@ai-ml.api/aimlapi-vercel-ai/dist/index.mjs.map

AI SDK provider for AI/ML API: 300+ models via OpenAI-compatible API.

{"version":3,"sources":["../src/aimlapi-provider.ts","../src/aimlapi-error.ts","../src/aimlapi-image-model.ts"],"sourcesContent":["import {\r\n  OpenAICompatibleChatLanguageModel,\r\n  OpenAICompatibleCompletionLanguageModel,\r\n  OpenAICompatibleEmbeddingModel,\r\n  type ProviderErrorStructure,\r\n} from '@ai-sdk/openai-compatible';\r\nimport {\r\n  type EmbeddingModelV2,\r\n  type ImageModelV2,\r\n  type LanguageModelV2,\r\n  type ProviderV2,\r\n} from '@ai-sdk/provider';\r\nimport {\r\n  type FetchFunction,\r\n  loadApiKey,\r\n  withoutTrailingSlash,\r\n  type Resolvable,\r\n} from '@ai-sdk/provider-utils';\r\nimport { aimlapiErrorSchema } from './aimlapi-error';\r\nimport {\r\n  type AIMLAPIImageModelId,\r\n  type AimlapiImageSettings,\r\n} from './aimlapi-image-settings';\r\nimport { AimlapiImageModel } from './aimlapi-image-model';\r\nimport { z } from 'zod';\r\n\r\nexport interface AIMLAPIProviderSettings {\r\n  apiKey?: string;\r\n  baseURL?: string;\r\n  headers?: Resolvable<Record<string, string | undefined>>;\r\n  fetch?: FetchFunction;\r\n}\r\n\r\nexport interface AIMLAPIProvider extends ProviderV2 {\r\n  (modelId: string): LanguageModelV2;\r\n\r\n  chat(modelId: string): LanguageModelV2;\r\n  completion(modelId: string): LanguageModelV2;\r\n  languageModel(modelId: string): LanguageModelV2;\r\n  textEmbeddingModel(modelId: string): EmbeddingModelV2<string>;\r\n  imageModel(\r\n    modelId: string,\r\n    settings?: AimlapiImageSettings,\r\n  ): ImageModelV2;\r\n}\r\n\r\nconst defaultBaseURL = 'https://api.aimlapi.com/v1';\r\n\r\n// ❗ Используем тип, ВЫВЕДЕННЫЙ ИЗ СХЕМЫ:\r\ntype AimErr = z.infer<typeof aimlapiErrorSchema>;\r\n\r\nconst aimlapiErrorStructure: ProviderErrorStructure<AimErr> = {\r\n  errorSchema: aimlapiErrorSchema,\r\n  errorToMessage: (data) => data.error.message,\r\n  // isRetryable: (res) => res.status === 429 || res.status >= 500,\r\n};\r\n\r\nexport function createAIMLAPI(\r\n  options: AIMLAPIProviderSettings = {},\r\n): AIMLAPIProvider {\r\n  const rawBaseURL = options.baseURL ?? defaultBaseURL;\r\n  const strictBaseURL = withoutTrailingSlash(rawBaseURL) ?? defaultBaseURL;\r\n\r\n  const getHeadersLoose = (): Record<string, string | undefined> => ({\r\n    Authorization: `Bearer ${loadApiKey({\r\n      apiKey: options.apiKey,\r\n      environmentVariableName: 'AIMLAPI_API_KEY',\r\n      description: 'AIMLAPI API key',\r\n    })}`,\r\n    'X-Title': 'vercel',\r\n    ...(typeof options.headers === 'function'\r\n      ? (options.headers as any)()\r\n      : options.headers),\r\n  });\r\n\r\n  const getHeadersStrict = (): Record<string, string> =>\r\n    Object.fromEntries(\r\n      Object.entries(getHeadersLoose()).filter(\r\n        ([, v]) => v !== undefined,\r\n      ) as Array<[string, string]>,\r\n    );\r\n\r\n  interface CommonModelConfig {\r\n    provider: string;\r\n    url: ({ path }: { path: string }) => string;\r\n    headers: () => Record<string, string>;\r\n    fetch?: FetchFunction;\r\n    errorStructure?: ProviderErrorStructure<AimErr>;\r\n  }\r\n\r\n  const getCommonModelConfig = (modelType: string): CommonModelConfig => ({\r\n    provider: `aimlapi.${modelType}`,\r\n    url: ({ path }) => `${strictBaseURL}${path}`,\r\n    headers: getHeadersStrict,\r\n    fetch: options.fetch,\r\n    errorStructure: aimlapiErrorStructure,\r\n  });\r\n\r\n  const createChat = (modelId: string) =>\r\n    new OpenAICompatibleChatLanguageModel(modelId, getCommonModelConfig('chat'));\r\n\r\n  const createCompletion = (modelId: string) =>\r\n    new OpenAICompatibleCompletionLanguageModel(\r\n      modelId,\r\n      getCommonModelConfig('completion'),\r\n    );\r\n\r\n  const createEmbedding = (modelId: string) =>\r\n    new OpenAICompatibleEmbeddingModel(\r\n      modelId,\r\n      getCommonModelConfig('embedding'),\r\n    );\r\n\r\n  const createImage = (\r\n    modelId: AIMLAPIImageModelId,\r\n    settings: AimlapiImageSettings = {},\r\n  ) =>\r\n    new AimlapiImageModel(modelId, settings, {\r\n      provider: 'aimlapi.image',\r\n      baseURL: strictBaseURL,\r\n      headers: getHeadersStrict,\r\n      fetch: options.fetch,\r\n    });\r\n\r\n  const providerFunc = (modelId: string) => createChat(modelId);\r\n  (providerFunc as any).chat = createChat;\r\n  (providerFunc as any).completion = createCompletion;\r\n  (providerFunc as any).languageModel = createChat;\r\n  (providerFunc as any).textEmbeddingModel = createEmbedding;\r\n  (providerFunc as any).imageModel = createImage;\r\n\r\n  return providerFunc as unknown as AIMLAPIProvider;\r\n}\r\n\r\nexport const aimlapi = createAIMLAPI();\r\n","import { z } from 'zod';\r\n\r\n/**\r\n * Unified AIMLAPI error schema.\r\n * Minimum required fields compatible with createJsonErrorResponseHandler<T>.\r\n */\r\nexport const aimlapiErrorSchema = z.object({\r\n  error: z.object({\r\n    message: z.string(),\r\n    code: z.string().optional(),\r\n    type: z.string().optional(),\r\n  }),\r\n});\r\n\r\nexport type AIMLAPIErrorData = z.infer<typeof aimlapiErrorSchema>;\r\n","import type { ImageModelV2, ImageModelV2CallWarning } from '@ai-sdk/provider';\r\nimport type { Resolvable } from '@ai-sdk/provider-utils';\r\nimport {\r\n  FetchFunction,\r\n  combineHeaders,\r\n  createJsonErrorResponseHandler,\r\n  createJsonResponseHandler,\r\n  postJsonToApi,\r\n  resolve,\r\n} from '@ai-sdk/provider-utils';\r\nimport { z } from 'zod';\r\nimport type { AIMLAPIImageModelId, AimlapiImageSettings } from './aimlapi-image-settings';\r\nimport { aimlapiErrorSchema } from './aimlapi-error';\r\ntype AimErr = z.infer<typeof aimlapiErrorSchema>;\r\n\r\ninterface AIMLAPIImageModelConfig {\r\n  provider: string;\r\n  baseURL: string;\r\n  headers?: Resolvable<Record<string, string | undefined>>;\r\n  fetch?: FetchFunction;\r\n  _internal?: {\r\n    currentDate?: () => Date;\r\n  };\r\n}\r\n\r\nexport class AimlapiImageModel implements ImageModelV2 {\r\n  readonly specificationVersion = 'v2';\r\n  readonly maxImagesPerCall = 1;\r\n\r\n  get provider(): string {\r\n    return this.config.provider;\r\n  }\r\n\r\n  constructor(\r\n    readonly modelId: AIMLAPIImageModelId,\r\n    readonly settings: AimlapiImageSettings = {},\r\n    private readonly config: AIMLAPIImageModelConfig,\r\n  ) {}\r\n\r\n  async doGenerate({ prompt, n, aspectRatio, size, seed, providerOptions, headers, abortSignal }: Parameters<ImageModelV2['doGenerate']>[0]) {\r\n    const warnings: ImageModelV2CallWarning[] = [];\r\n\r\n    const modelSupportsAspectRatio = true;\r\n    if (!modelSupportsAspectRatio && aspectRatio != null) {\r\n      warnings.push({\r\n        type: 'unsupported-setting',\r\n        setting: 'aspectRatio',\r\n        details: 'This model does not support `aspectRatio`. Use `size` instead.',\r\n      });\r\n    }\r\n\r\n    if (size != null && aspectRatio != null) {\r\n      warnings.push({\r\n        type: 'other',\r\n        message: 'Both `size` and `aspectRatio` were provided. The API will prioritize `size`.',\r\n      });\r\n    }\r\n\r\n    const currentDate = this.config._internal?.currentDate?.() ?? new Date();\r\n    const splitSize = size?.split('x');\r\n\r\n    const resolvedProviderHeaders =\r\n      (await resolve(this.config.headers)) as Record<string, string | undefined> | undefined;\r\n\r\n    const { value: response, responseHeaders } = await postJsonToApi({\r\n      url: `${this.config.baseURL}/images/generations`,\r\n      headers: combineHeaders(resolvedProviderHeaders, headers),\r\n      body: {\r\n        model: this.modelId,\r\n        prompt,\r\n        seed,\r\n        n,\r\n        ...(splitSize && {\r\n          width: parseInt(splitSize[0]!, 10),\r\n          height: parseInt(splitSize[1]!, 10),\r\n        }),\r\n        ...(aspectRatio && { aspect_ratio: aspectRatio }),\r\n        // Don't force base64: some models will return the url anyway\r\n        ...(providerOptions?.aimlapi ?? {}),\r\n      },\r\n      successfulResponseHandler: createJsonResponseHandler(aimlapiImageResponseSchema),\r\n      failedResponseHandler: createJsonErrorResponseHandler<AimErr>({\r\n        errorSchema: aimlapiErrorSchema,\r\n        errorToMessage: (e) => e.error.message,\r\n        isRetryable: (res, e) => {\r\n          if (res.status === 429) return true;\r\n          if (res.status >= 500) return true;\r\n          if (e?.error?.code === 'overloaded' || e?.error?.code === 'timeout') return true;\r\n          return false;\r\n        },\r\n      }),\r\n      abortSignal,\r\n      fetch: this.config.fetch,\r\n    });\r\n\r\n    // response.normalized: string[] (b64 or url)\r\n    return {\r\n      images: response.normalized,\r\n      warnings,\r\n      response: {\r\n        timestamp: currentDate,\r\n        modelId: this.modelId,\r\n        headers: responseHeaders,\r\n      },\r\n    };\r\n  }\r\n}\r\n\r\n// We accept both possible formats and normalize\r\nconst aimlapiImageResponseSchema = z.union([\r\n  // Option A: OpenAI-compatible base64\r\n  z.object({\r\n    data: z.array(z.object({ b64_json: z.string() })),\r\n  }).transform((v) => ({ normalized: v.data.map((d) => d.b64_json) })),\r\n\r\n  // Option B: AIMLAPI format with images (url or b64_json)\r\n  z.object({\r\n    images: z.array(\r\n      z.object({\r\n        b64_json: z.string().optional(),\r\n        url: z.string().url().optional(),\r\n      }),\r\n    ),\r\n  }).transform((v) => ({\r\n    normalized: v.images\r\n      .map((i) => i.b64_json ?? i.url ?? 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