@ai-sdk/openai-compatible/dist/index.mjs.map

This package provides a foundation for implementing providers that expose an OpenAI-compatible API.

{"version":3,"sources":["../src/chat/openai-compatible-chat-language-model.ts","../src/openai-compatible-error.ts","../src/chat/convert-openai-compatible-chat-usage.ts","../src/chat/convert-to-openai-compatible-chat-messages.ts","../src/chat/get-response-metadata.ts","../src/chat/map-openai-compatible-finish-reason.ts","../src/chat/openai-compatible-chat-options.ts","../src/chat/openai-compatible-prepare-tools.ts","../src/completion/openai-compatible-completion-language-model.ts","../src/completion/convert-openai-compatible-completion-usage.ts","../src/completion/convert-to-openai-compatible-completion-prompt.ts","../src/completion/get-response-metadata.ts","../src/completion/map-openai-compatible-finish-reason.ts","../src/completion/openai-compatible-completion-options.ts","../src/embedding/openai-compatible-embedding-model.ts","../src/embedding/openai-compatible-embedding-options.ts","../src/image/openai-compatible-image-model.ts","../src/openai-compatible-provider.ts","../src/version.ts"],"sourcesContent":["import {\n  APICallError,\n  InvalidResponseDataError,\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  SharedV3ProviderMetadata,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonErrorResponseHandler,\n  createJsonResponseHandler,\n  FetchFunction,\n  generateId,\n  isParsableJson,\n  parseProviderOptions,\n  ParseResult,\n  postJsonToApi,\n  ResponseHandler,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  defaultOpenAICompatibleErrorStructure,\n  ProviderErrorStructure,\n} from '../openai-compatible-error';\nimport { convertOpenAICompatibleChatUsage } from './convert-openai-compatible-chat-usage';\nimport { convertToOpenAICompatibleChatMessages } from './convert-to-openai-compatible-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAICompatibleFinishReason } from './map-openai-compatible-finish-reason';\nimport {\n  OpenAICompatibleChatModelId,\n  openaiCompatibleProviderOptions,\n} from './openai-compatible-chat-options';\nimport { MetadataExtractor } from './openai-compatible-metadata-extractor';\nimport { prepareTools } from './openai-compatible-prepare-tools';\n\nexport type OpenAICompatibleChatConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n  includeUsage?: boolean;\n  errorStructure?: ProviderErrorStructure<any>;\n  metadataExtractor?: MetadataExtractor;\n\n  /**\n   * Whether the model supports structured outputs.\n   */\n  supportsStructuredOutputs?: boolean;\n\n  /**\n   * The supported URLs for the model.\n   */\n  supportedUrls?: () => LanguageModelV3['supportedUrls'];\n};\n\nexport class OpenAICompatibleChatLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\n\n  readonly supportsStructuredOutputs: boolean;\n\n  readonly modelId: OpenAICompatibleChatModelId;\n  private readonly config: OpenAICompatibleChatConfig;\n  private readonly failedResponseHandler: ResponseHandler<APICallError>;\n  private readonly chunkSchema; // type inferred via constructor\n\n  constructor(\n    modelId: OpenAICompatibleChatModelId,\n    config: OpenAICompatibleChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n\n    // initialize error handling:\n    const errorStructure =\n      config.errorStructure ?? defaultOpenAICompatibleErrorStructure;\n    this.chunkSchema = createOpenAICompatibleChatChunkSchema(\n      errorStructure.errorSchema,\n    );\n    this.failedResponseHandler = createJsonErrorResponseHandler(errorStructure);\n\n    this.supportsStructuredOutputs = config.supportsStructuredOutputs ?? false;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private get providerOptionsName(): string {\n    return this.config.provider.split('.')[0].trim();\n  }\n\n  get supportedUrls() {\n    return this.config.supportedUrls?.() ?? {};\n  }\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    providerOptions,\n    stopSequences,\n    responseFormat,\n    seed,\n    toolChoice,\n    tools,\n  }: LanguageModelV3CallOptions) {\n    const warnings: SharedV3Warning[] = [];\n\n    // Parse provider options\n    const compatibleOptions = Object.assign(\n      (await parseProviderOptions({\n        provider: 'openai-compatible',\n        providerOptions,\n        schema: openaiCompatibleProviderOptions,\n      })) ?? {},\n      (await parseProviderOptions({\n        provider: this.providerOptionsName,\n        providerOptions,\n        schema: openaiCompatibleProviderOptions,\n      })) ?? {},\n    );\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported', feature: 'topK' });\n    }\n\n    if (\n      responseFormat?.type === 'json' &&\n      responseFormat.schema != null &&\n      !this.supportsStructuredOutputs\n    ) {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'responseFormat',\n        details:\n          'JSON response format schema is only supported with structuredOutputs',\n      });\n    }\n\n    const {\n      tools: openaiTools,\n      toolChoice: openaiToolChoice,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n    });\n\n    return {\n      args: {\n        // model id:\n        model: this.modelId,\n\n        // model specific settings:\n        user: compatibleOptions.user,\n\n        // standardized settings:\n        max_tokens: maxOutputTokens,\n        temperature,\n        top_p: topP,\n        frequency_penalty: frequencyPenalty,\n        presence_penalty: presencePenalty,\n        response_format:\n          responseFormat?.type === 'json'\n            ? this.supportsStructuredOutputs === true &&\n              responseFormat.schema != null\n              ? {\n                  type: 'json_schema',\n                  json_schema: {\n                    schema: responseFormat.schema,\n                    name: responseFormat.name ?? 'response',\n                    description: responseFormat.description,\n                  },\n                }\n              : { type: 'json_object' }\n            : undefined,\n\n        stop: stopSequences,\n        seed,\n        ...Object.fromEntries(\n          Object.entries(\n            providerOptions?.[this.providerOptionsName] ?? {},\n          ).filter(\n            ([key]) =>\n              !Object.keys(openaiCompatibleProviderOptions.shape).includes(key),\n          ),\n        ),\n\n        reasoning_effort: compatibleOptions.reasoningEffort,\n        verbosity: compatibleOptions.textVerbosity,\n\n        // messages:\n        messages: convertToOpenAICompatibleChatMessages(prompt),\n\n        // tools:\n        tools: openaiTools,\n        tool_choice: openaiToolChoice,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args, warnings } = await this.getArgs({ ...options });\n\n    const body = JSON.stringify(args);\n\n    const {\n      responseHeaders,\n      value: responseBody,\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: args,\n      failedResponseHandler: this.failedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        OpenAICompatibleChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = responseBody.choices[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // text content:\n    const text = choice.message.content;\n    if (text != null && text.length > 0) {\n      content.push({ type: 'text', text });\n    }\n\n    // reasoning content:\n    const reasoning =\n      choice.message.reasoning_content ?? choice.message.reasoning;\n    if (reasoning != null && reasoning.length > 0) {\n      content.push({\n        type: 'reasoning',\n        text: reasoning,\n      });\n    }\n\n    // tool calls:\n    if (choice.message.tool_calls != null) {\n      for (const toolCall of choice.message.tool_calls) {\n        content.push({\n          type: 'tool-call',\n          toolCallId: toolCall.id ?? generateId(),\n          toolName: toolCall.function.name,\n          input: toolCall.function.arguments!,\n        });\n      }\n    }\n\n    // provider metadata:\n    const providerMetadata: SharedV3ProviderMetadata = {\n      [this.providerOptionsName]: {},\n      ...(await this.config.metadataExtractor?.extractMetadata?.({\n        parsedBody: rawResponse,\n      })),\n    };\n    const completionTokenDetails =\n      responseBody.usage?.completion_tokens_details;\n    if (completionTokenDetails?.accepted_prediction_tokens != null) {\n      providerMetadata[this.providerOptionsName].acceptedPredictionTokens =\n        completionTokenDetails?.accepted_prediction_tokens;\n    }\n    if (completionTokenDetails?.rejected_prediction_tokens != null) {\n      providerMetadata[this.providerOptionsName].rejectedPredictionTokens =\n        completionTokenDetails?.rejected_prediction_tokens;\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n        raw: choice.finish_reason ?? undefined,\n      },\n      usage: convertOpenAICompatibleChatUsage(responseBody.usage),\n      providerMetadata,\n      request: { body },\n      response: {\n        ...getResponseMetadata(responseBody),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const { args, warnings } = await 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: this.config.includeUsage\n        ? { include_usage: true }\n        : undefined,\n    };\n\n    const metadataExtractor =\n      this.config.metadataExtractor?.createStreamExtractor();\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: this.failedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        this.chunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\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: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage: z.infer<typeof openaiCompatibleTokenUsageSchema> | undefined =\n      undefined;\n    let isFirstChunk = true;\n    const providerOptionsName = this.providerOptionsName;\n    let isActiveReasoning = false;\n    let isActiveText = false;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof this.chunkSchema>>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            // Emit raw chunk if requested (before anything else)\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            metadataExtractor?.processChunk(chunk.rawValue);\n\n            // handle error chunks:\n            if ('error' in chunk.value) {\n              finishReason = { unified: 'error', raw: undefined };\n              controller.enqueue({\n                type: 'error',\n                error: chunk.value.error.message,\n              });\n              return;\n            }\n\n            // TODO we lost type safety on Chunk, most likely due to the error schema. MUST FIX\n            // remove this workaround when the issue is fixed\n            const value = chunk.value as z.infer<typeof chunkBaseSchema>;\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 = value.usage;\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = {\n                unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n                raw: choice.finish_reason ?? undefined,\n              };\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            // enqueue reasoning before text deltas:\n            const reasoningContent = delta.reasoning_content ?? delta.reasoning;\n            if (reasoningContent) {\n              if (!isActiveReasoning) {\n                controller.enqueue({\n                  type: 'reasoning-start',\n                  id: 'reasoning-0',\n                });\n                isActiveReasoning = true;\n              }\n\n              controller.enqueue({\n                type: 'reasoning-delta',\n                id: 'reasoning-0',\n                delta: reasoningContent,\n              });\n            }\n\n            if (delta.content) {\n              if (!isActiveText) {\n                controller.enqueue({ type: 'text-start', id: 'txt-0' });\n                isActiveText = true;\n              }\n\n              controller.enqueue({\n                type: 'text-delta',\n                id: 'txt-0',\n                delta: delta.content,\n              });\n            }\n\n            if (delta.tool_calls != null) {\n              for (const toolCallDelta of delta.tool_calls) {\n                const index = toolCallDelta.index;\n\n                if (toolCalls[index] == null) {\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                  controller.enqueue({\n                    type: 'tool-input-start',\n                    id: toolCallDelta.id,\n                    toolName: toolCallDelta.function.name,\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-input-delta',\n                        id: toolCall.id,\n                        delta: 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-input-end',\n                        id: toolCall.id,\n                      });\n\n                      controller.enqueue({\n                        type: 'tool-call',\n                        toolCallId: toolCall.id ?? generateId(),\n                        toolName: toolCall.function.name,\n                        input: 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-input-delta',\n                  id: toolCall.id,\n                  delta: 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-input-end',\n                    id: toolCall.id,\n                  });\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallId: toolCall.id ?? generateId(),\n                    toolName: toolCall.function.name,\n                    input: toolCall.function.arguments,\n                  });\n                  toolCall.hasFinished = true;\n                }\n              }\n            }\n          },\n\n          flush(controller) {\n            if (isActiveReasoning) {\n              controller.enqueue({ type: 'reasoning-end', id: 'reasoning-0' });\n            }\n\n            if (isActiveText) {\n              controller.enqueue({ type: 'text-end', id: 'txt-0' });\n            }\n\n            // go through all tool calls and send the ones that are not finished\n            for (const toolCall of toolCalls.filter(\n              toolCall => !toolCall.hasFinished,\n            )) {\n              controller.enqueue({\n                type: 'tool-input-end',\n                id: toolCall.id,\n              });\n\n              controller.enqueue({\n                type: 'tool-call',\n                toolCallId: toolCall.id ?? generateId(),\n                toolName: toolCall.function.name,\n                input: toolCall.function.arguments,\n              });\n            }\n\n            const providerMetadata: SharedV3ProviderMetadata = {\n              [providerOptionsName]: {},\n              ...metadataExtractor?.buildMetadata(),\n            };\n            if (\n              usage?.completion_tokens_details?.accepted_prediction_tokens !=\n              null\n            ) {\n              providerMetadata[providerOptionsName].acceptedPredictionTokens =\n                usage?.completion_tokens_details?.accepted_prediction_tokens;\n            }\n            if (\n              usage?.completion_tokens_details?.rejected_prediction_tokens !=\n              null\n            ) {\n              providerMetadata[providerOptionsName].rejectedPredictionTokens =\n                usage?.completion_tokens_details?.rejected_prediction_tokens;\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertOpenAICompatibleChatUsage(usage),\n              providerMetadata,\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nconst openaiCompatibleTokenUsageSchema = z\n  .object({\n    prompt_tokens: z.number().nullish(),\n    completion_tokens: z.number().nullish(),\n    total_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 OpenAICompatibleChatResponseSchema = 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        reasoning_content: z.string().nullish(),\n        reasoning: z.string().nullish(),\n        tool_calls: z\n          .array(\n            z.object({\n              id: z.string().nullish(),\n              function: z.object({\n                name: z.string(),\n                arguments: z.string(),\n              }),\n            }),\n          )\n          .nullish(),\n      }),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  usage: openaiCompatibleTokenUsageSchema,\n});\n\nconst chunkBaseSchema = 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          // Most openai-compatible models set `reasoning_content`, but some\n          // providers serving `gpt-oss` set `reasoning`. See #7866\n          reasoning_content: z.string().nullish(),\n          reasoning: z.string().nullish(),\n          tool_calls: z\n            .array(\n              z.object({\n                index: z.number(),\n                id: z.string().nullish(),\n                function: z.object({\n                  name: z.string().nullish(),\n                  arguments: z.string().nullish(),\n                }),\n              }),\n            )\n            .nullish(),\n        })\n        .nullish(),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  usage: openaiCompatibleTokenUsageSchema,\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 createOpenAICompatibleChatChunkSchema = <\n  ERROR_SCHEMA extends z.core.$ZodType,\n>(\n  errorSchema: ERROR_SCHEMA,\n) => z.union([chunkBaseSchema, errorSchema]);\n","import { z, ZodType } from 'zod/v4';\n\nexport const openaiCompatibleErrorDataSchema = 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 OpenAICompatibleErrorData = z.infer<\n  typeof openaiCompatibleErrorDataSchema\n>;\n\nexport type ProviderErrorStructure<T> = {\n  errorSchema: ZodType<T>;\n  errorToMessage: (error: T) => string;\n  isRetryable?: (response: Response, error?: T) => boolean;\n};\n\nexport const defaultOpenAICompatibleErrorStructure: ProviderErrorStructure<OpenAICompatibleErrorData> =\n  {\n    errorSchema: openaiCompatibleErrorDataSchema,\n    errorToMessage: data => data.error.message,\n  };\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport function convertOpenAICompatibleChatUsage(\n  usage:\n    | {\n        prompt_tokens?: number | null;\n        completion_tokens?: number | null;\n        prompt_tokens_details?: {\n          cached_tokens?: number | null;\n        } | null;\n        completion_tokens_details?: {\n          reasoning_tokens?: number | null;\n        } | null;\n      }\n    | undefined\n    | null,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.prompt_tokens ?? 0;\n  const completionTokens = usage.completion_tokens ?? 0;\n  const cacheReadTokens = usage.prompt_tokens_details?.cached_tokens ?? 0;\n  const reasoningTokens =\n    usage.completion_tokens_details?.reasoning_tokens ?? 0;\n\n  return {\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: usage,\n  };\n}\n","import {\n  LanguageModelV3Prompt,\n  SharedV3ProviderMetadata,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { OpenAICompatibleChatPrompt } from './openai-compatible-api-types';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\n\nfunction getOpenAIMetadata(message: {\n  providerOptions?: SharedV3ProviderMetadata;\n}) {\n  return message?.providerOptions?.openaiCompatible ?? {};\n}\n\nexport function convertToOpenAICompatibleChatMessages(\n  prompt: LanguageModelV3Prompt,\n): OpenAICompatibleChatPrompt {\n  const messages: OpenAICompatibleChatPrompt = [];\n  for (const { role, content, ...message } of prompt) {\n    const metadata = getOpenAIMetadata({ ...message });\n    switch (role) {\n      case 'system': {\n        messages.push({ role: 'system', content, ...metadata });\n        break;\n      }\n\n      case 'user': {\n        if (content.length === 1 && content[0].type === 'text') {\n          messages.push({\n            role: 'user',\n            content: content[0].text,\n            ...getOpenAIMetadata(content[0]),\n          });\n          break;\n        }\n\n        messages.push({\n          role: 'user',\n          content: content.map(part => {\n            const partMetadata = getOpenAIMetadata(part);\n            switch (part.type) {\n              case 'text': {\n                return { type: 'text', text: part.text, ...partMetadata };\n              }\n              case 'file': {\n                if (part.mediaType.startsWith('image/')) {\n                  const mediaType =\n                    part.mediaType === 'image/*'\n                      ? 'image/jpeg'\n                      : part.mediaType;\n\n                  return {\n                    type: 'image_url',\n                    image_url: {\n                      url:\n                        part.data instanceof URL\n                          ? part.data.toString()\n                          : `data:${mediaType};base64,${convertToBase64(part.data)}`,\n                    },\n                    ...partMetadata,\n                  };\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: `file part media type ${part.mediaType}`,\n                  });\n                }\n              }\n            }\n          }),\n          ...metadata,\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          const partMetadata = getOpenAIMetadata(part);\n          switch (part.type) {\n            case 'text': {\n              text += part.text;\n              break;\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.input),\n                },\n                ...partMetadata,\n              });\n              break;\n            }\n          }\n        }\n\n        messages.push({\n          role: 'assistant',\n          content: text,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n          ...metadata,\n        });\n\n        break;\n      }\n\n      case 'tool': {\n        for (const toolResponse of content) {\n          if (toolResponse.type === 'tool-approval-response') {\n            continue;\n          }\n\n          const output = toolResponse.output;\n\n          let contentValue: string;\n          switch (output.type) {\n            case 'text':\n            case 'error-text':\n              contentValue = output.value;\n              break;\n            case 'execution-denied':\n              contentValue = output.reason ?? 'Tool execution denied.';\n              break;\n            case 'content':\n            case 'json':\n            case 'error-json':\n              contentValue = JSON.stringify(output.value);\n              break;\n          }\n\n          const toolResponseMetadata = getOpenAIMetadata(toolResponse);\n          messages.push({\n            role: 'tool',\n            tool_call_id: toolResponse.toolCallId,\n            content: contentValue,\n            ...toolResponseMetadata,\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","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 { LanguageModelV3FinishReason } from '@ai-sdk/provider';\n\nexport function mapOpenAICompatibleFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV3FinishReason['unified'] {\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 'other';\n  }\n}\n","import { z } from 'zod/v4';\n\nexport type OpenAICompatibleChatModelId = string;\n\nexport const openaiCompatibleProviderOptions = z.object({\n  /**\n   * A unique identifier representing your end-user, which can help the provider to\n   * monitor and detect abuse.\n   */\n  user: z.string().optional(),\n\n  /**\n   * Reasoning effort for reasoning models. Defaults to `medium`.\n   */\n  reasoningEffort: z.string().optional(),\n\n  /**\n   * Controls the verbosity of the generated text. Defaults to `medium`.\n   */\n  textVerbosity: z.string().optional(),\n});\n\nexport type OpenAICompatibleProviderOptions = z.infer<\n  typeof openaiCompatibleProviderOptions\n>;\n","import {\n  LanguageModelV3CallOptions,\n  SharedV3Warning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\n\nexport function prepareTools({\n  tools,\n  toolChoice,\n}: {\n  tools: LanguageModelV3CallOptions['tools'];\n  toolChoice?: LanguageModelV3CallOptions['toolChoice'];\n}): {\n  tools:\n    | undefined\n    | Array<{\n        type: 'function';\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n          strict?: boolean;\n        };\n      }>;\n  toolChoice:\n    | { type: 'function'; function: { name: string } }\n    | 'auto'\n    | 'none'\n    | 'required'\n    | undefined;\n  toolWarnings: SharedV3Warning[];\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  tools = tools?.length ? tools : undefined;\n\n  const toolWarnings: SharedV3Warning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const openaiCompatTools: Array<{\n    type: 'function';\n    function: {\n      name: string;\n      description: string | undefined;\n      parameters: unknown;\n      strict?: boolean;\n    };\n  }> = [];\n\n  for (const tool of tools) {\n    if (tool.type === 'provider') {\n      toolWarnings.push({\n        type: 'unsupported',\n        feature: `provider-defined tool ${tool.id}`,\n      });\n    } else {\n      openaiCompatTools.push({\n        type: 'function',\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.inputSchema,\n          ...(tool.strict != null ? { strict: tool.strict } : {}),\n        },\n      });\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: openaiCompatTools, toolChoice: 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: openaiCompatTools, toolChoice: type, toolWarnings };\n    case 'tool':\n      return {\n        tools: openaiCompatTools,\n        toolChoice: {\n          type: 'function',\n          function: { name: toolChoice.toolName },\n        },\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  APICallError,\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonErrorResponseHandler,\n  createJsonResponseHandler,\n  FetchFunction,\n  parseProviderOptions,\n  ParseResult,\n  postJsonToApi,\n  ResponseHandler,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  defaultOpenAICompatibleErrorStructure,\n  ProviderErrorStructure,\n} from '../openai-compatible-error';\nimport { convertOpenAICompatibleCompletionUsage } from './convert-openai-compatible-completion-usage';\nimport { convertToOpenAICompatibleCompletionPrompt } from './convert-to-openai-compatible-completion-prompt';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapOpenAICompatibleFinishReason } from './map-openai-compatible-finish-reason';\nimport {\n  OpenAICompatibleCompletionModelId,\n  openaiCompatibleCompletionProviderOptions,\n} from './openai-compatible-completion-options';\n\ntype OpenAICompatibleCompletionConfig = {\n  provider: string;\n  includeUsage?: boolean;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n  errorStructure?: ProviderErrorStructure<any>;\n\n  /**\n   * The supported URLs for the model.\n   */\n  supportedUrls?: () => LanguageModelV3['supportedUrls'];\n};\n\nexport class OpenAICompatibleCompletionLanguageModel\n  implements LanguageModelV3\n{\n  readonly specificationVersion = 'v3';\n\n  readonly modelId: OpenAICompatibleCompletionModelId;\n  private readonly config: OpenAICompatibleCompletionConfig;\n  private readonly failedResponseHandler: ResponseHandler<APICallError>;\n  private readonly chunkSchema; // type inferred via constructor\n\n  constructor(\n    modelId: OpenAICompatibleCompletionModelId,\n    config: OpenAICompatibleCompletionConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n\n    // initialize error handling:\n    const errorStructure =\n      config.errorStructure ?? defaultOpenAICompatibleErrorStructure;\n    this.chunkSchema = createOpenAICompatibleCompletionChunkSchema(\n      errorStructure.errorSchema,\n    );\n    this.failedResponseHandler = createJsonErrorResponseHandler(errorStructure);\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private get providerOptionsName(): string {\n    return this.config.provider.split('.')[0].trim();\n  }\n\n  get supportedUrls() {\n    return this.config.supportedUrls?.() ?? {};\n  }\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences: userStopSequences,\n    responseFormat,\n    seed,\n    providerOptions,\n    tools,\n    toolChoice,\n  }: LanguageModelV3CallOptions) {\n    const warnings: SharedV3Warning[] = [];\n\n    // Parse provider options\n    const completionOptions =\n      (await parseProviderOptions({\n        provider: this.providerOptionsName,\n        providerOptions,\n        schema: openaiCompatibleCompletionProviderOptions,\n      })) ?? {};\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported', feature: 'topK' });\n    }\n\n    if (tools?.length) {\n      warnings.push({ type: 'unsupported', feature: 'tools' });\n    }\n\n    if (toolChoice != null) {\n      warnings.push({ type: 'unsupported', feature: 'toolChoice' });\n    }\n\n    if (responseFormat != null && responseFormat.type !== 'text') {\n      warnings.push({\n        type: 'unsupported',\n        feature: 'responseFormat',\n        details: 'JSON response format is not supported.',\n      });\n    }\n\n    const { prompt: completionPrompt, stopSequences } =\n      convertToOpenAICompatibleCompletionPrompt({ prompt });\n\n    const stop = [...(stopSequences ?? []), ...(userStopSequences ?? [])];\n\n    return {\n      args: {\n        // model id:\n        model: this.modelId,\n\n        // model specific settings:\n        echo: completionOptions.echo,\n        logit_bias: completionOptions.logitBias,\n        suffix: completionOptions.suffix,\n        user: completionOptions.user,\n\n        // standardized settings:\n        max_tokens: maxOutputTokens,\n        temperature,\n        top_p: topP,\n        frequency_penalty: frequencyPenalty,\n        presence_penalty: presencePenalty,\n        seed,\n        ...providerOptions?.[this.providerOptionsName],\n\n        // prompt:\n        prompt: completionPrompt,\n\n        // stop sequences:\n        stop: stop.length > 0 ? stop : undefined,\n      },\n      warnings,\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args, warnings } = await 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: this.failedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        openaiCompatibleCompletionResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // text content:\n    if (choice.text != null && choice.text.length > 0) {\n      content.push({ type: 'text', text: choice.text });\n    }\n\n    return {\n      content,\n      usage: convertOpenAICompatibleCompletionUsage(response.usage),\n      finishReason: {\n        unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n        raw: choice.finish_reason,\n      },\n      request: { body: args },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const { args, warnings } = await 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: this.config.includeUsage\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: this.failedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        this.chunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage:\n      | {\n          prompt_tokens: number | undefined;\n          completion_tokens: number | undefined;\n          total_tokens: number | undefined;\n        }\n      | undefined = undefined;\n    let isFirstChunk = true;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof this.chunkSchema>>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = { unified: 'error', raw: undefined };\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 = { unified: 'error', raw: undefined };\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              controller.enqueue({\n                type: 'text-start',\n                id: '0',\n              });\n            }\n\n            if (value.usage != null) {\n              usage = value.usage;\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = {\n                unified: mapOpenAICompatibleFinishReason(choice.finish_reason),\n                raw: choice.finish_reason ?? undefined,\n              };\n            }\n\n            if (choice?.text != null) {\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: choice.text,\n              });\n            }\n          },\n\n          flush(controller) {\n            if (!isFirstChunk) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertOpenAICompatibleCompletionUsage(usage),\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nconst usageSchema = z.object({\n  prompt_tokens: z.number(),\n  completion_tokens: z.number(),\n  total_tokens: z.number(),\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 openaiCompatibleCompletionResponseSchema = 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    }),\n  ),\n  usage: usageSchema.nullish(),\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 createOpenAICompatibleCompletionChunkSchema = <\n  ERROR_SCHEMA extends z.core.$ZodType,\n>(\n  errorSchema: ERROR_SCHEMA,\n) =>\n  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        }),\n      ),\n      usage: usageSchema.nullish(),\n    }),\n    errorSchema,\n  ]);\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport function convertOpenAICompatibleCompletionUsage(\n  usage:\n    | {\n        prompt_tokens?: number | null;\n        completion_tokens?: number | null;\n      }\n    | undefined\n    | null,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.prompt_tokens ?? 0;\n  const completionTokens = usage.completion_tokens ?? 0;\n\n  return {\n    inputTokens: {\n      total: promptTokens,\n      noCache: promptTokens,\n      cacheRead: undefined,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: completionTokens,\n      text: completionTokens,\n      reasoning: undefined,\n    },\n    raw: usage,\n  };\n}\n","import {\n  InvalidPromptError,\n  LanguageModelV3Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\n\nexport function convertToOpenAICompatibleCompletionPrompt({\n  prompt,\n  user = 'user',\n  assistant = 'assistant',\n}: {\n  prompt: LanguageModelV3Prompt;\n  user?: string;\n  assistant?: string;\n}): {\n  prompt: string;\n  stopSequences?: string[];\n} {\n  // 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: ${content}',\n          prompt,\n        });\n      }\n\n      case 'user': {\n        const userMessage = content\n          .map(part => {\n            switch (part.type) {\n              case 'text': {\n                return part.text;\n              }\n            }\n          })\n          .filter(Boolean)\n          .join('');\n\n        text += `${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","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | n