72 lines
1.5 KiB
TypeScript
72 lines
1.5 KiB
TypeScript
// @ts-ignore
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import { containerBootstrap } from "@nlpjs/core";
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// @ts-ignore
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import { Nlp } from "@nlpjs/nlp";
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// @ts-ignore
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import { NluManager, NluNeural } from "@nlpjs/nlu";
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// @ts-ignore
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import { LangEn } from "@nlpjs/lang-en-min";
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// @ts-ignore
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import { LangZh } from "@nlpjs/lang-zh";
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import fs from "node:fs";
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import * as fflate from "fflate";
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let zh: TrainData = {};
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let en: TrainData = {};
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type TrainData = {
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[key: string]: string[];
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};
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export async function trainIntentionModel() {
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try {
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const dataZH = fs.readFileSync("./lib/nlp/data/zh.json", "utf8");
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const dataEN = fs.readFileSync("./lib/nlp/data/en.json", "utf8");
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zh = JSON.parse(dataZH);
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en = JSON.parse(dataEN);
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} catch (err) {
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console.error(err);
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}
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const container = await containerBootstrap();
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container.use(Nlp);
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container.use(LangEn);
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container.use(LangZh);
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container.use(NluNeural);
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const manager = new NluManager({
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container,
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locales: ["en", "zh"],
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nlu: {
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useNoneFeature: true
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}
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});
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// Adds the utterances and intents for the NLP
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for (const key in zh) {
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for (const value of zh[key]) {
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manager.add("zh", value, key);
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}
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}
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for (const key in en) {
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for (const value of en[key]) {
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manager.add("en", value, key);
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}
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}
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await manager.train();
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const resultModel = manager.toJSON();
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const buf = fflate.strToU8(JSON.stringify(resultModel));
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const gzipped = fflate.gzipSync(buf, {
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filename: "model.json",
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mtime: new Date().getTime()
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});
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fs.writeFileSync("./public/model", Buffer.from(gzipped));
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}
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trainIntentionModel();
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