new(basic.gblib): GPT replacing ALLEN NLP Reading Comp.
This commit is contained in:
Rodrigo Rodriguez
parent
4d8061db60
commit
ff6adacf9b
4 changed files with 375 additions and 259 deletions
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@ -309,6 +309,21 @@ export class GBDeployer implements IGBDeployer {
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return await this.core.saveInstance(instance);
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}
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public async loadOrCreateEmptyVectorStore(min: GBMinInstance): Promise<HNSWLib> {
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let vectorStore: HNSWLib;
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try {
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vectorStore = await HNSWLib.load(min['vectorStorePath'], new OpenAIEmbeddings({ maxConcurrency: 5 }));
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} catch {
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vectorStore = new HNSWLib(new OpenAIEmbeddings({ maxConcurrency: 5 }), {
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space: 'cosine',
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numDimensions: 1536,
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});
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}
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return vectorStore;
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}
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/**
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* Performs the NLP publishing process on remote service.
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*/
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@ -312,6 +312,10 @@ export class GBMinService {
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if (!Fs.existsSync(dir)) {
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mkdirp.sync(dir);
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}
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dir = `work/${gbai}/${botId}.gbkb/docs-vectorized`;
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if (!Fs.existsSync(dir)) {
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mkdirp.sync(dir);
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}
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dir = `work/${gbai}/${botId}.gbdialog`;
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if (!Fs.existsSync(dir)) {
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mkdirp.sync(dir);
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@ -746,6 +750,9 @@ export class GBMinService {
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min.sandBoxMap = {};
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min['scheduleMap'] = {};
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min['conversationWelcomed'] = {};
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min['vectorStore']= await this.deployer.loadOrCreateEmptyVectorStore(min);
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const gbkbPath = DialogKeywords.getGBAIPath(min.botId, 'gbkb');
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min['vectorStorePath']= Path.join('work', gbkbPath, 'docs-vectorized');
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min.packages = sysPackages;
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// NLP Manager.
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@ -31,7 +31,7 @@
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'use strict';
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import { GBMinInstance } from 'botlib';
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//import OpenAI from "openai";
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import OpenAI from "openai";
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import { ChatGPTAPIBrowser, getOpenAIAuth } from 'chatgpt'
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import { CollectionUtil } from 'pragmatismo-io-framework';
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import { DialogKeywords } from '../../basic.gblib/services/DialogKeywords.js';
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@ -69,15 +69,15 @@ export class ChatServices {
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// Calls Model.
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// const openai = new OpenAI({
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// apiKey: key
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// });
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// const chatCompletion = await openai.chat.completions.create({
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// model: "gpt-3.5-turbo",
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// messages: [{ role: "user", content: text }],
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// functions: functions
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// });
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// return chatCompletion.choices[0].message.content;
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const openai = new OpenAI({
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apiKey: key
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});
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const chatCompletion = await openai.chat.completions.create({
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model: "gpt-3.5-turbo",
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messages: [{ role: "user", content: text }],
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functions: functions
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});
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return chatCompletion.choices[0].message.content;
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}
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@ -35,13 +35,24 @@
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import Path from 'path';
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import Fs from 'fs';
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import urlJoin from 'url-join';
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import path from 'path';
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import asyncPromise from 'async-promises';
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import walkPromise from 'walk-promise';
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import { SearchClient } from '@azure/search-documents';
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import Excel from 'exceljs';
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import getSlug from 'speakingurl';
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import { GBServer } from '../../../src/app.js';
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import { HNSWLib } from 'langchain/vectorstores/hnswlib';
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import { JSONLoader } from 'langchain/document_loaders/fs/json';
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import { TextLoader } from 'langchain/document_loaders/fs/text';
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import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
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import { DocxLoader } from 'langchain/document_loaders/fs/docx';
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import { EPubLoader } from 'langchain/document_loaders/fs/epub';
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import { CSVLoader } from 'langchain/document_loaders/fs/csv';
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import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
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import { Document } from 'langchain/document';
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import path from 'path';
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import { YoutubeTranscript } from 'youtube-transcript';
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import {
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GBDialogStep,
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GBLog,
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@ -257,7 +268,7 @@ export class KBService implements IGBKBService {
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}
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public async ask(
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instance: IGBInstance,
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min: GBMinInstance,
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query: string,
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searchScore: number,
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subjects: GuaribasSubject[]
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@ -272,6 +283,8 @@ export class KBService implements IGBKBService {
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query = query.replace('\\', ' ');
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query = query.replace('\r\n', ' ');
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const instance = min.instance;
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// Try simple search first.
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const data = await this.getAnswerByText(instance.instanceId, query.trim());
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@ -287,10 +300,10 @@ export class KBService implements IGBKBService {
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}
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}
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const key = instance.searchKey ? instance.searchKey:
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const key = instance.searchKey ? instance.searchKey :
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GBServer.globals.minBoot.instance.searchKey;
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const host = instance.searchHost ? instance.searchHost :
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GBServer.globals.minBoot.instance.searchHost;
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GBServer.globals.minBoot.instance.searchHost;
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// No direct match found, so Search is used.
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@ -310,7 +323,7 @@ export class KBService implements IGBKBService {
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key: key
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} as any);
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const results = await client.search(query.substring(0,499), {
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const results = await client.search(query.substring(0, 499), {
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filter: `instanceId eq ${instance.instanceId} and skipIndex eq false`,
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searchFields: ['content', 'subject1', 'subject2', 'subject3', 'subject4'],
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select: ['instanceId', 'questionId', 'answerId'],
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@ -341,12 +354,17 @@ export class KBService implements IGBKBService {
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return { answer: undefined, questionId: 0 };
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}
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} else {
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GBLog.info(
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`SEARCH called but returned LOW level score,
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returnedScore: ${returnedScore} < required (searchScore): ${searchScore}`
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);
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return { answer: undefined, questionId: 0 };
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return await this.answerByGPT(min,
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query,
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searchScore,
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subjects
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);
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}
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}
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@ -354,6 +372,44 @@ export class KBService implements IGBKBService {
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}
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}
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private async getRelevantContext(
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vectorStore: HNSWLib,
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sanitizedQuestion: string,
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numDocuments: number
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): Promise<string> {
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const documents = await vectorStore.similaritySearch(sanitizedQuestion, numDocuments);
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return documents
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.map((doc) => doc.pageContent)
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.join(', ')
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.trim()
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.replaceAll('\n', ' ');
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}
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public async answerByGPT(min: GBMinInstance,
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query: string,
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searchScore: number,
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subjects: GuaribasSubject[]
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) {
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const contextVectorStore = min['VectorStore'];
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const question = query.trim().replaceAll('\n', ' ');
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const context = await this.getRelevantContext(contextVectorStore, question, 1);
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const response = await chain.call({
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input: question,
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context,
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history: '',
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immediate_history: '',
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});
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if (response) {
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return { answer: response.response, questionId: 0 };
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}
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return { answer: undefined, questionId: 0 };
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}
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public async getSubjectItems(instanceId: number, parentId: number): Promise<GuaribasSubject[]> {
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const where = { parentSubjectId: parentId, instanceId: instanceId };
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@ -830,183 +886,221 @@ export class KBService implements IGBKBService {
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await CollectionUtil.asyncForEach(files, async file => {
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let content = null;
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let filePath = Path.join(file.root, file.name);
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if (file !== null) {
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if (file.name.endsWith('.docx')) {
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content = await this.getTextFromFile(filePath);
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} else if (file.name.endsWith('.pdf')) {
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const read = await pdf(Fs.readFileSync(filePath));
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content = read.text;
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}
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}
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if (content) {
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content = await min.conversationalService.translate(min, content, 'en');
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await GuaribasAnswer.create(<GuaribasAnswer>{
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instanceId: instance.instanceId,
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content: content,
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format: '.docx',
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media: file.name,
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packageId: packageId
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});
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}
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const document = await this.loadAndSplitFile(filePath);
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const flattenedDocuments = document.reduce((acc, val) => acc.concat(val), []);
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const vectorStore = min['vectorStore'];
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await vectorStore.addDocuments(flattenedDocuments);
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await vectorStore.save(min['vectorStorePath']);
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});
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}
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}
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public async importKbTabularDirectory(localPath: string, min: GBMinInstance, packageId: number): Promise<any> {
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const files = await walkPromise(localPath);
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defaultRecursiveCharacterTextSplitter = new RecursiveCharacterTextSplitter({
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chunkSize: 1024,
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chunkOverlap: 1024,
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});
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await CollectionUtil.asyncForEach(files, async file => {
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if (file !== null && file.name.endsWith('.xlsx')) {
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return await this.importKbTabularFile(urlJoin(file.root, file.name), min, packageId);
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}
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});
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markdownRecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter.fromLanguage('markdown', {
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chunkSize: 1024,
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chunkOverlap: 1024,
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});
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private async loadAndSplitFile(filePath: string): Promise<Document<Record<string, unknown>>[]> {
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const fileExtension = path.extname(filePath);
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let loader;
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let documents: Document<Record<string, unknown>>[];
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switch (fileExtension) {
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case '.json':
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loader = new JSONLoader(filePath);
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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case '.txt':
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loader = new TextLoader(filePath);
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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case '.md':
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loader = new TextLoader(filePath);
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documents = await loader.loadAndSplit(this.markdownRecursiveCharacterTextSplitter);
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break;
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case '.pdf':
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loader = new PDFLoader(filePath, { splitPages: false });
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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case '.docx':
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loader = new DocxLoader(filePath);
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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case '.csv':
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loader = new CSVLoader(filePath);
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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case '.epub':
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loader = new EPubLoader(filePath, { splitChapters: false });
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documents = await loader.loadAndSplit(this.defaultRecursiveCharacterTextSplitter);
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break;
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default:
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throw new Error(`Unsupported file extension: ${fileExtension}`);
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}
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return documents;
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}
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public async importKbTabularDirectory(localPath: string, min: GBMinInstance, packageId: number): Promise < any > {
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const files = await walkPromise(localPath);
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await CollectionUtil.asyncForEach(files, async file => {
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if (file !== null && file.name.endsWith('.xlsx')) {
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return await this.importKbTabularFile(urlJoin(file.root, file.name), min, packageId);
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}
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});
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}
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public async importSubjectFile(
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packageId: number,
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filename: string,
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menuFile: string,
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instance: IGBInstance
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): Promise<any> {
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let subjectsLoaded;
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if (menuFile) {
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// Loads menu.xlsx and finds worksheet.
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packageId: number,
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filename: string,
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menuFile: string,
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instance: IGBInstance
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): Promise < any > {
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let subjectsLoaded;
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if(menuFile) {
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// Loads menu.xlsx and finds worksheet.
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const workbook = new Excel.Workbook();
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const data = await workbook.xlsx.readFile(menuFile);
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let worksheet: any;
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for (let t = 0; t < data.worksheets.length; t++) {
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worksheet = data.worksheets[t];
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if (worksheet) {
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const workbook = new Excel.Workbook();
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const data = await workbook.xlsx.readFile(menuFile);
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let worksheet: any;
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for (let t = 0; t < data.worksheets.length; t++) {
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worksheet = data.worksheets[t];
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if (worksheet) {
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break;
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}
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}
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const MAX_LEVEL = 4; // Max column level to reach menu items in plan.
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// Iterates over all items.
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let rows = worksheet._rows;
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rows.length = 24;
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let lastLevel = 0;
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let subjects = { children: [] };
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let childrenNode = subjects.children;
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let activeObj = null;
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let activeChildrenGivenLevel = [childrenNode];
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await asyncPromise.eachSeries(rows, async row => {
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if (!row) return;
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let menu;
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// Detect menu level by skipping blank cells on left.
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let level;
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for (level = 0; level < MAX_LEVEL; level++) {
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const cell = row._cells[level];
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if (cell && cell.text) {
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menu = cell.text;
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break;
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}
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}
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const MAX_LEVEL = 4; // Max column level to reach menu items in plan.
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// Iterates over all items.
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// Tree hierarchy calculation.
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let rows = worksheet._rows;
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rows.length = 24;
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let lastLevel = 0;
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let subjects = { children: [] };
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let childrenNode = subjects.children;
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let activeObj = null;
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if (level > lastLevel) {
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childrenNode = activeObj.children;
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} else if (level < lastLevel) {
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childrenNode = activeChildrenGivenLevel[level];
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}
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let activeChildrenGivenLevel = [childrenNode];
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/// Keeps the record of last subroots for each level, to
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// changel levels greater than one (return to main menu),
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// can exists between leaf nodes and roots.
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await asyncPromise.eachSeries(rows, async row => {
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if (!row) return;
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let menu;
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activeChildrenGivenLevel[level] = childrenNode;
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// Detect menu level by skipping blank cells on left.
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// Insert the object into JSON.
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const description = row._cells[level + 1] ? row._cells[level + 1].text : null;
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activeObj = {
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title: menu,
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description: description,
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id: menu,
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children: []
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};
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activeChildrenGivenLevel[level].push(activeObj);
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let level;
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for (level = 0; level < MAX_LEVEL; level++) {
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const cell = row._cells[level];
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if (cell && cell.text) {
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menu = cell.text;
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break;
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}
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}
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lastLevel = level;
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});
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// Tree hierarchy calculation.
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if (level > lastLevel) {
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childrenNode = activeObj.children;
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} else if (level < lastLevel) {
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childrenNode = activeChildrenGivenLevel[level];
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}
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/// Keeps the record of last subroots for each level, to
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// changel levels greater than one (return to main menu),
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// can exists between leaf nodes and roots.
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activeChildrenGivenLevel[level] = childrenNode;
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// Insert the object into JSON.
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const description = row._cells[level + 1] ? row._cells[level + 1].text : null;
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activeObj = {
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title: menu,
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description: description,
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id: menu,
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children: []
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};
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activeChildrenGivenLevel[level].push(activeObj);
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lastLevel = level;
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});
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subjectsLoaded = subjects;
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} else {
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subjectsLoaded = JSON.parse(Fs.readFileSync(filename, 'utf8'));
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}
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subjectsLoaded = subjects;
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} else {
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subjectsLoaded = JSON.parse(Fs.readFileSync(filename, 'utf8'));
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}
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const doIt = async (subjects: GuaribasSubject[], parentSubjectId: number) => {
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return asyncPromise.eachSeries(subjects, async item => {
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const value = await GuaribasSubject.create(<GuaribasSubject>{
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internalId: item.id,
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parentSubjectId: parentSubjectId,
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instanceId: instance.instanceId,
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from: item.from,
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to: item.to,
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title: item.title,
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description: item.description,
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packageId: packageId
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});
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if (item.children) {
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return doIt(item.children, value.subjectId);
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} else {
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return item;
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}
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return asyncPromise.eachSeries(subjects, async item => {
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const value = await GuaribasSubject.create(<GuaribasSubject>{
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internalId: item.id,
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parentSubjectId: parentSubjectId,
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instanceId: instance.instanceId,
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from: item.from,
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to: item.to,
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title: item.title,
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description: item.description,
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packageId: packageId
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});
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};
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|
||||
return doIt(subjectsLoaded.children, undefined);
|
||||
}
|
||||
|
||||
public async undeployKbFromStorage(instance: IGBInstance, deployer: GBDeployer, packageId: number) {
|
||||
await GuaribasQuestion.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await GuaribasAnswer.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await GuaribasSubject.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await this.undeployPackageFromStorage(instance, packageId);
|
||||
}
|
||||
|
||||
public static async RefreshNER(min: GBMinInstance) {
|
||||
const questions = await KBService.getQuestionsNER(min.instance.instanceId);
|
||||
const contentLocale = min.core.getParam<string>(
|
||||
min.instance,
|
||||
'Default Content Language',
|
||||
GBConfigService.get('DEFAULT_CONTENT_LANGUAGE')
|
||||
);
|
||||
|
||||
await CollectionUtil.asyncForEach(questions, async question => {
|
||||
const text = question.content;
|
||||
|
||||
const categoryReg = /.*\((.*)\).*/gi.exec(text);
|
||||
const nameReg = /(\w+)\(.*\).*/gi.exec(text);
|
||||
|
||||
if (categoryReg) {
|
||||
let category = categoryReg[1];
|
||||
|
||||
if (category === 'number') {
|
||||
min['nerEngine'].addRegexEntity('number', 'pt', '/d+/gi');
|
||||
}
|
||||
if (nameReg) {
|
||||
let name = nameReg[1];
|
||||
|
||||
min['nerEngine'].addNamedEntityText(category, name, [contentLocale], [name]);
|
||||
}
|
||||
if (item.children) {
|
||||
return doIt(item.children, value.subjectId);
|
||||
} else {
|
||||
return item;
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
return doIt(subjectsLoaded.children, undefined);
|
||||
}
|
||||
|
||||
public async undeployKbFromStorage(instance: IGBInstance, deployer: GBDeployer, packageId: number) {
|
||||
await GuaribasQuestion.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await GuaribasAnswer.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await GuaribasSubject.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
await this.undeployPackageFromStorage(instance, packageId);
|
||||
}
|
||||
|
||||
public static async RefreshNER(min: GBMinInstance) {
|
||||
const questions = await KBService.getQuestionsNER(min.instance.instanceId);
|
||||
const contentLocale = min.core.getParam<string>(
|
||||
min.instance,
|
||||
'Default Content Language',
|
||||
GBConfigService.get('DEFAULT_CONTENT_LANGUAGE')
|
||||
);
|
||||
|
||||
await CollectionUtil.asyncForEach(questions, async question => {
|
||||
const text = question.content;
|
||||
|
||||
const categoryReg = /.*\((.*)\).*/gi.exec(text);
|
||||
const nameReg = /(\w+)\(.*\).*/gi.exec(text);
|
||||
|
||||
if (categoryReg) {
|
||||
let category = categoryReg[1];
|
||||
|
||||
if (category === 'number') {
|
||||
min['nerEngine'].addRegexEntity('number', 'pt', '/d+/gi');
|
||||
}
|
||||
if (nameReg) {
|
||||
let name = nameReg[1];
|
||||
|
||||
min['nerEngine'].addNamedEntityText(category, name, [contentLocale], [name]);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Deploys a knowledge base to the storage using the .gbkb format.
|
||||
|
|
@ -1014,103 +1108,103 @@ export class KBService implements IGBKBService {
|
|||
* @param localPath Path to the .gbkb folder.
|
||||
*/
|
||||
public async deployKb(core: IGBCoreService, deployer: GBDeployer, localPath: string, min: GBMinInstance) {
|
||||
const packageName = Path.basename(localPath);
|
||||
const instance = await core.loadInstanceByBotId(min.botId);
|
||||
GBLog.info(`[GBDeployer] Importing: ${localPath}`);
|
||||
const packageName = Path.basename(localPath);
|
||||
const instance = await core.loadInstanceByBotId(min.botId);
|
||||
GBLog.info(`[GBDeployer] Importing: ${localPath}`);
|
||||
|
||||
const p = await deployer.deployPackageToStorage(instance.instanceId, packageName);
|
||||
await this.importKbPackage(min, localPath, p, instance);
|
||||
GBDeployer.mountGBKBAssets(packageName, min.botId, localPath);
|
||||
const service = await AzureDeployerService.createInstance(deployer);
|
||||
const searchIndex = instance.searchIndex ? instance.searchIndex : GBServer.globals.minBoot.instance.searchIndex;
|
||||
await deployer.rebuildIndex(instance, service.getKBSearchSchema(searchIndex));
|
||||
const p = await deployer.deployPackageToStorage(instance.instanceId, packageName);
|
||||
await this.importKbPackage(min, localPath, p, instance);
|
||||
GBDeployer.mountGBKBAssets(packageName, min.botId, localPath);
|
||||
const service = await AzureDeployerService.createInstance(deployer);
|
||||
const searchIndex = instance.searchIndex ? instance.searchIndex : GBServer.globals.minBoot.instance.searchIndex;
|
||||
await deployer.rebuildIndex(instance, service.getKBSearchSchema(searchIndex));
|
||||
|
||||
min['groupCache'] = await KBService.getGroupReplies(instance.instanceId);
|
||||
await KBService.RefreshNER(min);
|
||||
min['groupCache'] = await KBService.getGroupReplies(instance.instanceId);
|
||||
await KBService.RefreshNER(min);
|
||||
|
||||
GBLog.info(`[GBDeployer] Start Bot Server Side Rendering... ${localPath}`);
|
||||
const html = await GBSSR.getHTML(min);
|
||||
let path = DialogKeywords.getGBAIPath(min.botId, `gbui`);
|
||||
path = Path.join(process.env.PWD, 'work', path, 'index.html');
|
||||
GBLogEx.info(min, `[GBDeployer] Saving SSR HTML in ${path}.`);
|
||||
Fs.writeFileSync(path, html, 'utf8');
|
||||
GBLog.info(`[GBDeployer] Start Bot Server Side Rendering... ${localPath}`);
|
||||
const html = await GBSSR.getHTML(min);
|
||||
let path = DialogKeywords.getGBAIPath(min.botId, `gbui`);
|
||||
path = Path.join(process.env.PWD, 'work', path, 'index.html');
|
||||
GBLogEx.info(min, `[GBDeployer] Saving SSR HTML in ${path}.`);
|
||||
Fs.writeFileSync(path, html, 'utf8');
|
||||
|
||||
GBLog.info(`[GBDeployer] Finished import of ${localPath}`);
|
||||
}
|
||||
GBLog.info(`[GBDeployer] Finished import of ${localPath}`);
|
||||
}
|
||||
|
||||
private async playAudio(
|
||||
min: GBMinInstance,
|
||||
answer: GuaribasAnswer,
|
||||
channel: string,
|
||||
step: GBDialogStep,
|
||||
conversationalService: IGBConversationalService
|
||||
) {
|
||||
conversationalService.sendAudio(min, step, answer.content);
|
||||
}
|
||||
min: GBMinInstance,
|
||||
answer: GuaribasAnswer,
|
||||
channel: string,
|
||||
step: GBDialogStep,
|
||||
conversationalService: IGBConversationalService
|
||||
) {
|
||||
conversationalService.sendAudio(min, step, answer.content);
|
||||
}
|
||||
|
||||
private async playUrl(
|
||||
min,
|
||||
conversationalService: IGBConversationalService,
|
||||
step: GBDialogStep,
|
||||
url: string,
|
||||
channel: string
|
||||
) {
|
||||
if (channel === 'whatsapp') {
|
||||
await min.conversationalService.sendFile(min, step, null, url, '');
|
||||
} else {
|
||||
await conversationalService.sendEvent(min, step, 'play', {
|
||||
playerType: 'url',
|
||||
data: url
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
private async playVideo(
|
||||
min,
|
||||
conversationalService: IGBConversationalService,
|
||||
step: GBDialogStep,
|
||||
answer: GuaribasAnswer,
|
||||
channel: string
|
||||
) {
|
||||
if (channel === 'whatsapp') {
|
||||
await min.conversationalService.sendFile(min, step, null, answer.content, '');
|
||||
} else {
|
||||
const path = DialogKeywords.getGBAIPath(min.botId, `gbkb`);
|
||||
await conversationalService.sendEvent(min, step, 'play', {
|
||||
playerType: 'video',
|
||||
data: urlJoin(path, 'videos', answer.content)
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
private async undeployPackageFromStorage(instance: any, packageId: number) {
|
||||
await GuaribasPackage.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
}
|
||||
|
||||
public async readComprehension(instanceId: number, doc: string, question: string) {
|
||||
const url =
|
||||
`http://${process.env.GBMODELS_SERVER}/reading-comprehension` +
|
||||
new URLSearchParams({ question: question, key: process.env.GBMODELS_KEY });
|
||||
const form = new FormData();
|
||||
form.append('content', doc);
|
||||
const options = {
|
||||
body: form
|
||||
};
|
||||
GBLog.info(`[General Bots Models]: ReadComprehension for ${question}.`);
|
||||
return await fetch(url, options);
|
||||
}
|
||||
|
||||
private async getTextFromFile(filename: string) {
|
||||
return new Promise<string>(async (resolve, reject) => {
|
||||
textract.fromFileWithPath(filename, { preserveLineBreaks: true }, (error, text) => {
|
||||
if (error) {
|
||||
reject(error);
|
||||
} else {
|
||||
resolve(text);
|
||||
}
|
||||
});
|
||||
min,
|
||||
conversationalService: IGBConversationalService,
|
||||
step: GBDialogStep,
|
||||
url: string,
|
||||
channel: string
|
||||
) {
|
||||
if (channel === 'whatsapp') {
|
||||
await min.conversationalService.sendFile(min, step, null, url, '');
|
||||
} else {
|
||||
await conversationalService.sendEvent(min, step, 'play', {
|
||||
playerType: 'url',
|
||||
data: url
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
private async playVideo(
|
||||
min,
|
||||
conversationalService: IGBConversationalService,
|
||||
step: GBDialogStep,
|
||||
answer: GuaribasAnswer,
|
||||
channel: string
|
||||
) {
|
||||
if (channel === 'whatsapp') {
|
||||
await min.conversationalService.sendFile(min, step, null, answer.content, '');
|
||||
} else {
|
||||
const path = DialogKeywords.getGBAIPath(min.botId, `gbkb`);
|
||||
await conversationalService.sendEvent(min, step, 'play', {
|
||||
playerType: 'video',
|
||||
data: urlJoin(path, 'videos', answer.content)
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
private async undeployPackageFromStorage(instance: any, packageId: number) {
|
||||
await GuaribasPackage.destroy({
|
||||
where: { instanceId: instance.instanceId, packageId: packageId }
|
||||
});
|
||||
}
|
||||
|
||||
public async readComprehension(instanceId: number, doc: string, question: string) {
|
||||
const url =
|
||||
`http://${process.env.GBMODELS_SERVER}/reading-comprehension` +
|
||||
new URLSearchParams({ question: question, key: process.env.GBMODELS_KEY });
|
||||
const form = new FormData();
|
||||
form.append('content', doc);
|
||||
const options = {
|
||||
body: form
|
||||
};
|
||||
GBLog.info(`[General Bots Models]: ReadComprehension for ${question}.`);
|
||||
return await fetch(url, options);
|
||||
}
|
||||
|
||||
private async getTextFromFile(filename: string) {
|
||||
return new Promise<string>(async (resolve, reject) => {
|
||||
textract.fromFileWithPath(filename, { preserveLineBreaks: true }, (error, text) => {
|
||||
if (error) {
|
||||
reject(error);
|
||||
} else {
|
||||
resolve(text);
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue