const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { v4: uuidv4 } = require("uuid");
const {
  writeResponseChunk,
  clientAbortedHandler,
} = require("../../helpers/chat/responses");
const fs = require("fs");
const path = require("path");
const { safeJsonParse } = require("../../http");
const cacheFolder = path.resolve(
  process.env.STORAGE_DIR
    ? path.resolve(process.env.STORAGE_DIR, "models", "openrouter")
    : path.resolve(__dirname, `../../../storage/models/openrouter`)
);

class OpenRouterLLM {
  constructor(embedder = null, modelPreference = null) {
    if (!process.env.OPENROUTER_API_KEY)
      throw new Error("No OpenRouter API key was set.");

    const { OpenAI: OpenAIApi } = require("openai");
    this.basePath = "https://openrouter.ai/api/v1";
    this.openai = new OpenAIApi({
      baseURL: this.basePath,
      apiKey: process.env.OPENROUTER_API_KEY ?? null,
      defaultHeaders: {
        "HTTP-Referer": "https://useanything.com",
        "X-Title": "AnythingLLM",
      },
    });
    this.model =
      modelPreference || process.env.OPENROUTER_MODEL_PREF || "openrouter/auto";
    this.limits = {
      history: this.promptWindowLimit() * 0.15,
      system: this.promptWindowLimit() * 0.15,
      user: this.promptWindowLimit() * 0.7,
    };

    this.embedder = embedder ?? new NativeEmbedder();
    this.defaultTemp = 0.7;

    if (!fs.existsSync(cacheFolder))
      fs.mkdirSync(cacheFolder, { recursive: true });
    this.cacheModelPath = path.resolve(cacheFolder, "models.json");
    this.cacheAtPath = path.resolve(cacheFolder, ".cached_at");
  }

  log(text, ...args) {
    console.log(`\x1b[36m[${this.constructor.name}]\x1b[0m ${text}`, ...args);
  }

  // This checks if the .cached_at file has a timestamp that is more than 1Week (in millis)
  // from the current date. If it is, then we will refetch the API so that all the models are up
  // to date.
  #cacheIsStale() {
    const MAX_STALE = 6.048e8; // 1 Week in MS
    if (!fs.existsSync(this.cacheAtPath)) return true;
    const now = Number(new Date());
    const timestampMs = Number(fs.readFileSync(this.cacheAtPath));
    return now - timestampMs > MAX_STALE;
  }

  // The OpenRouter model API has a lot of models, so we cache this locally in the directory
  // as if the cache directory JSON file is stale or does not exist we will fetch from API and store it.
  // This might slow down the first request, but we need the proper token context window
  // for each model and this is a constructor property - so we can really only get it if this cache exists.
  // We used to have this as a chore, but given there is an API to get the info - this makes little sense.
  async #syncModels() {
    if (fs.existsSync(this.cacheModelPath) && !this.#cacheIsStale())
      return false;

    this.log(
      "Model cache is not present or stale. Fetching from OpenRouter API."
    );
    await fetchOpenRouterModels();
    return;
  }

  #appendContext(contextTexts = []) {
    if (!contextTexts || !contextTexts.length) return "";
    return (
      "\nContext:\n" +
      contextTexts
        .map((text, i) => {
          return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
        })
        .join("")
    );
  }

  models() {
    if (!fs.existsSync(this.cacheModelPath)) return {};
    return safeJsonParse(
      fs.readFileSync(this.cacheModelPath, { encoding: "utf-8" }),
      {}
    );
  }

  streamingEnabled() {
    return "streamGetChatCompletion" in this;
  }

  promptWindowLimit() {
    const availableModels = this.models();
    return availableModels[this.model]?.maxLength || 4096;
  }

  async isValidChatCompletionModel(model = "") {
    await this.#syncModels();
    const availableModels = this.models();
    return availableModels.hasOwnProperty(model);
  }

  constructPrompt({
    systemPrompt = "",
    contextTexts = [],
    chatHistory = [],
    userPrompt = "",
  }) {
    const prompt = {
      role: "system",
      content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
    };
    return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
  }

  async isSafe(_input = "") {
    // Not implemented so must be stubbed
    return { safe: true, reasons: [] };
  }

  async getChatCompletion(messages = null, { temperature = 0.7 }) {
    if (!(await this.isValidChatCompletionModel(this.model)))
      throw new Error(
        `OpenRouter chat: ${this.model} is not valid for chat completion!`
      );

    const result = await this.openai.chat.completions
      .create({
        model: this.model,
        messages,
        temperature,
      })
      .catch((e) => {
        throw new Error(e.message);
      });

    if (!result.hasOwnProperty("choices") || result.choices.length === 0)
      return null;
    return result.choices[0].message.content;
  }

  async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
    if (!(await this.isValidChatCompletionModel(this.model)))
      throw new Error(
        `OpenRouter chat: ${this.model} is not valid for chat completion!`
      );

    const streamRequest = await this.openai.chat.completions.create({
      model: this.model,
      stream: true,
      messages,
      temperature,
    });
    return streamRequest;
  }

  handleStream(response, stream, responseProps) {
    const timeoutThresholdMs = 500;
    const { uuid = uuidv4(), sources = [] } = responseProps;

    return new Promise(async (resolve) => {
      let fullText = "";
      let lastChunkTime = null; // null when first token is still not received.

      // Establish listener to early-abort a streaming response
      // in case things go sideways or the user does not like the response.
      // We preserve the generated text but continue as if chat was completed
      // to preserve previously generated content.
      const handleAbort = () => clientAbortedHandler(resolve, fullText);
      response.on("close", handleAbort);

      // NOTICE: Not all OpenRouter models will return a stop reason
      // which keeps the connection open and so the model never finalizes the stream
      // like the traditional OpenAI response schema does. So in the case the response stream
      // never reaches a formal close state we maintain an interval timer that if we go >=timeoutThresholdMs with
      // no new chunks then we kill the stream and assume it to be complete. OpenRouter is quite fast
      // so this threshold should permit most responses, but we can adjust `timeoutThresholdMs` if
      // we find it is too aggressive.
      const timeoutCheck = setInterval(() => {
        if (lastChunkTime === null) return;

        const now = Number(new Date());
        const diffMs = now - lastChunkTime;
        if (diffMs >= timeoutThresholdMs) {
          console.log(
            `OpenRouter stream did not self-close and has been stale for >${timeoutThresholdMs}ms. Closing response stream.`
          );
          writeResponseChunk(response, {
            uuid,
            sources,
            type: "textResponseChunk",
            textResponse: "",
            close: true,
            error: false,
          });
          clearInterval(timeoutCheck);
          response.removeListener("close", handleAbort);
          resolve(fullText);
        }
      }, 500);

      for await (const chunk of stream) {
        const message = chunk?.choices?.[0];
        const token = message?.delta?.content;
        lastChunkTime = Number(new Date());

        if (token) {
          fullText += token;
          writeResponseChunk(response, {
            uuid,
            sources: [],
            type: "textResponseChunk",
            textResponse: token,
            close: false,
            error: false,
          });
        }

        if (message.finish_reason !== null) {
          writeResponseChunk(response, {
            uuid,
            sources,
            type: "textResponseChunk",
            textResponse: "",
            close: true,
            error: false,
          });
          response.removeListener("close", handleAbort);
          resolve(fullText);
        }
      }
    });
  }

  // Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
  async embedTextInput(textInput) {
    return await this.embedder.embedTextInput(textInput);
  }
  async embedChunks(textChunks = []) {
    return await this.embedder.embedChunks(textChunks);
  }

  async compressMessages(promptArgs = {}, rawHistory = []) {
    const { messageArrayCompressor } = require("../../helpers/chat");
    const messageArray = this.constructPrompt(promptArgs);
    return await messageArrayCompressor(this, messageArray, rawHistory);
  }
}

async function fetchOpenRouterModels() {
  return await fetch(`https://openrouter.ai/api/v1/models`, {
    method: "GET",
    headers: {
      "Content-Type": "application/json",
    },
  })
    .then((res) => res.json())
    .then(({ data = [] }) => {
      const models = {};
      data.forEach((model) => {
        models[model.id] = {
          id: model.id,
          name: model.name,
          organization:
            model.id.split("/")[0].charAt(0).toUpperCase() +
            model.id.split("/")[0].slice(1),
          maxLength: model.context_length,
        };
      });

      // Cache all response information
      if (!fs.existsSync(cacheFolder))
        fs.mkdirSync(cacheFolder, { recursive: true });
      fs.writeFileSync(
        path.resolve(cacheFolder, "models.json"),
        JSON.stringify(models),
        {
          encoding: "utf-8",
        }
      );
      fs.writeFileSync(
        path.resolve(cacheFolder, ".cached_at"),
        String(Number(new Date())),
        {
          encoding: "utf-8",
        }
      );

      return models;
    })
    .catch((e) => {
      console.error(e);
      return {};
    });
}

module.exports = {
  OpenRouterLLM,
  fetchOpenRouterModels,
};