{"version":3,"sources":["../src/openai-provider.ts","../src/openai-chat-language-model.ts","../src/convert-to-openai-chat-messages.ts","../src/map-openai-chat-logprobs.ts","../src/map-openai-finish-reason.ts","../src/openai-error.ts","../src/get-response-metadata.ts","../src/openai-prepare-tools.ts","../src/openai-completion-language-model.ts","../src/convert-to-openai-completion-prompt.ts","../src/map-openai-completion-logprobs.ts","../src/openai-embedding-model.ts","../src/openai-image-model.ts","../src/openai-image-settings.ts"],"sourcesContent":["import {\n EmbeddingModelV1,\n ImageModelV1,\n LanguageModelV1,\n ProviderV1,\n} from '@ai-toolkit/provider';\nimport {\n FetchFunction,\n loadApiKey,\n withoutTrailingSlash,\n} from '@ai-toolkit/provider-utils';\nimport { OpenAIChatLanguageModel } from './openai-chat-language-model';\nimport { OpenAIChatModelId, OpenAIChatSettings } from './openai-chat-settings';\nimport { OpenAICompletionLanguageModel } from './openai-completion-language-model';\nimport {\n OpenAICompletionModelId,\n OpenAICompletionSettings,\n} from './openai-completion-settings';\nimport { OpenAIEmbeddingModel } from './openai-embedding-model';\nimport {\n OpenAIEmbeddingModelId,\n OpenAIEmbeddingSettings,\n} from './openai-embedding-settings';\nimport { OpenAIImageModel } from './openai-image-model';\nimport {\n OpenAIImageModelId,\n OpenAIImageSettings,\n} from './openai-image-settings';\n\nexport interface OpenAIProvider extends ProviderV1 {\n (\n modelId: 'gpt-3.5-turbo-instruct',\n settings?: OpenAICompletionSettings,\n ): OpenAICompletionLanguageModel;\n (modelId: OpenAIChatModelId, settings?: OpenAIChatSettings): LanguageModelV1;\n\n /**\nCreates an OpenAI model for text generation.\n */\n languageModel(\n modelId: 'gpt-3.5-turbo-instruct',\n settings?: OpenAICompletionSettings,\n ): OpenAICompletionLanguageModel;\n languageModel(\n modelId: OpenAIChatModelId,\n settings?: OpenAIChatSettings,\n ): LanguageModelV1;\n\n /**\nCreates an OpenAI chat model for text generation.\n */\n chat(\n modelId: OpenAIChatModelId,\n settings?: OpenAIChatSettings,\n ): LanguageModelV1;\n\n /**\nCreates an OpenAI completion model for text generation.\n */\n completion(\n modelId: OpenAICompletionModelId,\n settings?: OpenAICompletionSettings,\n ): LanguageModelV1;\n\n /**\nCreates a model for text embeddings.\n */\n embedding(\n modelId: OpenAIEmbeddingModelId,\n settings?: OpenAIEmbeddingSettings,\n ): EmbeddingModelV1;\n\n /**\nCreates a model for text embeddings.\n\n@deprecated Use `textEmbeddingModel` instead.\n */\n textEmbedding(\n modelId: OpenAIEmbeddingModelId,\n settings?: OpenAIEmbeddingSettings,\n ): EmbeddingModelV1;\n\n /**\nCreates a model for text embeddings.\n */\n textEmbeddingModel(\n modelId: OpenAIEmbeddingModelId,\n settings?: OpenAIEmbeddingSettings,\n ): EmbeddingModelV1;\n\n /**\nCreates a model for image generation.\n */\n image(\n modelId: OpenAIImageModelId,\n settings?: OpenAIImageSettings,\n ): ImageModelV1;\n\n /**\nCreates a model for image generation.\n */\n imageModel(\n modelId: OpenAIImageModelId,\n settings?: OpenAIImageSettings,\n ): ImageModelV1;\n}\n\nexport interface OpenAIProviderSettings {\n /**\nBase URL for the OpenAI API calls.\n */\n baseURL?: string;\n\n /**\nAPI key for authenticating requests.\n */\n apiKey?: string;\n\n /**\nOpenAI Organization.\n */\n organization?: string;\n\n /**\nOpenAI project.\n */\n project?: string;\n\n /**\nCustom headers to include in the requests.\n */\n headers?: Record;\n\n /**\nOpenAI compatibility mode. Should be set to `strict` when using the OpenAI API,\nand `compatible` when using 3rd party providers. In `compatible` mode, newer\ninformation such as streamOptions are not being sent. Defaults to 'compatible'.\n */\n compatibility?: 'strict' | 'compatible';\n\n /**\nProvider name. Overrides the `openai` default name for 3rd party providers.\n */\n name?: string;\n\n /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction;\n}\n\n/**\nCreate an OpenAI provider instance.\n */\nexport function createOpenAI(\n options: OpenAIProviderSettings = {},\n): OpenAIProvider {\n const baseURL =\n withoutTrailingSlash(options.baseURL) ?? 'https://api.openai.com/v1';\n\n // we default to compatible, because strict breaks providers like Groq:\n const compatibility = options.compatibility ?? 'compatible';\n\n const providerName = options.name ?? 'openai';\n\n const getHeaders = () => ({\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'OPENAI_API_KEY',\n description: 'OpenAI',\n })}`,\n 'OpenAI-Organization': options.organization,\n 'OpenAI-Project': options.project,\n ...options.headers,\n });\n\n const createChatModel = (\n modelId: OpenAIChatModelId,\n settings: OpenAIChatSettings = {},\n ) =>\n new OpenAIChatLanguageModel(modelId, settings, {\n provider: `${providerName}.chat`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n compatibility,\n fetch: options.fetch,\n });\n\n const createCompletionModel = (\n modelId: OpenAICompletionModelId,\n settings: OpenAICompletionSettings = {},\n ) =>\n new OpenAICompletionLanguageModel(modelId, settings, {\n provider: `${providerName}.completion`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n compatibility,\n fetch: options.fetch,\n });\n\n const createEmbeddingModel = (\n modelId: OpenAIEmbeddingModelId,\n settings: OpenAIEmbeddingSettings = {},\n ) =>\n new OpenAIEmbeddingModel(modelId, settings, {\n provider: `${providerName}.embedding`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createImageModel = (\n modelId: OpenAIImageModelId,\n settings: OpenAIImageSettings = {},\n ) =>\n new OpenAIImageModel(modelId, settings, {\n provider: `${providerName}.image`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n\n const createLanguageModel = (\n modelId: OpenAIChatModelId | OpenAICompletionModelId,\n settings?: OpenAIChatSettings | OpenAICompletionSettings,\n ) => {\n if (new.target) {\n throw new Error(\n 'The OpenAI model function cannot be called with the new keyword.',\n );\n }\n\n if (modelId === 'gpt-3.5-turbo-instruct') {\n return createCompletionModel(\n modelId,\n settings as OpenAICompletionSettings,\n );\n }\n\n return createChatModel(modelId, settings as OpenAIChatSettings);\n };\n\n const provider = function (\n modelId: OpenAIChatModelId | OpenAICompletionModelId,\n settings?: OpenAIChatSettings | OpenAICompletionSettings,\n ) {\n return createLanguageModel(modelId, settings);\n };\n\n provider.languageModel = createLanguageModel;\n provider.chat = createChatModel;\n provider.completion = createCompletionModel;\n provider.embedding = createEmbeddingModel;\n provider.textEmbedding = createEmbeddingModel;\n provider.textEmbeddingModel = createEmbeddingModel;\n\n provider.image = createImageModel;\n provider.imageModel = createImageModel;\n\n return provider as OpenAIProvider;\n}\n\n/**\nDefault OpenAI provider instance. It uses 'strict' compatibility mode.\n */\nexport const openai = createOpenAI({\n compatibility: 'strict', // strict for OpenAI API\n});\n","import {\n InvalidResponseDataError,\n LanguageModelV1,\n LanguageModelV1CallWarning,\n LanguageModelV1FinishReason,\n LanguageModelV1LogProbs,\n LanguageModelV1ProviderMetadata,\n LanguageModelV1StreamPart,\n UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport {\n FetchFunction,\n ParseResult,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n generateId,\n isParsableJson,\n postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport { convertToOpenAIChatMessages } from './convert-to-openai-chat-messages';\nimport { mapOpenAIChatLogProbsOutput } from './map-openai-chat-logprobs';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport { OpenAIChatModelId, OpenAIChatSettings } from './openai-chat-settings';\nimport {\n openaiErrorDataSchema,\n openaiFailedResponseHandler,\n} from './openai-error';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { prepareTools } from './openai-prepare-tools';\n\ntype OpenAIChatConfig = {\n provider: string;\n compatibility: 'strict' | 'compatible';\n headers: () => Record;\n url: (options: { modelId: string; path: string }) => string;\n fetch?: FetchFunction;\n};\n\nexport class OpenAIChatLanguageModel implements LanguageModelV1 {\n readonly specificationVersion = 'v1';\n\n readonly modelId: OpenAIChatModelId;\n readonly settings: OpenAIChatSettings;\n\n private readonly config: OpenAIChatConfig;\n\n constructor(\n modelId: OpenAIChatModelId,\n settings: OpenAIChatSettings,\n config: OpenAIChatConfig,\n ) {\n this.modelId = modelId;\n this.settings = settings;\n this.config = config;\n }\n\n get supportsStructuredOutputs(): boolean {\n // enable structured outputs for reasoning models by default:\n // TODO in the next major version, remove this and always use json mode for models\n // that support structured outputs (blacklist other models)\n return this.settings.structuredOutputs ?? isReasoningModel(this.modelId);\n }\n\n get defaultObjectGenerationMode() {\n // audio models don't support structured outputs:\n if (isAudioModel(this.modelId)) {\n return 'tool';\n }\n\n return this.supportsStructuredOutputs ? 'json' : 'tool';\n }\n\n get provider(): string {\n return this.config.provider;\n }\n\n get supportsImageUrls(): boolean {\n // image urls can be sent if downloadImages is disabled (default):\n return !this.settings.downloadImages;\n }\n\n private getArgs({\n mode,\n prompt,\n maxTokens,\n temperature,\n topP,\n topK,\n frequencyPenalty,\n presencePenalty,\n stopSequences,\n responseFormat,\n seed,\n providerMetadata,\n }: Parameters[0]) {\n const type = mode.type;\n\n const warnings: LanguageModelV1CallWarning[] = [];\n\n if (topK != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'topK',\n });\n }\n\n if (\n responseFormat?.type === 'json' &&\n responseFormat.schema != null &&\n !this.supportsStructuredOutputs\n ) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'responseFormat',\n details:\n 'JSON response format schema is only supported with structuredOutputs',\n });\n }\n\n const useLegacyFunctionCalling = this.settings.useLegacyFunctionCalling;\n\n if (useLegacyFunctionCalling && this.settings.parallelToolCalls === true) {\n throw new UnsupportedFunctionalityError({\n functionality: 'useLegacyFunctionCalling with parallelToolCalls',\n });\n }\n\n if (useLegacyFunctionCalling && this.supportsStructuredOutputs) {\n throw new UnsupportedFunctionalityError({\n functionality: 'structuredOutputs with useLegacyFunctionCalling',\n });\n }\n\n if (\n getSystemMessageMode(this.modelId) === 'remove' &&\n prompt.some(message => message.role === 'system')\n ) {\n warnings.push({\n type: 'other',\n message: 'system messages are removed for this model',\n });\n }\n\n const baseArgs = {\n // model id:\n model: this.modelId,\n\n // model specific settings:\n logit_bias: this.settings.logitBias,\n logprobs:\n this.settings.logprobs === true ||\n typeof this.settings.logprobs === 'number'\n ? true\n : undefined,\n top_logprobs:\n typeof this.settings.logprobs === 'number'\n ? this.settings.logprobs\n : typeof this.settings.logprobs === 'boolean'\n ? this.settings.logprobs\n ? 0\n : undefined\n : undefined,\n user: this.settings.user,\n parallel_tool_calls: this.settings.parallelToolCalls,\n\n // standardized settings:\n max_tokens: maxTokens,\n temperature,\n top_p: topP,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n response_format:\n responseFormat?.type === 'json'\n ? this.supportsStructuredOutputs && responseFormat.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: responseFormat.schema,\n strict: true,\n name: responseFormat.name ?? 'response',\n description: responseFormat.description,\n },\n }\n : { type: 'json_object' }\n : undefined,\n stop: stopSequences,\n seed,\n\n // openai specific settings:\n // TODO remove in next major version; we auto-map maxTokens now\n max_completion_tokens: providerMetadata?.openai?.maxCompletionTokens,\n store: providerMetadata?.openai?.store,\n metadata: providerMetadata?.openai?.metadata,\n prediction: providerMetadata?.openai?.prediction,\n reasoning_effort:\n providerMetadata?.openai?.reasoningEffort ??\n this.settings.reasoningEffort,\n\n // messages:\n messages: convertToOpenAIChatMessages({\n prompt,\n useLegacyFunctionCalling,\n systemMessageMode: getSystemMessageMode(this.modelId),\n }),\n };\n\n if (isReasoningModel(this.modelId)) {\n // remove unsupported settings for reasoning models\n // see https://platform.openai.com/docs/guides/reasoning#limitations\n if (baseArgs.temperature != null) {\n baseArgs.temperature = undefined;\n warnings.push({\n type: 'unsupported-setting',\n setting: 'temperature',\n details: 'temperature is not supported for reasoning models',\n });\n }\n if (baseArgs.top_p != null) {\n baseArgs.top_p = undefined;\n warnings.push({\n type: 'unsupported-setting',\n setting: 'topP',\n details: 'topP is not supported for reasoning models',\n });\n }\n if (baseArgs.frequency_penalty != null) {\n baseArgs.frequency_penalty = undefined;\n warnings.push({\n type: 'unsupported-setting',\n setting: 'frequencyPenalty',\n details: 'frequencyPenalty is not supported for reasoning models',\n });\n }\n if (baseArgs.presence_penalty != null) {\n baseArgs.presence_penalty = undefined;\n warnings.push({\n type: 'unsupported-setting',\n setting: 'presencePenalty',\n details: 'presencePenalty is not supported for reasoning models',\n });\n }\n if (baseArgs.logit_bias != null) {\n baseArgs.logit_bias = undefined;\n warnings.push({\n type: 'other',\n message: 'logitBias is not supported for reasoning models',\n });\n }\n if (baseArgs.logprobs != null) {\n baseArgs.logprobs = undefined;\n warnings.push({\n type: 'other',\n message: 'logprobs is not supported for reasoning models',\n });\n }\n if (baseArgs.top_logprobs != null) {\n baseArgs.top_logprobs = undefined;\n warnings.push({\n type: 'other',\n message: 'topLogprobs is not supported for reasoning models',\n });\n }\n\n // reasoning models use max_completion_tokens instead of max_tokens:\n if (baseArgs.max_tokens != null) {\n if (baseArgs.max_completion_tokens == null) {\n baseArgs.max_completion_tokens = baseArgs.max_tokens;\n }\n baseArgs.max_tokens = undefined;\n }\n }\n\n switch (type) {\n case 'regular': {\n const { tools, tool_choice, functions, function_call, toolWarnings } =\n prepareTools({\n mode,\n useLegacyFunctionCalling,\n structuredOutputs: this.supportsStructuredOutputs,\n });\n\n return {\n args: {\n ...baseArgs,\n tools,\n tool_choice,\n functions,\n function_call,\n },\n warnings: [...warnings, ...toolWarnings],\n };\n }\n\n case 'object-json': {\n return {\n args: {\n ...baseArgs,\n response_format:\n this.supportsStructuredOutputs && mode.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: mode.schema,\n strict: true,\n name: mode.name ?? 'response',\n description: mode.description,\n },\n }\n : { type: 'json_object' },\n },\n warnings,\n };\n }\n\n case 'object-tool': {\n return {\n args: useLegacyFunctionCalling\n ? {\n ...baseArgs,\n function_call: {\n name: mode.tool.name,\n },\n functions: [\n {\n name: mode.tool.name,\n description: mode.tool.description,\n parameters: mode.tool.parameters,\n },\n ],\n }\n : {\n ...baseArgs,\n tool_choice: {\n type: 'function',\n function: { name: mode.tool.name },\n },\n tools: [\n {\n type: 'function',\n function: {\n name: mode.tool.name,\n description: mode.tool.description,\n parameters: mode.tool.parameters,\n strict: this.supportsStructuredOutputs ? true : undefined,\n },\n },\n ],\n },\n warnings,\n };\n }\n\n default: {\n const _exhaustiveCheck: never = type;\n throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n }\n }\n }\n\n async doGenerate(\n options: Parameters[0],\n ): Promise>> {\n const { args: body, warnings } = this.getArgs(options);\n\n const {\n responseHeaders,\n value: response,\n rawValue: rawResponse,\n } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body,\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n openaiChatResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const { messages: rawPrompt, ...rawSettings } = body;\n const choice = response.choices[0];\n\n // provider metadata:\n const completionTokenDetails = response.usage?.completion_tokens_details;\n const promptTokenDetails = response.usage?.prompt_tokens_details;\n const providerMetadata: LanguageModelV1ProviderMetadata = { openai: {} };\n if (completionTokenDetails?.reasoning_tokens != null) {\n providerMetadata.openai.reasoningTokens =\n completionTokenDetails?.reasoning_tokens;\n }\n if (completionTokenDetails?.accepted_prediction_tokens != null) {\n providerMetadata.openai.acceptedPredictionTokens =\n completionTokenDetails?.accepted_prediction_tokens;\n }\n if (completionTokenDetails?.rejected_prediction_tokens != null) {\n providerMetadata.openai.rejectedPredictionTokens =\n completionTokenDetails?.rejected_prediction_tokens;\n }\n if (promptTokenDetails?.cached_tokens != null) {\n providerMetadata.openai.cachedPromptTokens =\n promptTokenDetails?.cached_tokens;\n }\n\n return {\n text: choice.message.content ?? undefined,\n toolCalls:\n this.settings.useLegacyFunctionCalling && choice.message.function_call\n ? [\n {\n toolCallType: 'function',\n toolCallId: generateId(),\n toolName: choice.message.function_call.name,\n args: choice.message.function_call.arguments,\n },\n ]\n : choice.message.tool_calls?.map(toolCall => ({\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args: toolCall.function.arguments!,\n })),\n finishReason: mapOpenAIFinishReason(choice.finish_reason),\n usage: {\n promptTokens: response.usage?.prompt_tokens ?? NaN,\n completionTokens: response.usage?.completion_tokens ?? NaN,\n },\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders, body: rawResponse },\n request: { body: JSON.stringify(body) },\n response: getResponseMetadata(response),\n warnings,\n logprobs: mapOpenAIChatLogProbsOutput(choice.logprobs),\n providerMetadata,\n };\n }\n\n async doStream(\n options: Parameters[0],\n ): Promise>> {\n if (this.settings.simulateStreaming) {\n const result = await this.doGenerate(options);\n\n const simulatedStream = new ReadableStream({\n start(controller) {\n controller.enqueue({ type: 'response-metadata', ...result.response });\n if (result.text) {\n controller.enqueue({\n type: 'text-delta',\n textDelta: result.text,\n });\n }\n if (result.toolCalls) {\n for (const toolCall of result.toolCalls) {\n controller.enqueue({\n type: 'tool-call-delta',\n toolCallType: 'function',\n toolCallId: toolCall.toolCallId,\n toolName: toolCall.toolName,\n argsTextDelta: toolCall.args,\n });\n\n controller.enqueue({\n type: 'tool-call',\n ...toolCall,\n });\n }\n }\n controller.enqueue({\n type: 'finish',\n finishReason: result.finishReason,\n usage: result.usage,\n logprobs: result.logprobs,\n providerMetadata: result.providerMetadata,\n });\n controller.close();\n },\n });\n return {\n stream: simulatedStream,\n rawCall: result.rawCall,\n rawResponse: result.rawResponse,\n warnings: result.warnings,\n };\n }\n\n const { args, warnings } = this.getArgs(options);\n\n const body = {\n ...args,\n stream: true,\n\n // only include stream_options when in strict compatibility mode:\n stream_options:\n this.config.compatibility === 'strict'\n ? { include_usage: true }\n : undefined,\n };\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body,\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createEventSourceResponseHandler(\n openaiChatChunkSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const { messages: rawPrompt, ...rawSettings } = args;\n\n const toolCalls: Array<{\n id: string;\n type: 'function';\n function: {\n name: string;\n arguments: string;\n };\n hasFinished: boolean;\n }> = [];\n\n let finishReason: LanguageModelV1FinishReason = 'unknown';\n let usage: {\n promptTokens: number | undefined;\n completionTokens: number | undefined;\n } = {\n promptTokens: undefined,\n completionTokens: undefined,\n };\n let logprobs: LanguageModelV1LogProbs;\n let isFirstChunk = true;\n\n const { useLegacyFunctionCalling } = this.settings;\n\n const providerMetadata: LanguageModelV1ProviderMetadata = { openai: {} };\n\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult>,\n LanguageModelV1StreamPart\n >({\n transform(chunk, controller) {\n // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = 'error';\n controller.enqueue({ type: 'error', error: chunk.error });\n return;\n }\n\n const value = chunk.value;\n\n // handle error chunks:\n if ('error' in value) {\n finishReason = 'error';\n controller.enqueue({ type: 'error', error: value.error });\n return;\n }\n\n if (isFirstChunk) {\n isFirstChunk = false;\n\n controller.enqueue({\n type: 'response-metadata',\n ...getResponseMetadata(value),\n });\n }\n\n if (value.usage != null) {\n const {\n prompt_tokens,\n completion_tokens,\n prompt_tokens_details,\n completion_tokens_details,\n } = value.usage;\n\n usage = {\n promptTokens: prompt_tokens ?? undefined,\n completionTokens: completion_tokens ?? undefined,\n };\n\n if (completion_tokens_details?.reasoning_tokens != null) {\n providerMetadata.openai.reasoningTokens =\n completion_tokens_details?.reasoning_tokens;\n }\n if (\n completion_tokens_details?.accepted_prediction_tokens != null\n ) {\n providerMetadata.openai.acceptedPredictionTokens =\n completion_tokens_details?.accepted_prediction_tokens;\n }\n if (\n completion_tokens_details?.rejected_prediction_tokens != null\n ) {\n providerMetadata.openai.rejectedPredictionTokens =\n completion_tokens_details?.rejected_prediction_tokens;\n }\n if (prompt_tokens_details?.cached_tokens != null) {\n providerMetadata.openai.cachedPromptTokens =\n prompt_tokens_details?.cached_tokens;\n }\n }\n\n const choice = value.choices[0];\n\n if (choice?.finish_reason != null) {\n finishReason = mapOpenAIFinishReason(choice.finish_reason);\n }\n\n if (choice?.delta == null) {\n return;\n }\n\n const delta = choice.delta;\n\n if (delta.content != null) {\n controller.enqueue({\n type: 'text-delta',\n textDelta: delta.content,\n });\n }\n\n const mappedLogprobs = mapOpenAIChatLogProbsOutput(\n choice?.logprobs,\n );\n if (mappedLogprobs?.length) {\n if (logprobs === undefined) logprobs = [];\n logprobs.push(...mappedLogprobs);\n }\n\n const mappedToolCalls: typeof delta.tool_calls =\n useLegacyFunctionCalling && delta.function_call != null\n ? [\n {\n type: 'function',\n id: generateId(),\n function: delta.function_call,\n index: 0,\n },\n ]\n : delta.tool_calls;\n\n if (mappedToolCalls != null) {\n for (const toolCallDelta of mappedToolCalls) {\n const index = toolCallDelta.index;\n\n // Tool call start. OpenAI returns all information except the arguments in the first chunk.\n if (toolCalls[index] == null) {\n if (toolCallDelta.type !== 'function') {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'function' type.`,\n });\n }\n\n if (toolCallDelta.id == null) {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'id' to be a string.`,\n });\n }\n\n if (toolCallDelta.function?.name == null) {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'function.name' to be a string.`,\n });\n }\n\n toolCalls[index] = {\n id: toolCallDelta.id,\n type: 'function',\n function: {\n name: toolCallDelta.function.name,\n arguments: toolCallDelta.function.arguments ?? '',\n },\n hasFinished: false,\n };\n\n const toolCall = toolCalls[index];\n\n if (\n toolCall.function?.name != null &&\n toolCall.function?.arguments != null\n ) {\n // send delta if the argument text has already started:\n if (toolCall.function.arguments.length > 0) {\n controller.enqueue({\n type: 'tool-call-delta',\n toolCallType: 'function',\n toolCallId: toolCall.id,\n toolName: toolCall.function.name,\n argsTextDelta: toolCall.function.arguments,\n });\n }\n\n // check if tool call is complete\n // (some providers send the full tool call in one chunk):\n if (isParsableJson(toolCall.function.arguments)) {\n controller.enqueue({\n type: 'tool-call',\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args: toolCall.function.arguments,\n });\n toolCall.hasFinished = true;\n }\n }\n\n continue;\n }\n\n // existing tool call, merge if not finished\n const toolCall = toolCalls[index];\n\n if (toolCall.hasFinished) {\n continue;\n }\n\n if (toolCallDelta.function?.arguments != null) {\n toolCall.function!.arguments +=\n toolCallDelta.function?.arguments ?? '';\n }\n\n // send delta\n controller.enqueue({\n type: 'tool-call-delta',\n toolCallType: 'function',\n toolCallId: toolCall.id,\n toolName: toolCall.function.name,\n argsTextDelta: toolCallDelta.function.arguments ?? '',\n });\n\n // check if tool call is complete\n if (\n toolCall.function?.name != null &&\n toolCall.function?.arguments != null &&\n isParsableJson(toolCall.function.arguments)\n ) {\n controller.enqueue({\n type: 'tool-call',\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args: toolCall.function.arguments,\n });\n toolCall.hasFinished = true;\n }\n }\n }\n },\n\n flush(controller) {\n controller.enqueue({\n type: 'finish',\n finishReason,\n logprobs,\n usage: {\n promptTokens: usage.promptTokens ?? NaN,\n completionTokens: usage.completionTokens ?? NaN,\n },\n ...(providerMetadata != null ? { providerMetadata } : {}),\n });\n },\n }),\n ),\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders },\n request: { body: JSON.stringify(body) },\n warnings,\n };\n }\n}\n\nconst openaiTokenUsageSchema = z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n prompt_tokens_details: z\n .object({\n cached_tokens: z.number().nullish(),\n })\n .nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n accepted_prediction_tokens: z.number().nullish(),\n rejected_prediction_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish();\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\nconst openaiChatResponseSchema = z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n message: z.object({\n role: z.literal('assistant').nullish(),\n content: z.string().nullish(),\n function_call: z\n .object({\n arguments: z.string(),\n name: z.string(),\n })\n .nullish(),\n tool_calls: z\n .array(\n z.object({\n id: z.string().nullish(),\n type: z.literal('function'),\n function: z.object({\n name: z.string(),\n arguments: z.string(),\n }),\n }),\n )\n .nullish(),\n }),\n index: z.number(),\n logprobs: z\n .object({\n content: z\n .array(\n z.object({\n token: z.string(),\n logprob: z.number(),\n top_logprobs: z.array(\n z.object({\n token: z.string(),\n logprob: z.number(),\n }),\n ),\n }),\n )\n .nullable(),\n })\n .nullish(),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: openaiTokenUsageSchema,\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\nconst openaiChatChunkSchema = z.union([\n z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n delta: z\n .object({\n role: z.enum(['assistant']).nullish(),\n content: z.string().nullish(),\n function_call: z\n .object({\n name: z.string().optional(),\n arguments: z.string().optional(),\n })\n .nullish(),\n tool_calls: z\n .array(\n z.object({\n index: z.number(),\n id: z.string().nullish(),\n type: z.literal('function').optional(),\n function: z.object({\n name: z.string().nullish(),\n arguments: z.string().nullish(),\n }),\n }),\n )\n .nullish(),\n })\n .nullish(),\n logprobs: z\n .object({\n content: z\n .array(\n z.object({\n token: z.string(),\n logprob: z.number(),\n top_logprobs: z.array(\n z.object({\n token: z.string(),\n logprob: z.number(),\n }),\n ),\n }),\n )\n .nullable(),\n })\n .nullish(),\n finish_reason: z.string().nullable().optional(),\n index: z.number(),\n }),\n ),\n usage: openaiTokenUsageSchema,\n }),\n openaiErrorDataSchema,\n]);\n\nfunction isReasoningModel(modelId: string) {\n return (\n modelId === 'o1' ||\n modelId.startsWith('o1-') ||\n modelId === 'o3' ||\n modelId.startsWith('o3-')\n );\n}\n\nfunction isAudioModel(modelId: string) {\n return modelId.startsWith('gpt-4o-audio-preview');\n}\n\nfunction getSystemMessageMode(modelId: string) {\n if (!isReasoningModel(modelId)) {\n return 'system';\n }\n\n return (\n reasoningModels[modelId as keyof typeof reasoningModels]\n ?.systemMessageMode ?? 'developer'\n );\n}\n\nconst reasoningModels = {\n 'o1-mini': {\n systemMessageMode: 'remove',\n },\n 'o1-mini-2024-09-12': {\n systemMessageMode: 'remove',\n },\n 'o1-preview': {\n systemMessageMode: 'remove',\n },\n 'o1-preview-2024-09-12': {\n systemMessageMode: 'remove',\n },\n 'o3-mini': {\n systemMessageMode: 'developer',\n },\n 'o3-mini-2025-01-31': {\n systemMessageMode: 'developer',\n },\n} as const;\n","import {\n LanguageModelV1Prompt,\n UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-toolkit/provider-utils';\nimport { OpenAIChatPrompt } from './openai-chat-prompt';\n\nexport function convertToOpenAIChatMessages({\n prompt,\n useLegacyFunctionCalling = false,\n systemMessageMode = 'system',\n}: {\n prompt: LanguageModelV1Prompt;\n useLegacyFunctionCalling?: boolean;\n systemMessageMode?: 'system' | 'developer' | 'remove';\n}): OpenAIChatPrompt {\n const messages: OpenAIChatPrompt = [];\n\n for (const { role, content } of prompt) {\n switch (role) {\n case 'system': {\n switch (systemMessageMode) {\n case 'system': {\n messages.push({ role: 'system', content });\n break;\n }\n case 'developer': {\n messages.push({ role: 'developer', content });\n break;\n }\n case 'remove': {\n break;\n }\n default: {\n const _exhaustiveCheck: never = systemMessageMode;\n throw new Error(\n `Unsupported system message mode: ${_exhaustiveCheck}`,\n );\n }\n }\n break;\n }\n\n case 'user': {\n if (content.length === 1 && content[0].type === 'text') {\n messages.push({ role: 'user', content: content[0].text });\n break;\n }\n\n messages.push({\n role: 'user',\n content: content.map(part => {\n switch (part.type) {\n case 'text': {\n return { type: 'text', text: part.text };\n }\n case 'image': {\n return {\n type: 'image_url',\n image_url: {\n url:\n part.image instanceof URL\n ? part.image.toString()\n : `data:${\n part.mimeType ?? 'image/jpeg'\n };base64,${convertUint8ArrayToBase64(part.image)}`,\n\n // OpenAI specific extension: image detail\n detail: part.providerMetadata?.openai?.imageDetail,\n },\n };\n }\n case 'file': {\n if (part.data instanceof URL) {\n throw new UnsupportedFunctionalityError({\n functionality:\n \"'File content parts with URL data' functionality not supported.\",\n });\n }\n\n switch (part.mimeType) {\n case 'audio/wav': {\n return {\n type: 'input_audio',\n input_audio: { data: part.data, format: 'wav' },\n };\n }\n case 'audio/mp3':\n case 'audio/mpeg': {\n return {\n type: 'input_audio',\n input_audio: { data: part.data, format: 'mp3' },\n };\n }\n\n default: {\n throw new UnsupportedFunctionalityError({\n functionality: `File content part type ${part.mimeType} in user messages`,\n });\n }\n }\n }\n }\n }),\n });\n\n break;\n }\n\n case 'assistant': {\n let text = '';\n const toolCalls: Array<{\n id: string;\n type: 'function';\n function: { name: string; arguments: string };\n }> = [];\n\n for (const part of content) {\n switch (part.type) {\n case 'text': {\n text += part.text;\n break;\n }\n case 'redacted-reasoning':\n case 'reasoning': {\n break; // ignored\n }\n case 'tool-call': {\n toolCalls.push({\n id: part.toolCallId,\n type: 'function',\n function: {\n name: part.toolName,\n arguments: JSON.stringify(part.args),\n },\n });\n break;\n }\n default: {\n const _exhaustiveCheck: never = part;\n throw new Error(`Unsupported part: ${_exhaustiveCheck}`);\n }\n }\n }\n\n if (useLegacyFunctionCalling) {\n if (toolCalls.length > 1) {\n throw new UnsupportedFunctionalityError({\n functionality:\n 'useLegacyFunctionCalling with multiple tool calls in one message',\n });\n }\n\n messages.push({\n role: 'assistant',\n content: text,\n function_call:\n toolCalls.length > 0 ? toolCalls[0].function : undefined,\n });\n } else {\n messages.push({\n role: 'assistant',\n content: text,\n tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n });\n }\n\n break;\n }\n\n case 'tool': {\n for (const toolResponse of content) {\n if (useLegacyFunctionCalling) {\n messages.push({\n role: 'function',\n name: toolResponse.toolName,\n content: JSON.stringify(toolResponse.result),\n });\n } else {\n messages.push({\n role: 'tool',\n tool_call_id: toolResponse.toolCallId,\n content: JSON.stringify(toolResponse.result),\n });\n }\n }\n break;\n }\n\n default: {\n const _exhaustiveCheck: never = role;\n throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n }\n }\n }\n\n return messages;\n}\n","import { LanguageModelV1LogProbs } from '@ai-toolkit/provider';\n\ntype OpenAIChatLogProbs = {\n content:\n | {\n token: string;\n logprob: number;\n top_logprobs:\n | {\n token: string;\n logprob: number;\n }[]\n | null;\n }[]\n | null;\n};\n\nexport function mapOpenAIChatLogProbsOutput(\n logprobs: OpenAIChatLogProbs | null | undefined,\n): LanguageModelV1LogProbs | undefined {\n return (\n logprobs?.content?.map(({ token, logprob, top_logprobs }) => ({\n token,\n logprob,\n topLogprobs: top_logprobs\n ? top_logprobs.map(({ token, logprob }) => ({\n token,\n logprob,\n }))\n : [],\n })) ?? undefined\n );\n}\n","import { LanguageModelV1FinishReason } from '@ai-toolkit/provider';\n\nexport function mapOpenAIFinishReason(\n finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\n switch (finishReason) {\n case 'stop':\n return 'stop';\n case 'length':\n return 'length';\n case 'content_filter':\n return 'content-filter';\n case 'function_call':\n case 'tool_calls':\n return 'tool-calls';\n default:\n return 'unknown';\n }\n}\n","import { z } from 'zod';\nimport { createJsonErrorResponseHandler } from '@ai-toolkit/provider-utils';\n\nexport const openaiErrorDataSchema = z.object({\n error: z.object({\n message: z.string(),\n\n // The additional information below is handled loosely to support\n // OpenAI-compatible providers that have slightly different error\n // responses:\n type: z.string().nullish(),\n param: z.any().nullish(),\n code: z.union([z.string(), z.number()]).nullish(),\n }),\n});\n\nexport type OpenAIErrorData = z.infer;\n\nexport const openaiFailedResponseHandler = createJsonErrorResponseHandler({\n errorSchema: openaiErrorDataSchema,\n errorToMessage: data => data.error.message,\n});\n","export function getResponseMetadata({\n id,\n model,\n created,\n}: {\n id?: string | undefined | null;\n created?: number | undefined | null;\n model?: string | undefined | null;\n}) {\n return {\n id: id ?? undefined,\n modelId: model ?? undefined,\n timestamp: created != null ? new Date(created * 1000) : undefined,\n };\n}\n","import {\n JSONSchema7,\n LanguageModelV1,\n LanguageModelV1CallWarning,\n UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\n\nexport function prepareTools({\n mode,\n useLegacyFunctionCalling = false,\n structuredOutputs,\n}: {\n mode: Parameters[0]['mode'] & {\n type: 'regular';\n };\n useLegacyFunctionCalling: boolean | undefined;\n structuredOutputs: boolean;\n}): {\n tools?: {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: JSONSchema7;\n strict?: boolean;\n };\n }[];\n tool_choice?:\n | 'auto'\n | 'none'\n | 'required'\n | { type: 'function'; function: { name: string } };\n\n // legacy support\n functions?: {\n name: string;\n description: string | undefined;\n parameters: JSONSchema7;\n }[];\n function_call?: { name: string };\n\n toolWarnings: LanguageModelV1CallWarning[];\n} {\n // when the tools array is empty, change it to undefined to prevent errors:\n const tools = mode.tools?.length ? mode.tools : undefined;\n\n const toolWarnings: LanguageModelV1CallWarning[] = [];\n\n if (tools == null) {\n return { tools: undefined, tool_choice: undefined, toolWarnings };\n }\n\n const toolChoice = mode.toolChoice;\n\n if (useLegacyFunctionCalling) {\n const openaiFunctions: Array<{\n name: string;\n description: string | undefined;\n parameters: JSONSchema7;\n }> = [];\n\n for (const tool of tools) {\n if (tool.type === 'provider-defined') {\n toolWarnings.push({ type: 'unsupported-tool', tool });\n } else {\n openaiFunctions.push({\n name: tool.name,\n description: tool.description,\n parameters: tool.parameters,\n });\n }\n }\n\n if (toolChoice == null) {\n return {\n functions: openaiFunctions,\n function_call: undefined,\n toolWarnings,\n };\n }\n\n const type = toolChoice.type;\n\n switch (type) {\n case 'auto':\n case 'none':\n case undefined:\n return {\n functions: openaiFunctions,\n function_call: undefined,\n toolWarnings,\n };\n case 'required':\n throw new UnsupportedFunctionalityError({\n functionality: 'useLegacyFunctionCalling and toolChoice: required',\n });\n default:\n return {\n functions: openaiFunctions,\n function_call: { name: toolChoice.toolName },\n toolWarnings,\n };\n }\n }\n\n const openaiTools: Array<{\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: JSONSchema7;\n strict: boolean | undefined;\n };\n }> = [];\n\n for (const tool of tools) {\n if (tool.type === 'provider-defined') {\n toolWarnings.push({ type: 'unsupported-tool', tool });\n } else {\n openaiTools.push({\n type: 'function',\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.parameters,\n strict: structuredOutputs ? true : undefined,\n },\n });\n }\n }\n\n if (toolChoice == null) {\n return { tools: openaiTools, tool_choice: undefined, toolWarnings };\n }\n\n const type = toolChoice.type;\n\n switch (type) {\n case 'auto':\n case 'none':\n case 'required':\n return { tools: openaiTools, tool_choice: type, toolWarnings };\n case 'tool':\n return {\n tools: openaiTools,\n tool_choice: {\n type: 'function',\n function: {\n name: toolChoice.toolName,\n },\n },\n toolWarnings,\n };\n default: {\n const _exhaustiveCheck: never = type;\n throw new UnsupportedFunctionalityError({\n functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n });\n }\n }\n}\n","import {\n LanguageModelV1,\n LanguageModelV1CallWarning,\n LanguageModelV1FinishReason,\n LanguageModelV1LogProbs,\n LanguageModelV1StreamPart,\n UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport {\n FetchFunction,\n ParseResult,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport { convertToOpenAICompletionPrompt } from './convert-to-openai-completion-prompt';\nimport { mapOpenAICompletionLogProbs } from './map-openai-completion-logprobs';\nimport { mapOpenAIFinishReason } from './map-openai-finish-reason';\nimport {\n OpenAICompletionModelId,\n OpenAICompletionSettings,\n} from './openai-completion-settings';\nimport {\n openaiErrorDataSchema,\n openaiFailedResponseHandler,\n} from './openai-error';\nimport { getResponseMetadata } from './get-response-metadata';\n\ntype OpenAICompletionConfig = {\n provider: string;\n compatibility: 'strict' | 'compatible';\n headers: () => Record;\n url: (options: { modelId: string; path: string }) => string;\n fetch?: FetchFunction;\n};\n\nexport class OpenAICompletionLanguageModel implements LanguageModelV1 {\n readonly specificationVersion = 'v1';\n readonly defaultObjectGenerationMode = undefined;\n\n readonly modelId: OpenAICompletionModelId;\n readonly settings: OpenAICompletionSettings;\n\n private readonly config: OpenAICompletionConfig;\n\n constructor(\n modelId: OpenAICompletionModelId,\n settings: OpenAICompletionSettings,\n config: OpenAICompletionConfig,\n ) {\n this.modelId = modelId;\n this.settings = settings;\n this.config = config;\n }\n\n get provider(): string {\n return this.config.provider;\n }\n\n private getArgs({\n mode,\n inputFormat,\n prompt,\n maxTokens,\n temperature,\n topP,\n topK,\n frequencyPenalty,\n presencePenalty,\n stopSequences: userStopSequences,\n responseFormat,\n seed,\n }: Parameters[0]) {\n const type = mode.type;\n\n const warnings: LanguageModelV1CallWarning[] = [];\n\n if (topK != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'topK',\n });\n }\n\n if (responseFormat != null && responseFormat.type !== 'text') {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'responseFormat',\n details: 'JSON response format is not supported.',\n });\n }\n\n const { prompt: completionPrompt, stopSequences } =\n convertToOpenAICompletionPrompt({ prompt, inputFormat });\n\n const stop = [...(stopSequences ?? []), ...(userStopSequences ?? [])];\n\n const baseArgs = {\n // model id:\n model: this.modelId,\n\n // model specific settings:\n echo: this.settings.echo,\n logit_bias: this.settings.logitBias,\n logprobs:\n typeof this.settings.logprobs === 'number'\n ? this.settings.logprobs\n : typeof this.settings.logprobs === 'boolean'\n ? this.settings.logprobs\n ? 0\n : undefined\n : undefined,\n suffix: this.settings.suffix,\n user: this.settings.user,\n\n // standardized settings:\n max_tokens: maxTokens,\n temperature,\n top_p: topP,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n seed,\n\n // prompt:\n prompt: completionPrompt,\n\n // stop sequences:\n stop: stop.length > 0 ? stop : undefined,\n };\n\n switch (type) {\n case 'regular': {\n if (mode.tools?.length) {\n throw new UnsupportedFunctionalityError({\n functionality: 'tools',\n });\n }\n\n if (mode.toolChoice) {\n throw new UnsupportedFunctionalityError({\n functionality: 'toolChoice',\n });\n }\n\n return { args: baseArgs, warnings };\n }\n\n case 'object-json': {\n throw new UnsupportedFunctionalityError({\n functionality: 'object-json mode',\n });\n }\n\n case 'object-tool': {\n throw new UnsupportedFunctionalityError({\n functionality: 'object-tool mode',\n });\n }\n\n default: {\n const _exhaustiveCheck: never = type;\n throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n }\n }\n }\n\n async doGenerate(\n options: Parameters[0],\n ): Promise>> {\n const { args, warnings } = this.getArgs(options);\n\n const {\n responseHeaders,\n value: response,\n rawValue: rawResponse,\n } = await postJsonToApi({\n url: this.config.url({\n path: '/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body: args,\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n openaiCompletionResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const { prompt: rawPrompt, ...rawSettings } = args;\n const choice = response.choices[0];\n\n return {\n text: choice.text,\n usage: {\n promptTokens: response.usage.prompt_tokens,\n completionTokens: response.usage.completion_tokens,\n },\n finishReason: mapOpenAIFinishReason(choice.finish_reason),\n logprobs: mapOpenAICompletionLogProbs(choice.logprobs),\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders, body: rawResponse },\n response: getResponseMetadata(response),\n warnings,\n request: { body: JSON.stringify(args) },\n };\n }\n\n async doStream(\n options: Parameters[0],\n ): Promise>> {\n const { args, warnings } = this.getArgs(options);\n\n const body = {\n ...args,\n stream: true,\n\n // only include stream_options when in strict compatibility mode:\n stream_options:\n this.config.compatibility === 'strict'\n ? { include_usage: true }\n : undefined,\n };\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body,\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createEventSourceResponseHandler(\n openaiCompletionChunkSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const { prompt: rawPrompt, ...rawSettings } = args;\n\n let finishReason: LanguageModelV1FinishReason = 'unknown';\n let usage: { promptTokens: number; completionTokens: number } = {\n promptTokens: Number.NaN,\n completionTokens: Number.NaN,\n };\n let logprobs: LanguageModelV1LogProbs;\n let isFirstChunk = true;\n\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult>,\n LanguageModelV1StreamPart\n >({\n transform(chunk, controller) {\n // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = 'error';\n controller.enqueue({ type: 'error', error: chunk.error });\n return;\n }\n\n const value = chunk.value;\n\n // handle error chunks:\n if ('error' in value) {\n finishReason = 'error';\n controller.enqueue({ type: 'error', error: value.error });\n return;\n }\n\n if (isFirstChunk) {\n isFirstChunk = false;\n\n controller.enqueue({\n type: 'response-metadata',\n ...getResponseMetadata(value),\n });\n }\n\n if (value.usage != null) {\n usage = {\n promptTokens: value.usage.prompt_tokens,\n completionTokens: value.usage.completion_tokens,\n };\n }\n\n const choice = value.choices[0];\n\n if (choice?.finish_reason != null) {\n finishReason = mapOpenAIFinishReason(choice.finish_reason);\n }\n\n if (choice?.text != null) {\n controller.enqueue({\n type: 'text-delta',\n textDelta: choice.text,\n });\n }\n\n const mappedLogprobs = mapOpenAICompletionLogProbs(\n choice?.logprobs,\n );\n if (mappedLogprobs?.length) {\n if (logprobs === undefined) logprobs = [];\n logprobs.push(...mappedLogprobs);\n }\n },\n\n flush(controller) {\n controller.enqueue({\n type: 'finish',\n finishReason,\n logprobs,\n usage,\n });\n },\n }),\n ),\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders },\n warnings,\n request: { body: JSON.stringify(body) },\n };\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\nconst openaiCompletionResponseSchema = z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n text: z.string(),\n finish_reason: z.string(),\n logprobs: z\n .object({\n tokens: z.array(z.string()),\n token_logprobs: z.array(z.number()),\n top_logprobs: z.array(z.record(z.string(), z.number())).nullable(),\n })\n .nullish(),\n }),\n ),\n usage: z.object({\n prompt_tokens: z.number(),\n completion_tokens: z.number(),\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\nconst openaiCompletionChunkSchema = z.union([\n z.object({\n id: z.string().nullish(),\n created: z.number().nullish(),\n model: z.string().nullish(),\n choices: z.array(\n z.object({\n text: z.string(),\n finish_reason: z.string().nullish(),\n index: z.number(),\n logprobs: z\n .object({\n tokens: z.array(z.string()),\n token_logprobs: z.array(z.number()),\n top_logprobs: z.array(z.record(z.string(), z.number())).nullable(),\n })\n .nullish(),\n }),\n ),\n usage: z\n .object({\n prompt_tokens: z.number(),\n completion_tokens: z.number(),\n })\n .nullish(),\n }),\n openaiErrorDataSchema,\n]);\n","import {\n InvalidPromptError,\n LanguageModelV1Prompt,\n UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\n\nexport function convertToOpenAICompletionPrompt({\n prompt,\n inputFormat,\n user = 'user',\n assistant = 'assistant',\n}: {\n prompt: LanguageModelV1Prompt;\n inputFormat: 'prompt' | 'messages';\n user?: string;\n assistant?: string;\n}): {\n prompt: string;\n stopSequences?: string[];\n} {\n // When the user supplied a prompt input, we don't transform it:\n if (\n inputFormat === 'prompt' &&\n prompt.length === 1 &&\n prompt[0].role === 'user' &&\n prompt[0].content.length === 1 &&\n prompt[0].content[0].type === 'text'\n ) {\n return { prompt: prompt[0].content[0].text };\n }\n\n // otherwise transform to a chat message format:\n let text = '';\n\n // if first message is a system message, add it to the text:\n if (prompt[0].role === 'system') {\n text += `${prompt[0].content}\\n\\n`;\n prompt = prompt.slice(1);\n }\n\n for (const { role, content } of prompt) {\n switch (role) {\n case 'system': {\n throw new InvalidPromptError({\n message: 'Unexpected system message in prompt: ${content}',\n prompt,\n });\n }\n\n case 'user': {\n const userMessage = content\n .map(part => {\n switch (part.type) {\n case 'text': {\n return part.text;\n }\n case 'image': {\n throw new UnsupportedFunctionalityError({\n functionality: 'images',\n });\n }\n }\n })\n .join('');\n\n text += `${user}:\\n${userMessage}\\n\\n`;\n break;\n }\n\n case 'assistant': {\n const assistantMessage = content\n .map(part => {\n switch (part.type) {\n case 'text': {\n return part.text;\n }\n case 'tool-call': {\n throw new UnsupportedFunctionalityError({\n functionality: 'tool-call messages',\n });\n }\n }\n })\n .join('');\n\n text += `${assistant}:\\n${assistantMessage}\\n\\n`;\n break;\n }\n\n case 'tool': {\n throw new UnsupportedFunctionalityError({\n functionality: 'tool messages',\n });\n }\n\n default: {\n const _exhaustiveCheck: never = role;\n throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n }\n }\n }\n\n // Assistant message prefix:\n text += `${assistant}:\\n`;\n\n return {\n prompt: text,\n stopSequences: [`\\n${user}:`],\n };\n}\n","import { LanguageModelV1LogProbs } from '@ai-toolkit/provider';\n\ntype OpenAICompletionLogProps = {\n tokens: string[];\n token_logprobs: number[];\n top_logprobs: Record[] | null;\n};\n\nexport function mapOpenAICompletionLogProbs(\n logprobs: OpenAICompletionLogProps | null | undefined,\n): LanguageModelV1LogProbs | undefined {\n return logprobs?.tokens.map((token, index) => ({\n token,\n logprob: logprobs.token_logprobs[index],\n topLogprobs: logprobs.top_logprobs\n ? Object.entries(logprobs.top_logprobs[index]).map(\n ([token, logprob]) => ({\n token,\n logprob,\n }),\n )\n : [],\n }));\n}\n","import {\n EmbeddingModelV1,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-toolkit/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport { OpenAIConfig } from './openai-config';\nimport {\n OpenAIEmbeddingModelId,\n OpenAIEmbeddingSettings,\n} from './openai-embedding-settings';\nimport { openaiFailedResponseHandler } from './openai-error';\n\nexport class OpenAIEmbeddingModel implements EmbeddingModelV1 {\n readonly specificationVersion = 'v1';\n readonly modelId: OpenAIEmbeddingModelId;\n\n private readonly config: OpenAIConfig;\n private readonly settings: OpenAIEmbeddingSettings;\n\n get provider(): string {\n return this.config.provider;\n }\n\n get maxEmbeddingsPerCall(): number {\n return this.settings.maxEmbeddingsPerCall ?? 2048;\n }\n\n get supportsParallelCalls(): boolean {\n return this.settings.supportsParallelCalls ?? true;\n }\n\n constructor(\n modelId: OpenAIEmbeddingModelId,\n settings: OpenAIEmbeddingSettings,\n config: OpenAIConfig,\n ) {\n this.modelId = modelId;\n this.settings = settings;\n this.config = config;\n }\n\n async doEmbed({\n values,\n headers,\n abortSignal,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/embeddings',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), headers),\n body: {\n model: this.modelId,\n input: values,\n encoding_format: 'float',\n dimensions: this.settings.dimensions,\n user: this.settings.user,\n },\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n openaiTextEmbeddingResponseSchema,\n ),\n abortSignal,\n fetch: this.config.fetch,\n });\n\n return {\n embeddings: response.data.map(item => item.embedding),\n usage: response.usage\n ? { tokens: response.usage.prompt_tokens }\n : undefined,\n rawResponse: { headers: responseHeaders },\n };\n }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst openaiTextEmbeddingResponseSchema = z.object({\n data: z.array(z.object({ embedding: z.array(z.number()) })),\n usage: z.object({ prompt_tokens: z.number() }).nullish(),\n});\n","import { ImageModelV1, ImageModelV1CallWarning } from '@ai-toolkit/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport { OpenAIConfig } from './openai-config';\nimport { openaiFailedResponseHandler } from './openai-error';\nimport {\n OpenAIImageModelId,\n OpenAIImageSettings,\n modelMaxImagesPerCall,\n} from './openai-image-settings';\n\ninterface OpenAIImageModelConfig extends OpenAIConfig {\n _internal?: {\n currentDate?: () => Date;\n };\n}\n\nexport class OpenAIImageModel implements ImageModelV1 {\n readonly specificationVersion = 'v1';\n\n get maxImagesPerCall(): number {\n return (\n this.settings.maxImagesPerCall ?? modelMaxImagesPerCall[this.modelId] ?? 1\n );\n }\n\n get provider(): string {\n return this.config.provider;\n }\n\n constructor(\n readonly modelId: OpenAIImageModelId,\n private readonly settings: OpenAIImageSettings,\n private readonly config: OpenAIImageModelConfig,\n ) {}\n\n async doGenerate({\n prompt,\n n,\n size,\n aspectRatio,\n seed,\n providerOptions,\n headers,\n abortSignal,\n }: Parameters[0]): Promise<\n Awaited>\n > {\n const warnings: Array = [];\n\n if (aspectRatio != null) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'aspectRatio',\n details:\n 'This model does not support aspect ratio. Use `size` instead.',\n });\n }\n\n if (seed != null) {\n warnings.push({ type: 'unsupported-setting', setting: 'seed' });\n }\n\n const currentDate = this.config._internal?.currentDate?.() ?? new Date();\n const { value: response, responseHeaders } = await postJsonToApi({\n url: this.config.url({\n path: '/images/generations',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), headers),\n body: {\n model: this.modelId,\n prompt,\n n,\n size,\n ...(providerOptions.openai ?? {}),\n response_format: 'b64_json',\n },\n failedResponseHandler: openaiFailedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n openaiImageResponseSchema,\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\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst openaiImageResponseSchema = z.object({\n data: z.array(z.object({ b64_json: z.string() })),\n});\n","export type OpenAIImageModelId = 'dall-e-3' | 'dall-e-2' | (string & {});\n\n// https://platform.openai.com/docs/guides/images\nexport const modelMaxImagesPerCall: Record = {\n 'dall-e-3': 1,\n 'dall-e-2': 10,\n};\n\nexport interface OpenAIImageSettings {\n /**\nOverride the maximum number of images per call (default is dependent on the\nmodel, or 1 for an unknown model).\n */\n maxImagesPerCall?: 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