } = require('@nlpjs/ner');

class Nerized {
    hello() { //this is the same intent name

   }
}

The amazing @nlpjs/ner can extract informations form text.
You can now use the @nlpjs/nlg to generate text or use a return directly in Nerized.
Read the nlpjs documentation to learn more about nlg and ner.

Hope this helps you, from here you can test and explore this amazing class combined to nlpjs.