LibreChat/api/server/services/ModelService.js

const Keyv = require('keyv');
const axios = require('axios');
const HttpsProxyAgent = require('https-proxy-agent');
const { EModelEndpoint, defaultModels } = require('librechat-data-provider');
const { isEnabled } = require('~/server/utils');
const keyvRedis = require('~/cache/keyvRedis');
const { extractBaseURL } = require('~/utils');
const { logger } = require('~/config');

// const { getAzureCredentials, genAzureChatCompletion } = require('~/utils/');

const { openAIApiKey, userProvidedOpenAI } = require('./Config/EndpointService').config;

const modelsCache = isEnabled(process.env.USE_REDIS)
  ? new Keyv({ store: keyvRedis })
  : new Keyv({ namespace: 'models' });

const {
  OPENROUTER_API_KEY,
  OPENAI_REVERSE_PROXY,
  CHATGPT_MODELS,
  ANTHROPIC_MODELS,
  GOOGLE_MODELS,
  PROXY,
} = process.env ?? {};

/**
 * Fetches OpenAI models from the specified base API path or Azure, based on the provided configuration.
 *
 * @param {Object} params - The parameters for fetching the models.
 * @param {string} params.apiKey - The API key for authentication with the API.
 * @param {string} params.baseURL - The base path URL for the API.
 * @param {string} [params.name='OpenAI'] - The name of the API; defaults to 'OpenAI'.
 * @param {boolean} [params.azure=false] - Whether to fetch models from Azure.
 * @returns {Promise<string[]>} A promise that resolves to an array of model identifiers.
 * @async
 */
const fetchModels = async ({ apiKey, baseURL, name = 'OpenAI', azure = false }) => {
  let models = [];

  if (!baseURL && !azure) {
    return models;
  }

  try {
    const payload = {
      headers: {
        Authorization: `Bearer ${apiKey}`,
      },
    };

    if (PROXY) {
      payload.httpsAgent = new HttpsProxyAgent(PROXY);
    }

    const res = await axios.get(`${baseURL}${azure ? '' : '/models'}`, payload);
    models = res.data.data.map((item) => item.id);
  } catch (err) {
    logger.error(`Failed to fetch models from ${azure ? 'Azure ' : ''}${name} API`, err);
  }

  return models;
};

const fetchOpenAIModels = async (opts = { azure: false, plugins: false }, _models = []) => {
  let models = _models.slice() ?? [];
  let apiKey = openAIApiKey;
  let baseURL = 'https://api.openai.com/v1';
  let reverseProxyUrl = OPENAI_REVERSE_PROXY;
  if (opts.azure) {
    return models;
    // const azure = getAzureCredentials();
    // baseURL = (genAzureChatCompletion(azure))
    //   .split('/deployments')[0]
    //   .concat(`/models?api-version=${azure.azureOpenAIApiVersion}`);
    // apiKey = azureOpenAIApiKey;
  } else if (OPENROUTER_API_KEY) {
    reverseProxyUrl = 'https://openrouter.ai/api/v1';
    apiKey = OPENROUTER_API_KEY;
  }

  if (reverseProxyUrl) {
    baseURL = extractBaseURL(reverseProxyUrl);
  }

  const cachedModels = await modelsCache.get(baseURL);
  if (cachedModels) {
    return cachedModels;
  }

  if (baseURL || opts.azure) {
    models = await fetchModels({
      apiKey,
      baseURL,
      azure: opts.azure,
    });
  }

  if (!reverseProxyUrl) {
    const regex = /(text-davinci-003|gpt-)/;
    models = models.filter((model) => regex.test(model));
  }

  await modelsCache.set(baseURL, models);
  return models;
};

const getOpenAIModels = async (opts = { azure: false, plugins: false }) => {
  let models = [
    'gpt-4',
    'gpt-4-0613',
    'gpt-3.5-turbo',
    'gpt-3.5-turbo-16k',
    'gpt-3.5-turbo-0613',
    'gpt-3.5-turbo-0301',
  ];

  if (!opts.plugins) {
    models.push('text-davinci-003');
  }

  let key;
  if (opts.azure) {
    key = 'AZURE_OPENAI_MODELS';
  } else if (opts.plugins) {
    key = 'PLUGIN_MODELS';
  } else {
    key = 'OPENAI_MODELS';
  }

  if (process.env[key]) {
    models = String(process.env[key]).split(',');
    return models;
  }

  if (userProvidedOpenAI && !OPENROUTER_API_KEY) {
    return models;
  }

  return await fetchOpenAIModels(opts, models);
};

const getChatGPTBrowserModels = () => {
  let models = ['text-davinci-002-render-sha', 'gpt-4'];
  if (CHATGPT_MODELS) {
    models = String(CHATGPT_MODELS).split(',');
  }

  return models;
};

const getAnthropicModels = () => {
  let models = defaultModels[EModelEndpoint.anthropic];
  if (ANTHROPIC_MODELS) {
    models = String(ANTHROPIC_MODELS).split(',');
  }

  return models;
};

const getGoogleModels = () => {
  let models = defaultModels[EModelEndpoint.google];
  if (GOOGLE_MODELS) {
    models = String(GOOGLE_MODELS).split(',');
  }

  return models;
};

module.exports = {
  fetchModels,
  getOpenAIModels,
  getChatGPTBrowserModels,
  getAnthropicModels,
  getGoogleModels,
};