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 { injectable } from "inversify";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";

import { CHATBOT_CONSTANTS } from "../constants/chatbot.constants";
import { IChatContext, IChatbotResponse } from "../interfaces/chatbot.interface";
import { Logger } from "../../../core/logging/logger";

@injectable()
export class LangChainService {
  private readonly logger: Logger;
  private embeddings: GoogleGenerativeAIEmbeddings;
  private llm: ChatGoogleGenerativeAI;
  private vectorStore: MemoryVectorStore;
  private textSplitter: RecursiveCharacterTextSplitter;
  private isInitialized = false;

  constructor() {
    this.logger = new Logger("LangChainService");
    const apiKey = process.env.GEMINI_API_KEY;
    if (!apiKey) {
      throw new Error("GEMINI_API_KEY is required");
    }

    // Initialize Google Gemini components
    this.embeddings = new GoogleGenerativeAIEmbeddings({
      apiKey,
      modelName: "embedding-001", // Google's embedding model
    });

    this.llm = new ChatGoogleGenerativeAI({
      apiKey,
      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", ".", "!", "?", "؟", "!", ".", " ", ""],
    });

    // Initialize vector store asynchronously
    this.initializeVectorStore().catch((error) => {
      this.logger.error("Failed to initialize vector store", error);
    });
  }

  private async initializeVectorStore() {
    try {
      this.logger.info(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.LOADING_DATA);

      // Load documents for training
      // TODO: Load actual data from your database models (Product, Category, etc.)
      const documents = await this.loadDocuments();

      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.info(
        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.info(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 loadDocuments(): Promise<Document[]> {
    const documents: Document[] = [];

    try {
      // TODO: Load actual data from your database
      // Example:
      // const products = await ProductModel.find({ ... }).limit(100);
      // products.forEach((product) => {
      //   documents.push(
      //     new Document({
      //       pageContent: `Product: ${product.title_fa} - Description: ${product.description}`,
      //       metadata: { type: "product", id: product._id.toString() },
      //     }),
      //   );
      // });

      // 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.info(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.info(CHATBOT_CONSTANTS.LANGCHAIN_MESSAGES.REFRESHING_VECTOR_STORE);
    this.isInitialized = false;
    await this.initializeVectorStore();
  }

  private estimateTokens(text: string): number {
    // Rough estimation: 1 token ≈ 4 characters for most languages
    return Math.ceil(text.length / 4);
  }

  get initialized(): boolean {
    return this.isInitialized;
  }
}