{"repo": "ahwanulm/amrouter", "files": {"backend/src/routes/providers/[id]/models/route.ts": {"3a762d0f6313eab0869e1b9f6bf5bee2a7ea607d": {"t": 1786068366.8542845, "n": 21446, "h": "fee0f5dc9c03d701", "c": "\nimport { getProviderConnectionById } from \"../../../../models/index.js\";\nimport { isOpenAICompatibleProvider, isAnthropicCompatibleProvider } from \"../../../../shared/constants/providers.js\";\nimport { GEMINI_CONFIG } from \"../../../../lib/oauth/constants/oauth.js\";\nimport { refreshGoogleToken, refreshTokenByProvider, updateProviderCredentials } from \"../../../../sse/services/tokenRefresh.js\";\nimport {\n  buildXaiCliHeaders,\n  expandXaiEffortVariants,\n  isXaiOAuthConnection,\n  xaiModelsUrl,\n} from \"../../../../shared/utils/xaiCli.js\";\nimport { resolveOllamaLocalHost } from \"../../../../../open-sse/config/providers.js\";\nimport { resolveKiroModels } from \"../../../../../open-sse/services/kiroModels.js\";\nimport { resolveQoderModels } from \"../../../../../open-sse/services/qoderModels.js\";\n\nconst GEMINI_CLI_MODELS_URL = \"https://cloudcode-pa.googleapis.com/v1internal:fetchAvailableModels\";\n\nconst parseOpenAIStyleModels = (data) => {\n  if (Array.isArray(data)) return data;\n  return data?.data || data?.models || data?.results || [];\n};\n\nconst parseGeminiCliModels = (data) => {\n  if (Array.isArray(data?.models)) {\n    return data.models\n      .map((item) => {\n        const id = item?.id || item?.model || item?.name;\n        if (!id) return null;\n        return { id, name: item?.displayName || item?.name || id };\n      })\n      .filter(Boolean);\n  }\n\n  if (data?.models && typeof data.models === \"object\") {\n    return Object.entries(data.models)\n      .filter(([, info]) => !info?.isInternal)\n      .map(([id, info]) => ({\n        id,\n        name: info?.displayName || info?.name || id,\n      }));\n  }\n\n  return [];\n};\n\nconst appendCodexReviewModels = (models) => models.flatMap((model) => {\n  const id = model?.id || model?.slug || model?.model || model?.name;\n  if (!id) return [];\n  const name = model?.display_name || model?.displayName || model?.name || id;\n  const normalized = { ...model, id, name };\n  const isChatModel = (model?.type || \"llm\") !== \"image\" && !id.toLowerCase().includes(\"embed\");\n  if (!isChatModel || id.endsWith(\"-review\")) return [normalized];\n  return [\n    normalized,\n    {\n      ...normalized,\n      id: `${id}-review`,\n      name: `${name} Review`,\n      upstreamModelId: id,\n      quotaFamily: \"review\",\n    },\n  ];\n});\n\nconst parseCodexModels = (data) => appendCodexReviewModels(parseOpenAIStyleModels(data));\n\nconst createOpenAIModelsConfig = (url) => ({\n  url,\n  method: \"GET\",\n  headers: { \"Content-Type\": \"application/json\" },\n  authHeader: \"Authorization\",\n  authPrefix: \"Bearer \",\n  parseResponse: parseOpenAIStyleModels\n});\n\nconst resolveQwenModelsUrl = (connection) => {\n  const fallback = \"https://portal.qwen.ai/v1/models\";\n  const raw = connection?.providerSpecificData?.resourceUrl;\n  if (!raw || typeof raw !== \"string\") return fallback;\n  const value = raw.trim();\n  if (!value) return fallback;\n  if (value.startsWith(\"http://\") || value.startsWith(\"https://\")) {\n    return `${value.replace(/\\/$/, \"\")}/models`;\n  }\n  return `https://${value.replace(/\\/$/, \"\")}/v1/models`;\n};\n\n// Generic custom resolver for OAuth providers that need refresh-on-401 + token persist.\n// Receives a `fetchFn(token)` and returns parsed models or throws.\nconst buildOAuthResolver = ({ refreshFn, fetchFn, parseFn, errorLabel }) => async (connection) => {\n  const { accessToken, refreshToken } = connection;\n  if (!accessToken) {\n    return { error: \"No valid token found\", status: 401 };\n  }\n  let warning;\n  try {\n    let response = await fetchFn(accessToken, connection);\n    if (!response.ok && (response.status === 401 || response.status === 403) && refreshToken) {\n      const refreshed = await refreshFn(connection);\n      if (refreshed?.accessToken) {\n        await updateProviderCredentials(connection.id, {\n          accessToken: refreshed.accessToken,\n          refreshToken: refreshed.refreshToken || refreshToken,\n          expiresIn: refreshed.expiresIn,\n        });\n        connection.accessToken = refreshed.accessToken;\n        if (refreshed.refreshToken) connection.refreshToken = refreshed.refreshToken;\n        response = await fetchFn(refreshed.accessToken, connection);\n      }\n    }\n    if (response.ok) {\n      const data = await response.json();\n      const models = parseFn(data);\n      if (models.length > 0) return { models };\n    } else {\n      const errorText = await response.text();\n      warning = `${errorLabel}: ${response.status} ${errorText}`;\n      console.log(`${errorLabel} (falling back to static):`, errorText);\n    }\n  } catch (error) {\n    warning = `${errorLabel}: ${error.message}`;\n    console.log(`${errorLabel} (falling back to static):`, error.message);\n  }\n  return { models: [], warning };\n};\n\n// Provider models endpoints configuration\nconst PROVIDER_MODELS_CONFIG = {\n  claude: {\n    url: \"https://api.anthropic.com/v1/models\",\n    method: \"GET\",\n    headers: {\n      \"Anthropic-Version\": \"2023-06-01\",\n      \"Content-Type\": \"application/json\"\n    },\n    authHeader: \"x-api-key\",\n    parseResponse: (data) => data.data || []\n  },\n  gemini: {\n    url: \"https://generativelanguage.googleapis.com/v1beta/models\",\n    method: \"GET\",\n    headers: { \"Content-Type\": \"application/json\" },\n    authQuery: \"key\", // Use query param for API key\n    parseResponse: (data) => data.models || []\n  },\n  qwen: {\n    url: \"https://portal.qwen.ai/v1/models\",\n    method: \"GET\",\n    headers: { \"Content-Type\": \"application/json\" },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    parseResponse: (data) => data.data || []\n  },\n  codex: {\n    url: \"https://chatgpt.com/backend-api/codex/models?client_version=1.0.0\",\n    method: \"GET\",\n    headers: { \"Content-Type\": \"application/json\", \"Accept\": \"application/json\" },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    parseResponse: parseCodexModels\n  },\n  antigravity: {\n    url: \"https://daily-cloudcode-pa.sandbox.googleapis.com/v1internal:models\",\n    method: \"POST\",\n    headers: { \"Content-Type\": \"application/json\" },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    body: {},\n    parseResponse: (data) => data.models || []\n  },\n  github: {\n    url: \"https://api.githubcopilot.com/models\",\n    method: \"GET\",\n    headers: {\n      \"Content-Type\": \"application/json\",\n      \"Copilot-Integration-Id\": \"vscode-chat\",\n      \"editor-version\": \"vscode/1.107.1\",\n      \"editor-plugin-version\": \"copilot-chat/0.26.7\",\n      \"user-agent\": \"GitHubCopilotChat/0.26.7\"\n    },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    parseResponse: (data) => {\n      if (!data?.data) return [];\n      // Filter out embeddings, non-chat models, and disabled models\n      return data.data\n        .filter(m => m.capabilities?.type === \"chat\")\n        .filter(m => m.policy?.state !== \"disabled\") // Only return explicitly enabled models\n        .map(m => ({\n          id: m.id,\n          name: m.name || m.id,\n          version: m.version,\n          capabilities: m.capabilities,\n          isDefault: m.model_picker_enabled === true\n        }));\n    }\n  },\n  openai: createOpenAIModelsConfig(\"https://api.openai.com/v1/models\"),\n  openrouter: createOpenAIModelsConfig(\"https://openrouter.ai/api/v1/models\"),\n  anthropic: {\n    url: \"https://api.anthropic.com/v1/models\",\n    method: \"GET\",\n    headers: {\n      \"Anthropic-Version\": \"2023-06-01\",\n      \"Content-Type\": \"application/json\"\n    },\n    authHeader: \"x-api-key\",\n    parseResponse: (data) => data.data || []\n  },\n\n  alicode: {\n    url: \"https://coding.dashscope.aliyuncs.com/v1/models\",\n    method: \"GET\",\n    headers: { \"Content-Type\": \"application/json\" },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    parseResponse: (data) => data.data || []\n  },\n  \"alicode-intl\": {\n    url: \"https://coding-intl.dashscope.aliyuncs.com/v1/models\",\n    method: \"GET\",\n    headers: { \"Content-Type\": \"application/json\" },\n    authHeader: \"Authorization\",\n    authPrefix: \"Bearer \",\n    parseResponse: (data) => data.data || []\n  },\n  \"volcengine-ark\": createOpenAIModelsConfig(\"https://ark.cn-beijing.volces.com/api/coding/v3/models\"),\n  byteplus: createOpenAIModelsConfig(\"https://ark.ap-southeast.bytepluses.com/api/coding/v3/models\"),\n\n  // OpenAI-compatible API key providers\n  deepseek: createOpenAIModelsConfig(\"https://api.deepseek.com/models\"),\n  groq: createOpenAIModelsConfig(\"https://api.groq.com/openai/v1/models\"),\n  xai: {\n    // OAuth (grok-cli) tokens have no API credits, so api.x.ai/v1/models answers\n    // 403 personal-team-blocked:spending-limit for them. Their entitlement lives\n    // on the CLI chat proxy instead — the same split XaiExecutor.buildUrl makes\n    // for inference. API-key connections keep the plain api.x.ai listing.\n    //\n    // The proxy lists exactly one id (grok-4.5) and exposes reasoning as a\n    // per-request parameter, so the effort variants are synthesised here from the\n    // supports_reasoning_effort/reasoning_efforts fields it declares —\n    // grok-4.5-thinking and friends do not exist upstream. XaiExecutor turns the\n    // suffix back into reasoning_effort at dispatch.\n    customResolver: async (connection) => {\n      if (!isXaiOAuthConnection(connection)) {\n        const response = await fetch(xaiModelsUrl(connection), {\n          method: \"GET\",\n          headers: {\n            \"Content-Type\": \"application/json\",\n            \"Authorization\": `Bearer ${connection.apiKey}`,\n          },\n        });\n        if (!response.ok) {\n          const errorText = await response.text();\n          console.log(\"Error fetching models from xai:\", errorText);\n          return { error: `Failed to fetch models: ${response.status}`, status: response.status };\n        }\n        return { models: parseOpenAIStyleModels(await response.json()) };\n      }\n      return buildOAuthResolver({\n        refreshFn: (conn) => refreshTokenByProvider(\"xai\", conn),\n        fetchFn: (token) => fetch(xaiModelsUrl({ accessToken: token }), {\n          method: \"GET\",\n          headers: buildXaiCliHeaders(token, { \"Content-Type\": \"application/json\" }),\n        }),\n        parseFn: (data) => expandXaiEffortVariants(parseOpenAIStyleModels(data)),\n        errorLabel: \"Failed to fetch xAI CLI models\",\n      })(connection);\n    },\n  },\n  mistral: createOpenAIModelsConfig(\"https://api.mistral.ai/v1/models\"),\n  perplexity: createOpenAIModelsConfig(\"https://api.perplexity.ai/models\"),\n  together: createOpenAIModelsConfig(\"https://api.together.xyz/v1/models\"),\n  fireworks: createOpenAIModelsConfig(\"https://api.fireworks.ai/inference/v1/models\"),\n  cerebras: createOpenAIModelsConfig(\"https://api.cerebras.ai/v1/models\"),\n  cohere: createOpenAIModelsConfig(\"https://api.cohere.ai/v1/models\"),\n  nebius: createOpenAIModelsConfig(\"https://api.studio.nebius.ai/v1/models\"),\n  siliconflow: createOpenAIModelsConfig(\"https://api.siliconflow.cn/v1/models\"),\n  hyperbolic: createOpenAIModelsConfig(\"https://api.hyperbolic.xyz/v1/models\"),\n  ollama: createOpenAIModelsConfig(\"https://ollama.com/api/tags\"),\n  // ollama-local: url resolved dynamically below via providerSpecificData.baseUrl\n  nanobanana: createOpenAIModelsConfig(\"https://api.nanobananaapi.ai/v1/models\"),\n  chutes: createOpenAIModelsConfig(\"https://llm.chutes.ai/v1/models\"),\n  nvidia: createOpenAIModelsConfig(\"https://integrate.api.nvidia.com/v1/models\"),\n  assemblyai: createOpenAIModelsConfig(\"https://api.assemblyai.com/v1/models\"),\n  \"vercel-ai-gateway\": createOpenAIModelsConfig(\"https://ai-gateway.vercel.sh/v1/models\"),\n\n  // Custom resolvers (non-OpenAI-shaped APIs / token-refresh flows)\n  codebuddy: {\n    customResolver: async (connection) => {\n      const { getModelsByProviderId } = await import(\"../../../../shared/constants/models\");\n      const models = getModelsByProviderId(\"codebuddy\");\n      return { models };\n    }\n  },\n  cb: {\n    customResolver: async (connection) => {\n      const { getModelsByProviderId } = await import(\"../../../../shared/constants/models\");\n      const models = getModelsByProviderId(\"cb\");\n      return { models };\n    }\n  },\n  kiro: {\n    customResolver: async (connection) => {\n      const credentials = {\n        accessToken: connection.accessToken,\n        refreshToken: connection.refreshToken,\n        providerSpecificData: connection.providerSpecificData || {}\n      };\n      let warning;\n      try {\n        const result = await resolveKiroModels(credentials, {\n          log: console,\n          onCredentialsRefreshed: async (refreshed) => {\n            if (refreshed?.accessToken) {\n              await updateProviderCredentials(connection.id, {\n                accessToken: refreshed.accessToken,\n                refreshToken: refreshed.refreshToken || connection.refreshToken,\n                expiresIn: refreshed.expiresIn,\n              });\n              connection.accessToken = refreshed.accessToken;\n              if (refreshed.refreshToken) connection.refreshToken = refreshed.refreshToken;\n            }\n          }\n        });\n        if (result?.models?.length) {\n          return {\n            models: result.models.map((m) => ({\n              id: m.id,\n              name: m.name,\n              upstreamModelId: m.upstreamModelId,\n              contextLength: m.contextLength,\n              rateMultiplier: m.rateMultiplier,\n              capabilities: m.capabilities,\n              description: m.description\n            }))\n          };\n        }\n        warning = \"Kiro returned no models; falling back to static catalog.\";\n      } catch (error) {\n        warning = `Failed to fetch Kiro models: ${error.message}`;\n        console.log(\"Failed to fetch Kiro models dynamically, falling back to static:\", error.message);\n      }\n      return { models: [], warning };\n    }\n  },\n  qoder: {\n    customResolver: async (connection) => {\n      const credentials = {\n        accessToken: connection.accessToken,\n        refreshToken: connection.refreshToken,\n        email: connection.email,\n        displayName: connection.displayName,\n        providerSpecificData: connection.providerSpecificData || {},\n      };\n      let warning;\n      try {\n        const result = await resolveQoderModels(credentials, { forceRefresh: true });\n        if (result?.models?.length) {\n          return {\n            models: result.models.map((m) => ({\n              // Use the canonical \"qoder/<key>\" id so the dashboard\n              // surfaces the same identifier the chat router expects.\n              id: `qoder/${m.id}`,\n              name: m.name,\n              contextLength: m.contextLength,\n              isVL: m.isVL,\n              isReasoning: m.isReasoning,\n              maxOutputTokens: m.maxOutputTokens,\n              description: m.description,\n            })),\n          };\n        }\n        warning = \"Qoder returned no models; falling back to static catalog.\";\n      } catch (error) {\n        warning = `Failed to fetch Qoder models: ${error.message}`;\n        console.log(\"Failed to fetch Qoder models dynamically, falling back to static:\", error.message);\n      }\n      return { models: [], warning };\n    },\n  },\n  \"gemini-cli\": {\n    customResolver: buildOAuthResolver({\n      refreshFn: (conn) => refreshGoogleToken(conn.refreshToken, GEMINI_CONFIG.clientId, GEMINI_CONFIG.clientSecret),\n      fetchFn: (token, conn) => {\n        const projectId = conn.projectId || conn.providerSpecificData?.projectId;\n        const body = projectId ? { project: projectId } : {};\n        return fetch(GEMINI_CLI_MODELS_URL, {\n          method: \"POST\",\n          headers: {\n            \"Content-Type\": \"application/json\",\n            \"Authorization\": `Bearer ${token}`,\n            \"User-Agent\": \"google-api-nodejs-client/9.15.1\",\n            \"X-Goog-Api-Client\": \"google-cloud-sdk vscode_cloudshelleditor/0.1\"\n          },\n          body: JSON.stringify(body)\n        });\n      },\n      parseFn: parseGeminiCliModels,\n      errorLabel: \"Failed to fetch Gemini CLI models\"\n    })\n  },\n  \"ollama-local\": {\n    customResolver: async (connection) => {\n      const url = `${resolveOllamaLocalHost(connection)}/api/tags`;\n      const response = await fetch(url, {\n        method: \"GET\",\n        headers: { \"Content-Type\": \"application/json\" }\n      });\n      if (!response.ok) {\n        const errorText = await response.text();\n        console.log(\"Error fetching models from ollama-local:\", errorText);\n        return { error: `Failed to fetch models: ${response.status}`, status: response.status };\n      }\n      const data = await response.json();\n      return { models: parseOpenAIStyleModels(data) };\n    }\n  }\n};\n\n/**\n * GET /api/providers/[id]/models - Get models list from provider\n */\nexport async function GET_handler(req, res, { params }) {\n  try {\n    const { id } = await params;\n    const connection = await getProviderConnectionById(id);\n\n    if (!connection) {\n      return res.status(404).json({ error: \"Connection not found\" });\n    }\n\n    if (isOpenAICompatibleProvider(connection.provider)) {\n      const baseUrl = connection.providerSpecificData?.baseUrl;\n      if (!baseUrl) {\n        return res.status(400).json({ error: \"No base URL configured for OpenAI compatible provider\" });\n      }\n      const url = `${baseUrl.replace(/\\/$/, \"\")}/models`;\n      const response = await fetch(url, {\n        method: \"GET\",\n        headers: {\n          \"Content-Type\": \"application/json\",\n          \"Authorization\": `Bearer ${connection.apiKey}`,\n        },\n      });\n\n      if (!response.ok) {\n        const errorText = await response.text();\n        console.log(`Error fetching models from ${connection.provider}:`, errorText);\n        return res.status(response.status).json(\n          { error: `Failed to fetch models: ${response.status}` }\n        );\n      }\n\n      const data = await response.json();\n      const models = data.data || data.models || [];\n\n      return res.json({\n        provider: connection.provider,\n        connectionId: connection.id,\n        models\n      });\n    }\n\n    if (isAnthropicCompatibleProvider(connection.provider)) {\n      let baseUrl = connection.providerSpecificData?.baseUrl;\n      if (!baseUrl) {\n        return res.status(400).json({ error: \"No base URL configured for Anthropic compatible provider\" });\n      }\n\n      baseUrl = baseUrl.replace(/\\/$/, \"\");\n      if (baseUrl.endsWith(\"/messages\")) {\n        baseUrl = baseUrl.slice(0, -9);\n      }\n\n      const url = `${baseUrl}/models`;\n      const response = await fetch(url, {\n        method: \"GET\",\n        headers: {\n          \"Content-Type\": \"application/json\",\n          \"x-api-key\": connection.apiKey,\n          \"anthropic-version\": \"2023-06-01\",\n          \"Authorization\": `Bearer ${connection.apiKey}`\n        },\n      });\n\n      if (!response.ok) {\n        const errorText = await response.text();\n        console.log(`Error fetching models from ${connection.provider}:`, errorText);\n        return res.status(response.status).json(\n          { error: `Failed to fetch models: ${response.status}` }\n        );\n      }\n\n      const data = await response.json();\n      const models = data.data || data.models || [];\n\n      return res.json({\n        provider: connection.provider,\n        connectionId: connection.id,\n        models\n      });\n    }\n\n    const config = PROVIDER_MODELS_CONFIG[connection.provider];\n    if (!config) {\n      return res.status(400).json(\n        { error: `Provider ${connection.provider} does not support models listing` }\n      );\n    }\n\n    // Config-driven custom resolver path (OAuth refresh, non-OpenAI shape, etc.)\n    if (typeof config.customResolver === \"function\") {\n      const result = await config.customResolver(connection);\n      if (result.error) {\n        return res.status(result.status || 500).json({ error: result.error });\n      }\n      return res.json({\n        provider: connection.provider,\n        connectionId: connection.id,\n        models: result.models,\n        ...(result.warning ? { warning: result.warning } : {})\n      });\n    }\n\n    // Get auth token\n    const token = connection.providerSpecificData?.copilotToken || connection.accessToken || connection.apiKey;\n    if (!token) {\n      return res.status(401).json({ error: \"No valid token found\" });\n    }\n\n    // Build request URL\n    let url = config.url;\n    if (connection.provider === \"qwen\") {\n      url = resolveQwenModelsUrl(connection);\n    }\n    if (config.authQuery) {\n      url += `?${config.authQuery}=${token}`;\n    }\n\n    // Build headers\n    const headers = { ...config.headers };\n    if (config.authHeader && !config.authQuery) {\n      headers[config.authHeader] = (config.authPrefix || \"\") + token;\n    }\n\n    // Make request\n    const fetchOptions = {\n      method: config.method,\n      headers\n    };\n\n    if (config.body && config.method === \"POST\") {\n      fetchOptions.body = JSON.stringify(config.body);\n    }\n\n    const response = await fetch(url, fetchOptions);\n\n    if (!response.ok) {\n      const errorText = await response.text();\n      console.log(`Error fetching models from ${connection.provider}:`, errorText);\n      return res.status(response.status).json(\n        { error: `Failed to fetch models: ${response.status}` }\n      );\n    }\n\n    const data = await response.json();\n    const models = config.parseResponse(data);\n\n    return res.json({\n      provider: connection.provider,\n      connectionId: connection.id,\n      models\n    });\n  } catch (error) {\n    console.log(\"Error fetching provider models:\", error);\n    return res.status(500).json({ error: \"Failed to fetch models\" });\n  }\n}\n"}}}}