chore: chat bot

This commit is contained in:
mahyargdz
2025-06-08 17:01:33 +03:30
parent a457bb276e
commit 173e8675c3
30 changed files with 7184 additions and 1681 deletions
@@ -0,0 +1,389 @@
import { Document } from "@langchain/core/documents";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
import { ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings } from "@langchain/google-genai";
import { EntityManager } from "@mikro-orm/core";
import { Injectable, Logger, OnModuleInit } from "@nestjs/common";
import { ConfigService } from "@nestjs/config";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { Company } from "../../companies/entities/company.entity";
import { Industry } from "../../industries/entities/industry.entity";
import { CHATBOT_CONSTANTS } from "../constants/chatbot.constants";
import { IChatContext, IChatbotResponse } from "../interfaces/chatbot.interface";
@Injectable()
export class LangChainService implements OnModuleInit {
private readonly logger = new Logger(LangChainService.name);
private embeddings: GoogleGenerativeAIEmbeddings;
private llm: ChatGoogleGenerativeAI;
private vectorStore: MemoryVectorStore;
private textSplitter: RecursiveCharacterTextSplitter;
private isInitialized = false;
constructor(
private em: EntityManager,
private configService: ConfigService,
) {
// Initialize Google Gemini components
this.embeddings = new GoogleGenerativeAIEmbeddings({
apiKey: this.configService.getOrThrow("GEMINI_API_KEY"),
modelName: "embedding-001", // Google's embedding model
});
this.llm = new ChatGoogleGenerativeAI({
apiKey: this.configService.getOrThrow("GEMINI_API_KEY"),
model: CHATBOT_CONSTANTS.DEFAULT_MODEL,
temperature: CHATBOT_CONSTANTS.DEFAULT_TEMPERATURE,
maxOutputTokens: CHATBOT_CONSTANTS.DEFAULT_MAX_TOKENS,
});
this.textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000,
chunkOverlap: 200,
separators: ["\n\n", "\n", ".", "!", "?", "؟", "!", ".", " ", ""],
});
}
async onModuleInit() {
await this.initializeVectorStore();
}
private async initializeVectorStore() {
try {
this.logger.log(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.LOADING_DATA);
// Load company and industry data for training
const documents = await this.loadCompanyAndIndustryDocuments();
if (documents.length === 0) {
this.logger.warn(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.NO_DATA_FOUND);
this.vectorStore = new MemoryVectorStore(this.embeddings);
this.isInitialized = true;
return;
}
// Split documents into chunks
const splitDocs = await this.textSplitter.splitDocuments(documents);
this.logger.log(
CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.DOCUMENTS_SPLIT.replace("{totalDocs}", documents.length.toString()).replace(
"{chunks}",
splitDocs.length.toString(),
),
);
// Create vector store from documents
this.vectorStore = await MemoryVectorStore.fromDocuments(splitDocs, this.embeddings);
this.isInitialized = true;
this.logger.log(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.VECTOR_STORE_READY);
} catch (error) {
this.logger.error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.VECTOR_STORE_ERROR, error);
// Fallback to empty vector store
this.vectorStore = new MemoryVectorStore(this.embeddings);
this.isInitialized = true;
}
}
private async loadCompanyAndIndustryDocuments(): Promise<Document[]> {
const documents: Document[] = [];
const em = this.em.fork();
try {
// Load companies data with their products and services
const companies = await em.find(Company, { isActive: true, deletedAt: null }, { populate: ["industry", "business", "products", "services"] });
companies.forEach((company) => {
// Company basic information using template
const companyContent = this.replaceTemplate(CHATBOT_CONSTANTS.COMPANY_DATA_TEMPLATES.COMPANY_INFO, {
name: company.name,
ceo: company.chiefExecutiveOfficer,
email: company.email,
phone: company.phone,
registrationNumber: company.identificationNumber,
establishmentDate: new Date(company.dateOfEstablishment).toLocaleDateString("fa-IR"),
address: company.address,
website: company.websiteUrl || CHATBOT_CONSTANTS.DEFAULT_VALUES.NO_WEBSITE,
description: company.description,
industry: company.industry?.title || CHATBOT_CONSTANTS.DEFAULT_VALUES.UNSPECIFIED,
status: company.status,
business: company.business?.name || CHATBOT_CONSTANTS.DEFAULT_VALUES.UNSPECIFIED,
});
documents.push(
new Document({
pageContent: companyContent,
metadata: {
type: "company",
id: company.id,
name: company.name,
industry: company.industry?.title,
businessId: company.business?.id,
status: company.status,
},
}),
);
// Company products using template
if (company.products && company.products.getItems().length > 0) {
company.products.getItems().forEach((product) => {
const productContent = this.replaceTemplate(CHATBOT_CONSTANTS.COMPANY_DATA_TEMPLATES.COMPANY_PRODUCT, {
companyName: company.name,
productTitle: product.title,
industry: company.industry?.title || CHATBOT_CONSTANTS.DEFAULT_VALUES.UNSPECIFIED,
companyDescription: company.description,
companyAddress: company.address,
companyPhone: company.phone,
companyEmail: company.email,
});
documents.push(
new Document({
pageContent: productContent,
metadata: {
type: "product",
id: product.id,
title: product.title,
companyId: company.id,
companyName: company.name,
industry: company.industry?.title,
},
}),
);
});
}
// Company services using template
if (company.services && company.services.getItems().length > 0) {
company.services.getItems().forEach((service) => {
const serviceContent = this.replaceTemplate(CHATBOT_CONSTANTS.COMPANY_DATA_TEMPLATES.COMPANY_SERVICE, {
companyName: company.name,
serviceTitle: service.title,
industry: company.industry?.title || CHATBOT_CONSTANTS.DEFAULT_VALUES.UNSPECIFIED,
companyDescription: company.description,
companyAddress: company.address,
companyPhone: company.phone,
companyEmail: company.email,
});
documents.push(
new Document({
pageContent: serviceContent,
metadata: {
type: "service",
id: service.id,
title: service.title,
companyId: company.id,
companyName: company.name,
industry: company.industry?.title,
},
}),
);
});
}
});
// Load industries data using template
const industries = await em.find(Industry, { isActive: true, deletedAt: null }, { populate: ["companies", "business"] });
industries.forEach((industry) => {
const companiesInIndustry = industry.companies?.getItems().filter((c) => c.isActive && !c.deletedAt) || [];
const industryContent = this.replaceTemplate(CHATBOT_CONSTANTS.COMPANY_DATA_TEMPLATES.INDUSTRY_INFO, {
title: industry.title,
status: industry.isActive ? CHATBOT_CONSTANTS.DEFAULT_VALUES.ACTIVE : CHATBOT_CONSTANTS.DEFAULT_VALUES.INACTIVE,
companiesCount: companiesInIndustry.length.toString(),
business: industry.business?.name || CHATBOT_CONSTANTS.DEFAULT_VALUES.UNSPECIFIED,
companiesList: companiesInIndustry.map((company) => `- ${company.name}`).join("\n"),
});
documents.push(
new Document({
pageContent: industryContent,
metadata: {
type: "industry",
id: industry.id,
title: industry.title,
isActive: industry.isActive,
companiesCount: companiesInIndustry.length,
businessId: industry.business?.id,
},
}),
);
});
// Add comprehensive Farsi guidance documents from constants
const companyGuidanceDocuments = [
new Document({
pageContent: CHATBOT_CONSTANTS.COMPANY_GUIDANCE_DOCUMENTS.COMPANY_GUIDE,
metadata: {
type: "company_guide",
category: "guidance",
language: "فارسی",
},
}),
new Document({
pageContent: CHATBOT_CONSTANTS.COMPANY_GUIDANCE_DOCUMENTS.COMPANY_FAQ,
metadata: {
type: "company_faq",
category: "support",
language: "فارسی",
},
}),
new Document({
pageContent: CHATBOT_CONSTANTS.COMPANY_GUIDANCE_DOCUMENTS.SEARCH_GUIDE,
metadata: {
type: "search_guide",
category: "tutorial",
language: "فارسی",
},
}),
new Document({
pageContent: CHATBOT_CONSTANTS.COMPANY_GUIDANCE_DOCUMENTS.INDUSTRY_GUIDE,
metadata: {
type: "industry_guide",
category: "education",
language: "فارسی",
},
}),
];
documents.push(...companyGuidanceDocuments);
this.logger.log(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.DOCUMENTS_LOADED.replace("{count}", documents.length.toString()));
return documents;
} catch (error) {
this.logger.error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.LOADING_ERROR, error);
return [];
}
}
async generateResponse(message: string, context: IChatContext): Promise<IChatbotResponse> {
if (!this.isInitialized) {
throw new Error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.SERVICE_NOT_INITIALIZED);
}
try {
// Retrieve relevant documents based on the user's query
const relevantDocs = await this.vectorStore.similaritySearch(message, 5);
// Build context from retrieved documents
const retrievedContext = relevantDocs.map((doc, index) => `${index + 1}. ${doc.pageContent}`).join("\n\n");
// Build conversation history
const conversationHistory =
context.conversationHistory
?.slice(-CHATBOT_CONSTANTS.MAX_CONVERSATION_HISTORY / 4)
?.map((msg) => {
const roleLabel = msg.type === "user" ? "کاربر" : msg.type === "bot" ? "ربات" : "سیستم";
return `${roleLabel}: ${msg.content}`;
})
?.join("\n") || "";
// Create the prompt template using constants
const promptTemplate = PromptTemplate.fromTemplate(CHATBOT_CONSTANTS.COMPANY_GUIDANCE_SYSTEM_PROMPT);
// Create the runnable sequence
const chain = RunnableSequence.from([promptTemplate, this.llm, new StringOutputParser()]);
// Execute the chain
const response = await chain.invoke({
context: retrievedContext,
conversation_history: conversationHistory,
question: message,
});
// Calculate approximate token usage
const tokensUsed = this.estimateTokens(retrievedContext) + this.estimateTokens(message) + this.estimateTokens(response);
return {
message: response,
confidence: 0.9, // Higher confidence for company guidance
sources: relevantDocs.map((doc) => {
const type = doc.metadata.type || "شرکت";
return CHATBOT_CONSTANTS.SOURCE_TYPE_LABELS[type as keyof typeof CHATBOT_CONSTANTS.SOURCE_TYPE_LABELS] || type;
}),
tokensUsed,
context: {
model: `${CHATBOT_CONSTANTS.DEFAULT_MODEL}-company-guide`,
relevantDataFound: relevantDocs.length > 0,
documentsRetrieved: relevantDocs.length,
vectorStoreInitialized: this.isInitialized,
language: "فارسی",
dataTypes: relevantDocs.map((doc) => doc.metadata.type).filter((type, index, arr) => arr.indexOf(type) === index),
focus: "company_and_industry_guidance",
},
};
} catch (error) {
this.logger.error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.RESPONSE_ERROR, error);
throw new Error(CHATBOT_CONSTANTS.ERROR_MESSAGES.LLM_SERVICE_ERROR);
}
}
async generateStreamResponse(message: string, context: IChatContext) {
if (!this.isInitialized) {
throw new Error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.SERVICE_NOT_INITIALIZED);
}
try {
// Retrieve relevant documents
const relevantDocs = await this.vectorStore.similaritySearch(message, 5);
const retrievedContext = relevantDocs.map((doc, index) => `${index + 1}. ${doc.pageContent}`).join("\n\n");
// Build conversation history
const conversationHistory =
context.conversationHistory
?.slice(-CHATBOT_CONSTANTS.MAX_CONVERSATION_HISTORY / 4)
?.map((msg) => {
const roleLabel = msg.type === "user" ? "کاربر" : msg.type === "bot" ? "ربات" : "سیستم";
return `${roleLabel}: ${msg.content}`;
})
?.join("\n") || "";
// Create prompt template using constants
const promptTemplate = PromptTemplate.fromTemplate(CHATBOT_CONSTANTS.COMPANY_GUIDANCE_SYSTEM_PROMPT);
// Create streaming chain
const chain = RunnableSequence.from([promptTemplate, this.llm, new StringOutputParser()]);
// Return async iterable for streaming
const stream = await chain.stream({
context: retrievedContext,
conversation_history: conversationHistory,
question: message,
});
return stream;
} catch (error) {
this.logger.error(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.STREAM_RESPONSE_ERROR, error);
throw new Error(CHATBOT_CONSTANTS.ERROR_MESSAGES.LLM_SERVICE_ERROR);
}
}
async refreshVectorStore(): Promise<void> {
this.logger.log(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.REFRESHING_VECTOR_STORE);
await this.initializeVectorStore();
}
private estimateTokens(text: string): number {
// Rough estimation: 1 token ≈ 4 characters for most languages
return Math.ceil(text.length / 4);
}
/**
* Replace placeholders in template with actual values
*/
private replaceTemplate(template: string, values: Record<string, string>): string {
let result = template;
Object.entries(values).forEach(([key, value]) => {
result = result.replace(new RegExp(`{${key}}`, "g"), value);
});
return result;
}
get initialized(): boolean {
return this.isInitialized;
}
}