{"version":3,"sources":["../src/deepinfra-provider.ts","../src/deepinfra-image-model.ts","../src/deepinfra-chat-language-model.ts","../src/version.ts"],"sourcesContent":["import type {\n LanguageModelV3,\n EmbeddingModelV3,\n ProviderV3,\n ImageModelV3,\n} from '@ai-sdk/provider';\nimport {\n OpenAICompatibleCompletionLanguageModel,\n OpenAICompatibleEmbeddingModel,\n} from '@ai-sdk/openai-compatible';\nimport {\n loadApiKey,\n withoutTrailingSlash,\n withUserAgentSuffix,\n type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport type { DeepInfraChatModelId } from './deepinfra-chat-options';\nimport type { DeepInfraEmbeddingModelId } from './deepinfra-embedding-options';\nimport type { DeepInfraCompletionModelId } from './deepinfra-completion-options';\nimport type { DeepInfraImageModelId } from './deepinfra-image-settings';\nimport { DeepInfraImageModel } from './deepinfra-image-model';\nimport { DeepInfraChatLanguageModel } from './deepinfra-chat-language-model';\nimport { VERSION } from './version';\n\nexport interface DeepInfraProviderSettings {\n /**\n * DeepInfra API key.\n */\n apiKey?: string;\n /**\n * Base URL for the API calls.\n */\n baseURL?: string;\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record;\n /**\n * Custom fetch implementation. You can use it as a middleware to intercept requests,\n * or to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\nexport interface DeepInfraProvider extends ProviderV3 {\n /**\n * Creates a model for text generation.\n */\n (modelId: DeepInfraChatModelId): LanguageModelV3;\n\n /**\n * Creates a chat model for text generation.\n */\n chatModel(modelId: DeepInfraChatModelId): LanguageModelV3;\n\n /**\n * Creates a model for image generation.\n */\n image(modelId: DeepInfraImageModelId): ImageModelV3;\n\n /**\n * Creates a model for image generation.\n */\n imageModel(modelId: DeepInfraImageModelId): ImageModelV3;\n\n /**\n * Creates a chat model for text generation.\n */\n languageModel(modelId: DeepInfraChatModelId): LanguageModelV3;\n\n /**\n * Creates a completion model for text generation.\n */\n completionModel(modelId: DeepInfraCompletionModelId): LanguageModelV3;\n\n /**\n * Creates a embedding model for text generation.\n */\n embeddingModel(modelId: DeepInfraEmbeddingModelId): EmbeddingModelV3;\n\n /**\n * @deprecated Use `embeddingModel` instead.\n */\n textEmbeddingModel(modelId: DeepInfraEmbeddingModelId): EmbeddingModelV3;\n}\n\nexport function createDeepInfra(\n options: DeepInfraProviderSettings = {},\n): DeepInfraProvider {\n const baseURL = withoutTrailingSlash(\n options.baseURL ?? 'https://api.deepinfra.com/v1',\n );\n const getHeaders = () =>\n withUserAgentSuffix(\n {\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'DEEPINFRA_API_KEY',\n description: \"DeepInfra's API key\",\n })}`,\n ...options.headers,\n },\n `ai-sdk/deepinfra/${VERSION}`,\n );\n\n interface CommonModelConfig {\n provider: string;\n url: ({ path }: { path: string }) => string;\n headers: () => Record;\n fetch?: FetchFunction;\n }\n\n const getCommonModelConfig = (modelType: string): CommonModelConfig => ({\n provider: `deepinfra.${modelType}`,\n url: ({ path }) => `${baseURL}/openai${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createChatModel = (modelId: DeepInfraChatModelId) => {\n return new DeepInfraChatLanguageModel(\n modelId,\n getCommonModelConfig('chat'),\n );\n };\n\n const createCompletionModel = (modelId: DeepInfraCompletionModelId) =>\n new OpenAICompatibleCompletionLanguageModel(\n modelId,\n getCommonModelConfig('completion'),\n );\n\n const createEmbeddingModel = (modelId: DeepInfraEmbeddingModelId) =>\n new OpenAICompatibleEmbeddingModel(\n modelId,\n getCommonModelConfig('embedding'),\n );\n\n const createImageModel = (modelId: DeepInfraImageModelId) =>\n new DeepInfraImageModel(modelId, {\n ...getCommonModelConfig('image'),\n baseURL: baseURL\n ? `${baseURL}/inference`\n : 'https://api.deepinfra.com/v1/inference',\n });\n\n const provider = (modelId: DeepInfraChatModelId) => createChatModel(modelId);\n\n provider.specificationVersion = 'v3' as const;\n provider.completionModel = createCompletionModel;\n provider.chatModel = createChatModel;\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n provider.languageModel = createChatModel;\n provider.embeddingModel = createEmbeddingModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n\n return provider;\n}\n\nexport const deepinfra = createDeepInfra();\n","import type {\n ImageModelV3,\n ImageModelV3File,\n SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n convertBase64ToUint8Array,\n convertToFormData,\n createJsonErrorResponseHandler,\n createJsonResponseHandler,\n downloadBlob,\n postFormDataToApi,\n postJsonToApi,\n type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport type { DeepInfraImageModelId } from './deepinfra-image-settings';\nimport { z } from 'zod/v4';\n\ninterface DeepInfraImageModelConfig {\n provider: string;\n baseURL: string;\n headers: () => Record;\n fetch?: FetchFunction;\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class DeepInfraImageModel implements ImageModelV3 {\n readonly specificationVersion = 'v3';\n readonly maxImagesPerCall = 1;\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: DeepInfraImageModelId,\n private config: DeepInfraImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n files,\n mask,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n\n // Image editing mode - use OpenAI-compatible /images/edits endpoint\n if (files != null && files.length > 0) {\n const { value: response, responseHeaders } = await postFormDataToApi({\n url: this.getEditUrl(),\n headers: combineHeaders(this.config.headers(), headers),\n formData: convertToFormData(\n {\n model: this.modelId,\n prompt,\n image: await Promise.all(files.map(file => fileToBlob(file))),\n mask: mask != null ? await fileToBlob(mask) : undefined,\n n,\n size,\n ...(providerOptions.deepinfra ?? {}),\n },\n { useArrayBrackets: false },\n ),\n failedResponseHandler: createJsonErrorResponseHandler({\n errorSchema: deepInfraEditErrorSchema,\n errorToMessage: error => error.error?.message ?? 'Unknown error',\n }),\n successfulResponseHandler: createJsonResponseHandler(\n deepInfraEditResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images: response.data.map(item => item.b64_json),\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n };\n }\n\n // Standard image generation mode\n // Some deepinfra models support size while others support aspect ratio.\n // Allow passing either and leave it up to the server to validate.\n const splitSize = size?.split('x');\n const { value: response, responseHeaders } = await postJsonToApi({\n url: `${this.config.baseURL}/${this.modelId}`,\n headers: combineHeaders(this.config.headers(), headers),\n body: {\n prompt,\n num_images: n,\n ...(aspectRatio && { aspect_ratio: aspectRatio }),\n ...(splitSize && { width: splitSize[0], height: splitSize[1] }),\n ...(seed != null && { seed }),\n ...(providerOptions.deepinfra ?? {}),\n },\n failedResponseHandler: createJsonErrorResponseHandler({\n errorSchema: deepInfraErrorSchema,\n errorToMessage: error => error.detail.error,\n }),\n successfulResponseHandler: createJsonResponseHandler(\n deepInfraImageResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n images: response.images.map(image =>\n image.replace(/^data:image\\/\\w+;base64,/, ''),\n ),\n warnings,\n response: {\n timestamp: currentDate,\n modelId: this.modelId,\n headers: responseHeaders,\n },\n };\n }\n\n private getEditUrl(): string {\n // Use OpenAI-compatible endpoint for image editing\n // baseURL is typically https://api.deepinfra.com/v1/inference\n // We need to use https://api.deepinfra.com/v1/openai/images/edits\n const baseUrl = this.config.baseURL.replace('/inference', '/openai');\n return `${baseUrl}/images/edits`;\n }\n}\n\nexport const deepInfraErrorSchema = z.object({\n detail: z.object({\n error: z.string(),\n }),\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nexport const deepInfraImageResponseSchema = z.object({\n images: z.array(z.string()),\n});\n\n// Schema for OpenAI-compatible image edit endpoint errors\nexport const deepInfraEditErrorSchema = z.object({\n error: z\n .object({\n message: z.string(),\n })\n .optional(),\n});\n\n// Schema for OpenAI-compatible image edit endpoint response\nexport const deepInfraEditResponseSchema = z.object({\n data: z.array(z.object({ b64_json: z.string() })),\n});\n\ntype DeepInfraFormDataInput = {\n model: string;\n prompt: string | undefined;\n image: Blob | Blob[];\n mask?: Blob;\n n: number;\n size: `${number}x${number}` | undefined;\n [key: string]: unknown;\n};\n\nasync function fileToBlob(file: ImageModelV3File): Promise {\n if (file.type === 'url') {\n return downloadBlob(file.url);\n }\n\n const data =\n file.data instanceof Uint8Array\n ? file.data\n : convertBase64ToUint8Array(file.data);\n\n return new Blob([data as BlobPart], { type: file.mediaType });\n}\n","import type {\n LanguageModelV3CallOptions,\n LanguageModelV3GenerateResult,\n LanguageModelV3StreamResult,\n} from '@ai-sdk/provider';\nimport { OpenAICompatibleChatLanguageModel } from '@ai-sdk/openai-compatible';\nimport type { FetchFunction } from '@ai-sdk/provider-utils';\n\ntype DeepInfraChatConfig = {\n provider: string;\n url: (options: { path: string; modelId?: string }) => string;\n headers: () => Record;\n fetch?: FetchFunction;\n};\n\nexport class DeepInfraChatLanguageModel extends OpenAICompatibleChatLanguageModel {\n constructor(modelId: string, config: DeepInfraChatConfig) {\n super(modelId, config);\n }\n\n /**\n * Fixes incorrect token usage for Gemini/Gemma models from DeepInfra.\n *\n * DeepInfra's API returns completion_tokens that don't include reasoning_tokens\n * for Gemini/Gemma models, which violates the OpenAI-compatible spec.\n * According to the spec, completion_tokens should include reasoning_tokens.\n *\n * Example of incorrect data from DeepInfra:\n * {\n * \"completion_tokens\": 84, // text-only tokens\n * \"completion_tokens_details\": {\n * \"reasoning_tokens\": 1081 // reasoning tokens not included above\n * }\n * }\n *\n * This would result in negative text tokens: 84 - 1081 = -997\n *\n * The fix: If reasoning_tokens > completion_tokens, add reasoning_tokens\n * to completion_tokens: 84 + 1081 = 1165\n */\n private fixUsageForGeminiModels(usage: any): typeof usage {\n if (!usage || !usage.completion_tokens_details?.reasoning_tokens) {\n return usage;\n }\n\n const completionTokens = usage.completion_tokens ?? 0;\n const reasoningTokens = usage.completion_tokens_details.reasoning_tokens;\n\n // If reasoning tokens exceed completion tokens, the API data is incorrect\n // DeepInfra is returning only text tokens in completion_tokens, not including reasoning\n if (reasoningTokens > completionTokens) {\n const correctedCompletionTokens = completionTokens + reasoningTokens;\n\n return {\n ...usage,\n // Add reasoning_tokens to completion_tokens to get the correct total\n completion_tokens: correctedCompletionTokens,\n // Update total_tokens if present\n total_tokens:\n usage.total_tokens != null\n ? usage.total_tokens + reasoningTokens\n : undefined,\n };\n }\n\n return usage;\n }\n\n async doGenerate(\n options: LanguageModelV3CallOptions,\n ): Promise {\n const result = await super.doGenerate(options);\n\n // Fix usage if needed\n if (result.usage?.raw) {\n const fixedRawUsage = this.fixUsageForGeminiModels(result.usage.raw);\n if (fixedRawUsage !== result.usage.raw) {\n // Recalculate usage with fixed data\n const promptTokens = fixedRawUsage.prompt_tokens ?? 0;\n const completionTokens = fixedRawUsage.completion_tokens ?? 0;\n const cacheReadTokens =\n fixedRawUsage.prompt_tokens_details?.cached_tokens ?? 0;\n const reasoningTokens =\n fixedRawUsage.completion_tokens_details?.reasoning_tokens ?? 0;\n\n return {\n ...result,\n usage: {\n inputTokens: {\n total: promptTokens,\n noCache: promptTokens - cacheReadTokens,\n cacheRead: cacheReadTokens,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: completionTokens,\n text: completionTokens - reasoningTokens,\n reasoning: reasoningTokens,\n },\n raw: fixedRawUsage,\n },\n };\n }\n }\n\n return result;\n }\n\n async doStream(\n options: LanguageModelV3CallOptions,\n ): Promise {\n const result = await super.doStream(options);\n\n // Wrap the stream to fix usage in the final chunk\n const originalStream = result.stream;\n const fixUsage = this.fixUsageForGeminiModels.bind(this);\n\n const transformedStream = new ReadableStream({\n async start(controller) {\n const reader = originalStream.getReader();\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n\n // Fix usage in finish chunks\n if (value.type === 'finish' && value.usage?.raw) {\n const fixedRawUsage = fixUsage(value.usage.raw);\n if (fixedRawUsage !== value.usage.raw) {\n const promptTokens = fixedRawUsage.prompt_tokens ?? 0;\n const completionTokens = fixedRawUsage.completion_tokens ?? 0;\n const cacheReadTokens =\n fixedRawUsage.prompt_tokens_details?.cached_tokens ?? 0;\n const reasoningTokens =\n fixedRawUsage.completion_tokens_details?.reasoning_tokens ??\n 0;\n\n controller.enqueue({\n ...value,\n usage: {\n inputTokens: {\n total: promptTokens,\n noCache: promptTokens - cacheReadTokens,\n cacheRead: cacheReadTokens,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: completionTokens,\n text: completionTokens - reasoningTokens,\n reasoning: reasoningTokens,\n },\n raw: fixedRawUsage,\n },\n });\n } else {\n controller.enqueue(value);\n }\n } else {\n controller.enqueue(value);\n }\n }\n controller.close();\n } catch (error) {\n controller.error(error);\n }\n },\n });\n\n return {\n ...result,\n stream: transformedStream,\n };\n 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