{"repo": "bukhhh/rita-edge-demo", "files": {"server/utils/helpers/customModels.js": {"ad5cf5d5dcfe953b8b579354701e9a02374615ce": {"t": 1785745547.3611917, "n": 31189, "h": "cbf8da861a169320", "c": "const { fetchOpenRouterModels } = require(\"../AiProviders/openRouter\");\nconst {\n  fetchOpenRouterEmbeddingModels,\n} = require(\"../EmbeddingEngines/openRouter\");\nconst { fetchApiPieModels } = require(\"../AiProviders/apipie\");\nconst { perplexityModels } = require(\"../AiProviders/perplexity\");\nconst { fireworksAiModels } = require(\"../AiProviders/fireworksAi\");\nconst { ElevenLabsTTS } = require(\"../TextToSpeech/elevenLabs\");\nconst { fetchNovitaModels } = require(\"../AiProviders/novita\");\nconst { parseLMStudioBasePath } = require(\"../AiProviders/lmStudio\");\nconst { parseNvidiaNimBasePath } = require(\"../AiProviders/nvidiaNim\");\nconst { fetchPPIOModels } = require(\"../AiProviders/ppio\");\nconst { GeminiLLM } = require(\"../AiProviders/gemini\");\nconst { fetchCometApiModels } = require(\"../AiProviders/cometapi\");\nconst { parseFoundryBasePath } = require(\"../AiProviders/foundry\");\nconst { getDockerModels } = require(\"../AiProviders/dockerModelRunner\");\nconst { getAllLemonadeModels } = require(\"../AiProviders/lemonade\");\n\nconst SUPPORT_CUSTOM_MODELS = [\n  \"openai\",\n  \"anthropic\",\n  \"localai\",\n  \"ollama\",\n  \"togetherai\",\n  \"fireworksai\",\n  \"nvidia-nim\",\n  \"mistral\",\n  \"perplexity\",\n  \"openrouter\",\n  \"lmstudio\",\n  \"koboldcpp\",\n  \"litellm\",\n  \"elevenlabs-tts\",\n  \"groq\",\n  \"deepseek\",\n  \"apipie\",\n  \"novita\",\n  \"cometapi\",\n  \"xai\",\n  \"gemini\",\n  \"ppio\",\n  \"dpais\",\n  \"moonshotai\",\n  \"foundry\",\n  \"cohere\",\n  \"zai\",\n  \"giteeai\",\n  \"docker-model-runner\",\n  \"privatemode\",\n  \"sambanova\",\n  \"lemonade\",\n  \"minimax\",\n  // Embedding Engines\n  \"native-embedder\",\n  \"cohere-embedder\",\n  \"openrouter-embedder\",\n  \"lemonade-embedder\",\n];\n\nasync function getCustomModels(provider = \"\", apiKey = null, basePath = null) {\n  if (!SUPPORT_CUSTOM_MODELS.includes(provider))\n    return { models: [], error: \"Invalid provider for custom models\" };\n\n  switch (provider) {\n    case \"openai\":\n      return await openAiModels(apiKey);\n    case \"anthropic\":\n      return await anthropicModels(apiKey);\n    case \"localai\":\n      return await localAIModels(basePath, apiKey);\n    case \"ollama\":\n      return await ollamaAIModels(basePath, apiKey);\n    case \"togetherai\":\n      return await getTogetherAiModels(apiKey);\n    case \"fireworksai\":\n      return await getFireworksAiModels(apiKey);\n    case \"mistral\":\n      return await getMistralModels(apiKey);\n    case \"perplexity\":\n      return await getPerplexityModels();\n    case \"openrouter\":\n      return await getOpenRouterModels();\n    case \"lmstudio\":\n      return await getLMStudioModels(basePath, apiKey);\n    case \"koboldcpp\":\n      return await getKoboldCPPModels(basePath);\n    case \"litellm\":\n      return await liteLLMModels(basePath, apiKey);\n    case \"elevenlabs-tts\":\n      return await getElevenLabsModels(apiKey);\n    case \"groq\":\n      return await getGroqAiModels(apiKey);\n    case \"deepseek\":\n      return await getDeepSeekModels(apiKey);\n    case \"apipie\":\n      return await getAPIPieModels(apiKey);\n    case \"novita\":\n      return await getNovitaModels();\n    case \"cometapi\":\n      return await getCometApiModels();\n    case \"xai\":\n      return await getXAIModels(apiKey);\n    case \"nvidia-nim\":\n      return await getNvidiaNimModels(basePath);\n    case \"gemini\":\n      return await getGeminiModels(apiKey);\n    case \"ppio\":\n      return await getPPIOModels(apiKey);\n    case \"dpais\":\n      return await getDellProAiStudioModels(basePath);\n    case \"moonshotai\":\n      return await getMoonshotAiModels(apiKey);\n    case \"foundry\":\n      return await getFoundryModels(basePath);\n    case \"cohere\":\n      return await getCohereModels(apiKey, \"chat\");\n    case \"zai\":\n      return await getZAiModels(apiKey);\n    case \"native-embedder\":\n      return await getNativeEmbedderModels();\n    case \"cohere-embedder\":\n      return await getCohereModels(apiKey, \"embed\");\n    case \"openrouter-embedder\":\n      return await getOpenRouterEmbeddingModels();\n    case \"giteeai\":\n      return await getGiteeAIModels(apiKey);\n    case \"docker-model-runner\":\n      return await getDockerModelRunnerModels(basePath);\n    case \"privatemode\":\n      return await getPrivatemodeModels(basePath, \"generate\");\n    case \"sambanova\":\n      return await getSambaNovaModels(apiKey);\n    case \"lemonade\":\n      return await getLemonadeModels(basePath);\n    case \"lemonade-embedder\":\n      return await getLemonadeModels(basePath, \"embedding\");\n    case \"minimax\":\n      return await getMinimaxModels(apiKey);\n    default:\n      return { models: [], error: \"Invalid provider for custom models\" };\n  }\n}\n\nasync function openAiModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.OPEN_AI_KEY,\n  });\n  const allModels = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`OpenAI:listModels`, e.message);\n      return [\n        {\n          name: \"gpt-3.5-turbo\",\n          id: \"gpt-3.5-turbo\",\n          object: \"model\",\n          created: 1677610602,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4o\",\n          id: \"gpt-4o\",\n          object: \"model\",\n          created: 1677610602,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4\",\n          id: \"gpt-4\",\n          object: \"model\",\n          created: 1687882411,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4-turbo\",\n          id: \"gpt-4-turbo\",\n          object: \"model\",\n          created: 1712361441,\n          owned_by: \"system\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4-32k\",\n          id: \"gpt-4-32k\",\n          object: \"model\",\n          created: 1687979321,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-3.5-turbo-16k\",\n          id: \"gpt-3.5-turbo-16k\",\n          object: \"model\",\n          created: 1683758102,\n          owned_by: \"openai-internal\",\n          organization: \"OpenAi\",\n        },\n      ];\n    });\n\n  const gpts = allModels\n    .filter(\n      (model) =>\n        (model.id.includes(\"gpt\") && !model.id.startsWith(\"ft:\")) ||\n        model.id.startsWith(\"o\") // o1, o1-mini, o3, etc\n    )\n    .filter(\n      (model) =>\n        !model.id.includes(\"vision\") &&\n        !model.id.includes(\"instruct\") &&\n        !model.id.includes(\"audio\") &&\n        !model.id.includes(\"realtime\") &&\n        !model.id.includes(\"image\") &&\n        !model.id.includes(\"moderation\") &&\n        !model.id.includes(\"transcribe\")\n    )\n    .map((model) => {\n      return {\n        ...model,\n        name: model.id,\n        organization: \"OpenAi\",\n      };\n    });\n\n  const customModels = allModels\n    .filter(\n      (model) =>\n        !model.owned_by.includes(\"openai\") && model.owned_by !== \"system\"\n    )\n    .map((model) => {\n      return {\n        ...model,\n        name: model.id,\n        organization: \"Your Fine-Tunes\",\n      };\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if ((gpts.length > 0 || customModels.length > 0) && !!apiKey)\n    process.env.OPEN_AI_KEY = apiKey;\n  return { models: [...gpts, ...customModels], error: null };\n}\n\nasync function anthropicModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.ANTHROPIC_API_KEY\n      : _apiKey || process.env.ANTHROPIC_API_KEY || null;\n  const AnthropicAI = require(\"@anthropic-ai/sdk\");\n  const anthropic = new AnthropicAI({ apiKey });\n  const models = await anthropic.models\n    .list()\n    .then((results) => results.data)\n    .then((models) => {\n      return models\n        .filter((model) => model.type === \"model\")\n        .map((model) => {\n          return {\n            id: model.id,\n            name: model.display_name,\n          };\n        });\n    })\n    .catch((e) => {\n      console.error(`Anthropic:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.ANTHROPIC_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function localAIModels(basePath = null, apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: basePath || process.env.LOCAL_AI_BASE_PATH,\n    apiKey: apiKey || process.env.LOCAL_AI_API_KEY || null,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`LocalAI:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.LOCAL_AI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getGroqAiModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.GROQ_API_KEY\n      : _apiKey || process.env.GROQ_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.groq.com/openai/v1\",\n    apiKey,\n  });\n  const models = (\n    await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`GroqAi:listModels`, e.message);\n        return [];\n      })\n  ).filter(\n    (model) => !model.id.includes(\"whisper\") && !model.id.includes(\"tool-use\")\n  );\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.GROQ_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function liteLLMModels(basePath = null, apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: basePath || process.env.LITE_LLM_BASE_PATH,\n    apiKey: apiKey || process.env.LITE_LLM_API_KEY || null,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`LiteLLM:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.LITE_LLM_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getLMStudioModels(basePath = null, _apiKey = null) {\n  try {\n    const apiKey =\n      _apiKey === true\n        ? process.env.LMSTUDIO_AUTH_TOKEN\n        : _apiKey || process.env.LMSTUDIO_AUTH_TOKEN || null;\n\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseLMStudioBasePath(\n        basePath || process.env.LMSTUDIO_BASE_PATH\n      ),\n      apiKey: apiKey || null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`LMStudio:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`LMStudio:getLMStudioModels`, e.message);\n    return { models: [], error: \"Could not fetch LMStudio Models\" };\n  }\n}\n\nasync function getKoboldCPPModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: basePath || process.env.KOBOLD_CPP_BASE_PATH,\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`KoboldCPP:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`KoboldCPP:getKoboldCPPModels`, e.message);\n    return { models: [], error: \"Could not fetch KoboldCPP Models\" };\n  }\n}\n\nasync function ollamaAIModels(basePath = null, _authToken = null) {\n  let url;\n  try {\n    let urlPath = basePath ?? process.env.OLLAMA_BASE_PATH;\n    new URL(urlPath);\n    if (urlPath.split(\"\").slice(-1)?.[0] === \"/\")\n      throw new Error(\"BasePath Cannot end in /!\");\n    url = urlPath;\n  } catch {\n    return { models: [], error: \"Not a valid URL.\" };\n  }\n\n  const authToken = _authToken || process.env.OLLAMA_AUTH_TOKEN || null;\n  const headers = authToken ? { Authorization: `Bearer ${authToken}` } : {};\n  const models = await fetch(`${url}/api/tags`, { headers: headers })\n    .then((res) => {\n      if (!res.ok)\n        throw new Error(`Could not reach Ollama server! ${res.status}`);\n      return res.json();\n    })\n    .then((data) => data?.models || [])\n    .then((models) =>\n      models.map((model) => {\n        return { id: model.name };\n      })\n    )\n    .catch((e) => {\n      console.error(e);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!authToken)\n    process.env.OLLAMA_AUTH_TOKEN = authToken;\n  return { models, error: null };\n}\n\nasync function getTogetherAiModels(apiKey = null) {\n  const _apiKey =\n    apiKey === true\n      ? process.env.TOGETHER_AI_API_KEY\n      : apiKey || process.env.TOGETHER_AI_API_KEY || null;\n  try {\n    const { togetherAiModels } = require(\"../AiProviders/togetherAi\");\n    const models = await togetherAiModels(_apiKey);\n    if (models.length > 0 && !!_apiKey)\n      process.env.TOGETHER_AI_API_KEY = _apiKey;\n    return { models, error: null };\n  } catch (error) {\n    console.error(\"Error in getTogetherAiModels:\", error);\n    return { models: [], error: \"Failed to fetch Together AI models\" };\n  }\n}\n\nasync function getFireworksAiModels(apiKey = null) {\n  const knownModels = await fireworksAiModels(apiKey);\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getPerplexityModels() {\n  const knownModels = perplexityModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getOpenRouterModels() {\n  const knownModels = await fetchOpenRouterModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getNovitaModels() {\n  const knownModels = await fetchNovitaModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getCometApiModels() {\n  const knownModels = await fetchCometApiModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getAPIPieModels(apiKey = null) {\n  const knownModels = await fetchApiPieModels(apiKey);\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels)\n    .filter((model) => {\n      // Filter for chat models\n      return (\n        model.subtype &&\n        (model.subtype.includes(\"chat\") || model.subtype.includes(\"chatx\"))\n      );\n    })\n    .map((model) => {\n      return {\n        id: model.id,\n        organization: model.organization,\n        name: model.name,\n      };\n    });\n  return { models, error: null };\n}\n\nasync function getMistralModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.MISTRAL_API_KEY || null,\n    baseURL: \"https://api.mistral.ai/v1\",\n  });\n  const models = await openai.models\n    .list()\n    .then((results) =>\n      results.data.filter((model) => !model.id.includes(\"embed\"))\n    )\n    .catch((e) => {\n      console.error(`Mistral:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.MISTRAL_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getElevenLabsModels(apiKey = null) {\n  const models = (await ElevenLabsTTS.voices(apiKey)).map((model) => {\n    return {\n      id: model.voice_id,\n      organization: model.category,\n      name: model.name,\n    };\n  });\n\n  if (models.length === 0) {\n    return {\n      models: [\n        {\n          id: \"21m00Tcm4TlvDq8ikWAM\",\n          organization: \"premade\",\n          name: \"Rachel (default)\",\n        },\n      ],\n      error: null,\n    };\n  }\n\n  if (models.length > 0 && !!apiKey) process.env.TTS_ELEVEN_LABS_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getMinimaxModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.MINIMAX_API_KEY\n      : _apiKey || process.env.MINIMAX_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.minimax.io/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by || \"minimax\",\n      }))\n    )\n    .catch((e) => {\n      console.error(`Minimax:listModels`, e.message);\n      return [\n        {\n          id: \"MiniMax-M2.7\",\n          name: \"MiniMax-M2.7\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.7-highspeed\",\n          name: \"MiniMax-M2.7-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.5\",\n          name: \"MiniMax-M2.5\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.5-highspeed\",\n          name: \"MiniMax-M2.5-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.1\",\n          name: \"MiniMax-M2.1\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.1-highspeed\",\n          name: \"MiniMax-M2.1-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2\",\n          name: \"MiniMax-M2\",\n          organization: \"minimax\",\n        },\n      ];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.MINIMAX_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getDeepSeekModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.DEEPSEEK_API_KEY,\n    baseURL: \"https://api.deepseek.com/v1\",\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by,\n      }))\n    )\n    .catch((e) => {\n      console.error(`DeepSeek:listModels`, e.message);\n      return [\n        {\n          id: \"deepseek-chat\",\n          name: \"deepseek-chat\",\n          organization: \"deepseek\",\n        },\n        {\n          id: \"deepseek-reasoner\",\n          name: \"deepseek-reasoner\",\n          organization: \"deepseek\",\n        },\n      ];\n    });\n\n  if (models.length > 0 && !!apiKey) process.env.DEEPSEEK_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getGiteeAIModels() {\n  const { giteeAiModels } = require(\"../AiProviders/giteeai\");\n  const modelMap = await giteeAiModels();\n  if (!Object.keys(modelMap).length === 0) return { models: [], error: null };\n  const models = Object.values(modelMap).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization ?? \"GiteeAI\",\n      name: model.id,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getXAIModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.XAI_LLM_API_KEY\n      : _apiKey || process.env.XAI_LLM_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.x.ai/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`XAI:listModels`, e.message);\n      return [\n        {\n          created: 1725148800,\n          id: \"grok-beta\",\n          object: \"model\",\n          owned_by: \"xai\",\n        },\n      ];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.XAI_LLM_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getNvidiaNimModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseNvidiaNimBasePath(\n        basePath ?? process.env.NVIDIA_NIM_LLM_BASE_PATH\n      ),\n      apiKey: null,\n    });\n    const modelResponse = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        throw new Error(e.message);\n      });\n\n    const models = modelResponse.map((model) => {\n      return {\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by,\n      };\n    });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`NVIDIA NIM:getNvidiaNimModels`, e.message);\n    return { models: [], error: \"Could not fetch NVIDIA NIM Models\" };\n  }\n}\n\nasync function getGeminiModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.GEMINI_API_KEY\n      : _apiKey || process.env.GEMINI_API_KEY || null;\n  const models = await GeminiLLM.fetchModels(apiKey);\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.GEMINI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getPPIOModels() {\n  const ppioModels = await fetchPPIOModels();\n  if (!Object.keys(ppioModels).length === 0) return { models: [], error: null };\n  const models = Object.values(ppioModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getDellProAiStudioModels(basePath = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  try {\n    const { origin } = new URL(\n      basePath || process.env.DELL_PRO_AI_STUDIO_BASE_PATH\n    );\n    const openai = new OpenAIApi({\n      baseURL: `${origin}/v1/openai`,\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then((models) => {\n        return models\n          .filter(\n            (model) => model?.capability?.includes(\"TextToText\") // Only include text-to-text models for this handler\n          )\n          .map((model) => {\n            return {\n              id: model.id,\n              name: model.name,\n              organization: model.owned_by,\n            };\n          });\n      })\n      .catch((e) => {\n        throw new Error(e.message);\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`getDellProAiStudioModels`, e.message);\n    return {\n      models: [],\n      error: \"Could not reach Dell Pro Ai Studio from the provided base path\",\n    };\n  }\n}\n\nfunction getNativeEmbedderModels() {\n  const { NativeEmbedder } = require(\"../EmbeddingEngines/native\");\n  return { models: NativeEmbedder.availableModels(), error: null };\n}\n\nasync function getMoonshotAiModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.MOONSHOT_AI_API_KEY\n      : _apiKey || process.env.MOONSHOT_AI_API_KEY || null;\n\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.moonshot.ai/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`MoonshotAi:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0) process.env.MOONSHOT_AI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getFoundryModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseFoundryBasePath(basePath || process.env.FOUNDRY_BASE_PATH),\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) =>\n        results.data.map((model) => ({\n          ...model,\n          name: model.id,\n        }))\n      )\n      .catch((e) => {\n        console.error(`Foundry:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Foundry:getFoundryModels`, e.message);\n    return { models: [], error: \"Could not fetch Foundry Models\" };\n  }\n}\n\n/**\n * Get Cohere models\n * @param {string} _apiKey - The API key to use\n * @param {'chat' | 'embed'} type - The type of model to get\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getCohereModels(_apiKey = null, type = \"chat\") {\n  const apiKey =\n    _apiKey === true\n      ? process.env.COHERE_API_KEY\n      : _apiKey || process.env.COHERE_API_KEY || null;\n\n  const { CohereClient } = require(\"cohere-ai\");\n  const cohere = new CohereClient({\n    token: apiKey,\n  });\n  const models = await cohere.models\n    .list({ pageSize: 1000, endpoint: type })\n    .then((results) => results.models)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.name,\n        name: model.name,\n      }))\n    )\n    .catch((e) => {\n      console.error(`Cohere:listModels`, e.message);\n      return [];\n    });\n\n  return { models, error: null };\n}\n\nasync function getZAiModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.ZAI_API_KEY\n      : _apiKey || process.env.ZAI_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.z.ai/api/paas/v4\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`Z.AI:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.ZAI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getOpenRouterEmbeddingModels() {\n  const knownModels = await fetchOpenRouterEmbeddingModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getDockerModelRunnerModels(basePath = null) {\n  try {\n    const models = await getDockerModels(basePath);\n    return { models, error: null };\n  } catch (e) {\n    console.error(`DockerModelRunner:getDockerModelRunnerModels`, e.message);\n    return {\n      models: [],\n      error: \"Could not fetch Docker Model Runner Models\",\n    };\n  }\n}\n\nasync function getLemonadeModels(basePath = null, task = \"chat\") {\n  try {\n    const models = await getAllLemonadeModels(basePath, task);\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Lemonade:getLemonadeModels`, e.message);\n    return { models: [], error: \"Could not fetch Lemonade Models\" };\n  }\n}\n\n/**\n * Get Privatemode models\n * @param {string} basePath - The base path of the Privatemode endpoint.\n * @param {'any' | 'generate' | 'embed' | 'transcribe'} task - The task to fetch the models for.\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getPrivatemodeModels(basePath = null, task = \"any\") {\n  try {\n    const { PrivatemodeLLM } = require(\"../AiProviders/privatemode\");\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: PrivatemodeLLM.parseBasePath(\n        basePath || process.env.PRIVATEMODE_LLM_BASE_PATH\n      ),\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then(\n        (models) =>\n          models\n            .filter((model) => !model.id.includes(\"/\")) // remove legacy prefixed models\n            .filter((model) =>\n              task === \"any\" ? true : model.tasks.includes(task)\n            ) // filter by task or show all if task is any\n      )\n      .then((models) =>\n        models.map((model) => ({\n          id: model.id,\n          organization: \"Privatemode\",\n          name: model.id\n            .split(\"-\")\n            .map((word) => word.charAt(0).toUpperCase() + word.slice(1))\n            .join(\" \"),\n        }))\n      )\n      .catch((e) => {\n        console.error(`Privatemode:listModels`, e.message);\n        return [];\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Privatemode:getPrivatemodeModels`, e.message);\n    return { models: [], error: \"Could not fetch Privatemode Models\" };\n  }\n}\n\n/**\n * Get SambaNova models\n * @param {string} _apiKey - The API key to use\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getSambaNovaModels(_apiKey = null) {\n  try {\n    const apiKey =\n      _apiKey === true\n        ? process.env.SAMBANOVA_LLM_API_KEY\n        : _apiKey || process.env.SAMBANOVA_LLM_API_KEY || null;\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: \"https://api.sambanova.ai/v1\",\n      apiKey,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then((models) =>\n        models.filter((model) => !model.id.toLowerCase().startsWith(\"whisper\"))\n      )\n      .then((models) =>\n        models.map((model) => {\n          const organization =\n            model.hasOwnProperty(\"owned_by\") &&\n            model.owned_by !== \"no-reply@sambanova.ai\"\n              ? model.owned_by\n              : \"SambaNova\";\n          return {\n            id: model.id,\n            organization,\n            name: model.id,\n          };\n        })\n      )\n      .catch((e) => {\n        console.error(`SambaNova:listModels`, e.message);\n        return [];\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`SambaNova:getSambaNovaModels`, e.message);\n    return { models: [], error: \"Could not fetch SambaNova Models\" };\n  }\n}\n\nmodule.exports = {\n  getCustomModels,\n  SUPPORT_CUSTOM_MODELS,\n};\n"}, "ad2da5a6ae36356e7635ef5fd5503cd3a6d3016c": {"t": 1785754106.7976694, "n": 31189, "h": "cbf8da861a169320", "c": "const { fetchOpenRouterModels } = require(\"../AiProviders/openRouter\");\nconst {\n  fetchOpenRouterEmbeddingModels,\n} = require(\"../EmbeddingEngines/openRouter\");\nconst { fetchApiPieModels } = require(\"../AiProviders/apipie\");\nconst { perplexityModels } = require(\"../AiProviders/perplexity\");\nconst { fireworksAiModels } = require(\"../AiProviders/fireworksAi\");\nconst { ElevenLabsTTS } = require(\"../TextToSpeech/elevenLabs\");\nconst { fetchNovitaModels } = require(\"../AiProviders/novita\");\nconst { parseLMStudioBasePath } = require(\"../AiProviders/lmStudio\");\nconst { parseNvidiaNimBasePath } = require(\"../AiProviders/nvidiaNim\");\nconst { fetchPPIOModels } = require(\"../AiProviders/ppio\");\nconst { GeminiLLM } = require(\"../AiProviders/gemini\");\nconst { fetchCometApiModels } = require(\"../AiProviders/cometapi\");\nconst { parseFoundryBasePath } = require(\"../AiProviders/foundry\");\nconst { getDockerModels } = require(\"../AiProviders/dockerModelRunner\");\nconst { getAllLemonadeModels } = require(\"../AiProviders/lemonade\");\n\nconst SUPPORT_CUSTOM_MODELS = [\n  \"openai\",\n  \"anthropic\",\n  \"localai\",\n  \"ollama\",\n  \"togetherai\",\n  \"fireworksai\",\n  \"nvidia-nim\",\n  \"mistral\",\n  \"perplexity\",\n  \"openrouter\",\n  \"lmstudio\",\n  \"koboldcpp\",\n  \"litellm\",\n  \"elevenlabs-tts\",\n  \"groq\",\n  \"deepseek\",\n  \"apipie\",\n  \"novita\",\n  \"cometapi\",\n  \"xai\",\n  \"gemini\",\n  \"ppio\",\n  \"dpais\",\n  \"moonshotai\",\n  \"foundry\",\n  \"cohere\",\n  \"zai\",\n  \"giteeai\",\n  \"docker-model-runner\",\n  \"privatemode\",\n  \"sambanova\",\n  \"lemonade\",\n  \"minimax\",\n  // Embedding Engines\n  \"native-embedder\",\n  \"cohere-embedder\",\n  \"openrouter-embedder\",\n  \"lemonade-embedder\",\n];\n\nasync function getCustomModels(provider = \"\", apiKey = null, basePath = null) {\n  if (!SUPPORT_CUSTOM_MODELS.includes(provider))\n    return { models: [], error: \"Invalid provider for custom models\" };\n\n  switch (provider) {\n    case \"openai\":\n      return await openAiModels(apiKey);\n    case \"anthropic\":\n      return await anthropicModels(apiKey);\n    case \"localai\":\n      return await localAIModels(basePath, apiKey);\n    case \"ollama\":\n      return await ollamaAIModels(basePath, apiKey);\n    case \"togetherai\":\n      return await getTogetherAiModels(apiKey);\n    case \"fireworksai\":\n      return await getFireworksAiModels(apiKey);\n    case \"mistral\":\n      return await getMistralModels(apiKey);\n    case \"perplexity\":\n      return await getPerplexityModels();\n    case \"openrouter\":\n      return await getOpenRouterModels();\n    case \"lmstudio\":\n      return await getLMStudioModels(basePath, apiKey);\n    case \"koboldcpp\":\n      return await getKoboldCPPModels(basePath);\n    case \"litellm\":\n      return await liteLLMModels(basePath, apiKey);\n    case \"elevenlabs-tts\":\n      return await getElevenLabsModels(apiKey);\n    case \"groq\":\n      return await getGroqAiModels(apiKey);\n    case \"deepseek\":\n      return await getDeepSeekModels(apiKey);\n    case \"apipie\":\n      return await getAPIPieModels(apiKey);\n    case \"novita\":\n      return await getNovitaModels();\n    case \"cometapi\":\n      return await getCometApiModels();\n    case \"xai\":\n      return await getXAIModels(apiKey);\n    case \"nvidia-nim\":\n      return await getNvidiaNimModels(basePath);\n    case \"gemini\":\n      return await getGeminiModels(apiKey);\n    case \"ppio\":\n      return await getPPIOModels(apiKey);\n    case \"dpais\":\n      return await getDellProAiStudioModels(basePath);\n    case \"moonshotai\":\n      return await getMoonshotAiModels(apiKey);\n    case \"foundry\":\n      return await getFoundryModels(basePath);\n    case \"cohere\":\n      return await getCohereModels(apiKey, \"chat\");\n    case \"zai\":\n      return await getZAiModels(apiKey);\n    case \"native-embedder\":\n      return await getNativeEmbedderModels();\n    case \"cohere-embedder\":\n      return await getCohereModels(apiKey, \"embed\");\n    case \"openrouter-embedder\":\n      return await getOpenRouterEmbeddingModels();\n    case \"giteeai\":\n      return await getGiteeAIModels(apiKey);\n    case \"docker-model-runner\":\n      return await getDockerModelRunnerModels(basePath);\n    case \"privatemode\":\n      return await getPrivatemodeModels(basePath, \"generate\");\n    case \"sambanova\":\n      return await getSambaNovaModels(apiKey);\n    case \"lemonade\":\n      return await getLemonadeModels(basePath);\n    case \"lemonade-embedder\":\n      return await getLemonadeModels(basePath, \"embedding\");\n    case \"minimax\":\n      return await getMinimaxModels(apiKey);\n    default:\n      return { models: [], error: \"Invalid provider for custom models\" };\n  }\n}\n\nasync function openAiModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.OPEN_AI_KEY,\n  });\n  const allModels = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`OpenAI:listModels`, e.message);\n      return [\n        {\n          name: \"gpt-3.5-turbo\",\n          id: \"gpt-3.5-turbo\",\n          object: \"model\",\n          created: 1677610602,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4o\",\n          id: \"gpt-4o\",\n          object: \"model\",\n          created: 1677610602,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4\",\n          id: \"gpt-4\",\n          object: \"model\",\n          created: 1687882411,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4-turbo\",\n          id: \"gpt-4-turbo\",\n          object: \"model\",\n          created: 1712361441,\n          owned_by: \"system\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-4-32k\",\n          id: \"gpt-4-32k\",\n          object: \"model\",\n          created: 1687979321,\n          owned_by: \"openai\",\n          organization: \"OpenAi\",\n        },\n        {\n          name: \"gpt-3.5-turbo-16k\",\n          id: \"gpt-3.5-turbo-16k\",\n          object: \"model\",\n          created: 1683758102,\n          owned_by: \"openai-internal\",\n          organization: \"OpenAi\",\n        },\n      ];\n    });\n\n  const gpts = allModels\n    .filter(\n      (model) =>\n        (model.id.includes(\"gpt\") && !model.id.startsWith(\"ft:\")) ||\n        model.id.startsWith(\"o\") // o1, o1-mini, o3, etc\n    )\n    .filter(\n      (model) =>\n        !model.id.includes(\"vision\") &&\n        !model.id.includes(\"instruct\") &&\n        !model.id.includes(\"audio\") &&\n        !model.id.includes(\"realtime\") &&\n        !model.id.includes(\"image\") &&\n        !model.id.includes(\"moderation\") &&\n        !model.id.includes(\"transcribe\")\n    )\n    .map((model) => {\n      return {\n        ...model,\n        name: model.id,\n        organization: \"OpenAi\",\n      };\n    });\n\n  const customModels = allModels\n    .filter(\n      (model) =>\n        !model.owned_by.includes(\"openai\") && model.owned_by !== \"system\"\n    )\n    .map((model) => {\n      return {\n        ...model,\n        name: model.id,\n        organization: \"Your Fine-Tunes\",\n      };\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if ((gpts.length > 0 || customModels.length > 0) && !!apiKey)\n    process.env.OPEN_AI_KEY = apiKey;\n  return { models: [...gpts, ...customModels], error: null };\n}\n\nasync function anthropicModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.ANTHROPIC_API_KEY\n      : _apiKey || process.env.ANTHROPIC_API_KEY || null;\n  const AnthropicAI = require(\"@anthropic-ai/sdk\");\n  const anthropic = new AnthropicAI({ apiKey });\n  const models = await anthropic.models\n    .list()\n    .then((results) => results.data)\n    .then((models) => {\n      return models\n        .filter((model) => model.type === \"model\")\n        .map((model) => {\n          return {\n            id: model.id,\n            name: model.display_name,\n          };\n        });\n    })\n    .catch((e) => {\n      console.error(`Anthropic:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.ANTHROPIC_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function localAIModels(basePath = null, apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: basePath || process.env.LOCAL_AI_BASE_PATH,\n    apiKey: apiKey || process.env.LOCAL_AI_API_KEY || null,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`LocalAI:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.LOCAL_AI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getGroqAiModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.GROQ_API_KEY\n      : _apiKey || process.env.GROQ_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.groq.com/openai/v1\",\n    apiKey,\n  });\n  const models = (\n    await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`GroqAi:listModels`, e.message);\n        return [];\n      })\n  ).filter(\n    (model) => !model.id.includes(\"whisper\") && !model.id.includes(\"tool-use\")\n  );\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.GROQ_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function liteLLMModels(basePath = null, apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: basePath || process.env.LITE_LLM_BASE_PATH,\n    apiKey: apiKey || process.env.LITE_LLM_API_KEY || null,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`LiteLLM:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.LITE_LLM_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getLMStudioModels(basePath = null, _apiKey = null) {\n  try {\n    const apiKey =\n      _apiKey === true\n        ? process.env.LMSTUDIO_AUTH_TOKEN\n        : _apiKey || process.env.LMSTUDIO_AUTH_TOKEN || null;\n\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseLMStudioBasePath(\n        basePath || process.env.LMSTUDIO_BASE_PATH\n      ),\n      apiKey: apiKey || null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`LMStudio:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`LMStudio:getLMStudioModels`, e.message);\n    return { models: [], error: \"Could not fetch LMStudio Models\" };\n  }\n}\n\nasync function getKoboldCPPModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: basePath || process.env.KOBOLD_CPP_BASE_PATH,\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        console.error(`KoboldCPP:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`KoboldCPP:getKoboldCPPModels`, e.message);\n    return { models: [], error: \"Could not fetch KoboldCPP Models\" };\n  }\n}\n\nasync function ollamaAIModels(basePath = null, _authToken = null) {\n  let url;\n  try {\n    let urlPath = basePath ?? process.env.OLLAMA_BASE_PATH;\n    new URL(urlPath);\n    if (urlPath.split(\"\").slice(-1)?.[0] === \"/\")\n      throw new Error(\"BasePath Cannot end in /!\");\n    url = urlPath;\n  } catch {\n    return { models: [], error: \"Not a valid URL.\" };\n  }\n\n  const authToken = _authToken || process.env.OLLAMA_AUTH_TOKEN || null;\n  const headers = authToken ? { Authorization: `Bearer ${authToken}` } : {};\n  const models = await fetch(`${url}/api/tags`, { headers: headers })\n    .then((res) => {\n      if (!res.ok)\n        throw new Error(`Could not reach Ollama server! ${res.status}`);\n      return res.json();\n    })\n    .then((data) => data?.models || [])\n    .then((models) =>\n      models.map((model) => {\n        return { id: model.name };\n      })\n    )\n    .catch((e) => {\n      console.error(e);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!authToken)\n    process.env.OLLAMA_AUTH_TOKEN = authToken;\n  return { models, error: null };\n}\n\nasync function getTogetherAiModels(apiKey = null) {\n  const _apiKey =\n    apiKey === true\n      ? process.env.TOGETHER_AI_API_KEY\n      : apiKey || process.env.TOGETHER_AI_API_KEY || null;\n  try {\n    const { togetherAiModels } = require(\"../AiProviders/togetherAi\");\n    const models = await togetherAiModels(_apiKey);\n    if (models.length > 0 && !!_apiKey)\n      process.env.TOGETHER_AI_API_KEY = _apiKey;\n    return { models, error: null };\n  } catch (error) {\n    console.error(\"Error in getTogetherAiModels:\", error);\n    return { models: [], error: \"Failed to fetch Together AI models\" };\n  }\n}\n\nasync function getFireworksAiModels(apiKey = null) {\n  const knownModels = await fireworksAiModels(apiKey);\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getPerplexityModels() {\n  const knownModels = perplexityModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getOpenRouterModels() {\n  const knownModels = await fetchOpenRouterModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getNovitaModels() {\n  const knownModels = await fetchNovitaModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getCometApiModels() {\n  const knownModels = await fetchCometApiModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getAPIPieModels(apiKey = null) {\n  const knownModels = await fetchApiPieModels(apiKey);\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels)\n    .filter((model) => {\n      // Filter for chat models\n      return (\n        model.subtype &&\n        (model.subtype.includes(\"chat\") || model.subtype.includes(\"chatx\"))\n      );\n    })\n    .map((model) => {\n      return {\n        id: model.id,\n        organization: model.organization,\n        name: model.name,\n      };\n    });\n  return { models, error: null };\n}\n\nasync function getMistralModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.MISTRAL_API_KEY || null,\n    baseURL: \"https://api.mistral.ai/v1\",\n  });\n  const models = await openai.models\n    .list()\n    .then((results) =>\n      results.data.filter((model) => !model.id.includes(\"embed\"))\n    )\n    .catch((e) => {\n      console.error(`Mistral:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.MISTRAL_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getElevenLabsModels(apiKey = null) {\n  const models = (await ElevenLabsTTS.voices(apiKey)).map((model) => {\n    return {\n      id: model.voice_id,\n      organization: model.category,\n      name: model.name,\n    };\n  });\n\n  if (models.length === 0) {\n    return {\n      models: [\n        {\n          id: \"21m00Tcm4TlvDq8ikWAM\",\n          organization: \"premade\",\n          name: \"Rachel (default)\",\n        },\n      ],\n      error: null,\n    };\n  }\n\n  if (models.length > 0 && !!apiKey) process.env.TTS_ELEVEN_LABS_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getMinimaxModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.MINIMAX_API_KEY\n      : _apiKey || process.env.MINIMAX_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.minimax.io/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by || \"minimax\",\n      }))\n    )\n    .catch((e) => {\n      console.error(`Minimax:listModels`, e.message);\n      return [\n        {\n          id: \"MiniMax-M2.7\",\n          name: \"MiniMax-M2.7\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.7-highspeed\",\n          name: \"MiniMax-M2.7-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.5\",\n          name: \"MiniMax-M2.5\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.5-highspeed\",\n          name: \"MiniMax-M2.5-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.1\",\n          name: \"MiniMax-M2.1\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2.1-highspeed\",\n          name: \"MiniMax-M2.1-highspeed\",\n          organization: \"minimax\",\n        },\n        {\n          id: \"MiniMax-M2\",\n          name: \"MiniMax-M2\",\n          organization: \"minimax\",\n        },\n      ];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.MINIMAX_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getDeepSeekModels(apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    apiKey: apiKey || process.env.DEEPSEEK_API_KEY,\n    baseURL: \"https://api.deepseek.com/v1\",\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by,\n      }))\n    )\n    .catch((e) => {\n      console.error(`DeepSeek:listModels`, e.message);\n      return [\n        {\n          id: \"deepseek-chat\",\n          name: \"deepseek-chat\",\n          organization: \"deepseek\",\n        },\n        {\n          id: \"deepseek-reasoner\",\n          name: \"deepseek-reasoner\",\n          organization: \"deepseek\",\n        },\n      ];\n    });\n\n  if (models.length > 0 && !!apiKey) process.env.DEEPSEEK_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getGiteeAIModels() {\n  const { giteeAiModels } = require(\"../AiProviders/giteeai\");\n  const modelMap = await giteeAiModels();\n  if (!Object.keys(modelMap).length === 0) return { models: [], error: null };\n  const models = Object.values(modelMap).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization ?? \"GiteeAI\",\n      name: model.id,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getXAIModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.XAI_LLM_API_KEY\n      : _apiKey || process.env.XAI_LLM_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.x.ai/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`XAI:listModels`, e.message);\n      return [\n        {\n          created: 1725148800,\n          id: \"grok-beta\",\n          object: \"model\",\n          owned_by: \"xai\",\n        },\n      ];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.XAI_LLM_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getNvidiaNimModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseNvidiaNimBasePath(\n        basePath ?? process.env.NVIDIA_NIM_LLM_BASE_PATH\n      ),\n      apiKey: null,\n    });\n    const modelResponse = await openai.models\n      .list()\n      .then((results) => results.data)\n      .catch((e) => {\n        throw new Error(e.message);\n      });\n\n    const models = modelResponse.map((model) => {\n      return {\n        id: model.id,\n        name: model.id,\n        organization: model.owned_by,\n      };\n    });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`NVIDIA NIM:getNvidiaNimModels`, e.message);\n    return { models: [], error: \"Could not fetch NVIDIA NIM Models\" };\n  }\n}\n\nasync function getGeminiModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.GEMINI_API_KEY\n      : _apiKey || process.env.GEMINI_API_KEY || null;\n  const models = await GeminiLLM.fetchModels(apiKey);\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.GEMINI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getPPIOModels() {\n  const ppioModels = await fetchPPIOModels();\n  if (!Object.keys(ppioModels).length === 0) return { models: [], error: null };\n  const models = Object.values(ppioModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getDellProAiStudioModels(basePath = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  try {\n    const { origin } = new URL(\n      basePath || process.env.DELL_PRO_AI_STUDIO_BASE_PATH\n    );\n    const openai = new OpenAIApi({\n      baseURL: `${origin}/v1/openai`,\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then((models) => {\n        return models\n          .filter(\n            (model) => model?.capability?.includes(\"TextToText\") // Only include text-to-text models for this handler\n          )\n          .map((model) => {\n            return {\n              id: model.id,\n              name: model.name,\n              organization: model.owned_by,\n            };\n          });\n      })\n      .catch((e) => {\n        throw new Error(e.message);\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`getDellProAiStudioModels`, e.message);\n    return {\n      models: [],\n      error: \"Could not reach Dell Pro Ai Studio from the provided base path\",\n    };\n  }\n}\n\nfunction getNativeEmbedderModels() {\n  const { NativeEmbedder } = require(\"../EmbeddingEngines/native\");\n  return { models: NativeEmbedder.availableModels(), error: null };\n}\n\nasync function getMoonshotAiModels(_apiKey = null) {\n  const apiKey =\n    _apiKey === true\n      ? process.env.MOONSHOT_AI_API_KEY\n      : _apiKey || process.env.MOONSHOT_AI_API_KEY || null;\n\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.moonshot.ai/v1\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`MoonshotAi:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0) process.env.MOONSHOT_AI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getFoundryModels(basePath = null) {\n  try {\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: parseFoundryBasePath(basePath || process.env.FOUNDRY_BASE_PATH),\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) =>\n        results.data.map((model) => ({\n          ...model,\n          name: model.id,\n        }))\n      )\n      .catch((e) => {\n        console.error(`Foundry:listModels`, e.message);\n        return [];\n      });\n\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Foundry:getFoundryModels`, e.message);\n    return { models: [], error: \"Could not fetch Foundry Models\" };\n  }\n}\n\n/**\n * Get Cohere models\n * @param {string} _apiKey - The API key to use\n * @param {'chat' | 'embed'} type - The type of model to get\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getCohereModels(_apiKey = null, type = \"chat\") {\n  const apiKey =\n    _apiKey === true\n      ? process.env.COHERE_API_KEY\n      : _apiKey || process.env.COHERE_API_KEY || null;\n\n  const { CohereClient } = require(\"cohere-ai\");\n  const cohere = new CohereClient({\n    token: apiKey,\n  });\n  const models = await cohere.models\n    .list({ pageSize: 1000, endpoint: type })\n    .then((results) => results.models)\n    .then((models) =>\n      models.map((model) => ({\n        id: model.name,\n        name: model.name,\n      }))\n    )\n    .catch((e) => {\n      console.error(`Cohere:listModels`, e.message);\n      return [];\n    });\n\n  return { models, error: null };\n}\n\nasync function getZAiModels(_apiKey = null) {\n  const { OpenAI: OpenAIApi } = require(\"openai\");\n  const apiKey =\n    _apiKey === true\n      ? process.env.ZAI_API_KEY\n      : _apiKey || process.env.ZAI_API_KEY || null;\n  const openai = new OpenAIApi({\n    baseURL: \"https://api.z.ai/api/paas/v4\",\n    apiKey,\n  });\n  const models = await openai.models\n    .list()\n    .then((results) => results.data)\n    .catch((e) => {\n      console.error(`Z.AI:listModels`, e.message);\n      return [];\n    });\n\n  // Api Key was successful so lets save it for future uses\n  if (models.length > 0 && !!apiKey) process.env.ZAI_API_KEY = apiKey;\n  return { models, error: null };\n}\n\nasync function getOpenRouterEmbeddingModels() {\n  const knownModels = await fetchOpenRouterEmbeddingModels();\n  if (!Object.keys(knownModels).length === 0)\n    return { models: [], error: null };\n\n  const models = Object.values(knownModels).map((model) => {\n    return {\n      id: model.id,\n      organization: model.organization,\n      name: model.name,\n    };\n  });\n  return { models, error: null };\n}\n\nasync function getDockerModelRunnerModels(basePath = null) {\n  try {\n    const models = await getDockerModels(basePath);\n    return { models, error: null };\n  } catch (e) {\n    console.error(`DockerModelRunner:getDockerModelRunnerModels`, e.message);\n    return {\n      models: [],\n      error: \"Could not fetch Docker Model Runner Models\",\n    };\n  }\n}\n\nasync function getLemonadeModels(basePath = null, task = \"chat\") {\n  try {\n    const models = await getAllLemonadeModels(basePath, task);\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Lemonade:getLemonadeModels`, e.message);\n    return { models: [], error: \"Could not fetch Lemonade Models\" };\n  }\n}\n\n/**\n * Get Privatemode models\n * @param {string} basePath - The base path of the Privatemode endpoint.\n * @param {'any' | 'generate' | 'embed' | 'transcribe'} task - The task to fetch the models for.\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getPrivatemodeModels(basePath = null, task = \"any\") {\n  try {\n    const { PrivatemodeLLM } = require(\"../AiProviders/privatemode\");\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: PrivatemodeLLM.parseBasePath(\n        basePath || process.env.PRIVATEMODE_LLM_BASE_PATH\n      ),\n      apiKey: null,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then(\n        (models) =>\n          models\n            .filter((model) => !model.id.includes(\"/\")) // remove legacy prefixed models\n            .filter((model) =>\n              task === \"any\" ? true : model.tasks.includes(task)\n            ) // filter by task or show all if task is any\n      )\n      .then((models) =>\n        models.map((model) => ({\n          id: model.id,\n          organization: \"Privatemode\",\n          name: model.id\n            .split(\"-\")\n            .map((word) => word.charAt(0).toUpperCase() + word.slice(1))\n            .join(\" \"),\n        }))\n      )\n      .catch((e) => {\n        console.error(`Privatemode:listModels`, e.message);\n        return [];\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`Privatemode:getPrivatemodeModels`, e.message);\n    return { models: [], error: \"Could not fetch Privatemode Models\" };\n  }\n}\n\n/**\n * Get SambaNova models\n * @param {string} _apiKey - The API key to use\n * @returns {Promise<{models: Array<{id: string, organization: string, name: string}>, error: string | null}>}\n */\nasync function getSambaNovaModels(_apiKey = null) {\n  try {\n    const apiKey =\n      _apiKey === true\n        ? process.env.SAMBANOVA_LLM_API_KEY\n        : _apiKey || process.env.SAMBANOVA_LLM_API_KEY || null;\n    const { OpenAI: OpenAIApi } = require(\"openai\");\n    const openai = new OpenAIApi({\n      baseURL: \"https://api.sambanova.ai/v1\",\n      apiKey,\n    });\n    const models = await openai.models\n      .list()\n      .then((results) => results.data)\n      .then((models) =>\n        models.filter((model) => !model.id.toLowerCase().startsWith(\"whisper\"))\n      )\n      .then((models) =>\n        models.map((model) => {\n          const organization =\n            model.hasOwnProperty(\"owned_by\") &&\n            model.owned_by !== \"no-reply@sambanova.ai\"\n              ? model.owned_by\n              : \"SambaNova\";\n          return {\n            id: model.id,\n            organization,\n            name: model.id,\n          };\n        })\n      )\n      .catch((e) => {\n        console.error(`SambaNova:listModels`, e.message);\n        return [];\n      });\n    return { models, error: null };\n  } catch (e) {\n    console.error(`SambaNova:getSambaNovaModels`, e.message);\n    return { models: [], error: \"Could not fetch SambaNova Models\" };\n  }\n}\n\nmodule.exports = {\n  getCustomModels,\n  SUPPORT_CUSTOM_MODELS,\n};\n"}}}}