sarvam-ai-sdk/dist/index.mjs.map

Sarvam AI Provider support for AI-SDK

{"version":3,"sources":["../src/sarvam-provider.ts","../src/sarvam-chat-language-model.ts","../src/convert-to-sarvam-chat-messages.ts","../src/get-response-metadata.ts","../src/map-sarvam-finish-reason.ts","../src/sarvam-error.ts","../src/sarvam-prepare-tools.ts","../src/sarvam-speech-model.ts","../src/sarvam-speech-settings.ts","../src/sarvam-transcription-model.ts","../src/sarvam-transcription-settings.ts","../src/sarvam-translation-model.ts","../src/sarvam-config.ts","../src/sarvam-transliterate-model.ts","../src/sarvam-lid-model.ts","../src/sarvam-speech-translation-model.ts"],"sourcesContent":["import {\n    LanguageModelV1,\n    SpeechModelV1,\n    TranscriptionModelV1\n} from \"@ai-sdk/provider\";\nimport {\n    FetchFunction,\n    loadApiKey,\n    withoutTrailingSlash,\n} from \"@ai-sdk/provider-utils\";\nimport { SarvamChatLanguageModel } from \"./sarvam-chat-language-model\";\nimport { SarvamChatModelId, SarvamChatSettings } from \"./sarvam-chat-settings\";\nimport { SarvamLanguageCode } from \"./sarvam-config\";\nimport {\n    SarvamSpeechModel,\n} from \"./sarvam-speech-model\";\nimport { SarvamSpeechModelId, SarvamSpeechSettings } from \"./sarvam-speech-settings\";\nimport {\n    SarvamTranscriptionModel,\n} from \"./sarvam-transcription-model\";\nimport { SarvamSpeechTranslationModelId, SarvamTranscriptionCallOptions, SarvamTranscriptionModelId } from \"./sarvam-transcription-settings\";\nimport { SarvamTranslationModel } from \"./sarvam-translation-model\";\nimport { SarvamTranslationSettings } from \"./sarvam-translation-settings\";\nimport { SarvamTransliterateModel } from \"./sarvam-transliterate-model\";\nimport { SarvamTransliterateSettings } from \"./sarvam-transliterate-settings\";\nimport { SarvamLidModel } from \"./sarvam-lid-model\";\nimport { SarvamSpeechTranslationModel } from \"./sarvam-speech-translation-model\";\n\nexport interface SarvamProvider {\n  /**\n  * Creates a model for text generation.\n  */\n  (modelId: SarvamChatModelId, settings?: SarvamChatSettings): LanguageModelV1;\n\n  /**\n  * Creates an Sarvam chat model for text generation.\n  */\n  languageModel(\n    modelId: SarvamChatModelId,\n    settings?: SarvamChatSettings,\n  ): LanguageModelV1;\n\n  /**\n  * Creates a Sarvam model for transcription.\n  */\n  transcription(\n    modelId: SarvamTranscriptionModelId,\n    /**\n    * Audio source language code\n    *\n    * @default unknown\n    */\n    languageCode?: SarvamLanguageCode | \"unknown\",\n    settings?: SarvamTranscriptionCallOptions,\n  ): TranscriptionModelV1;\n\n  /**\n  * Creates a Sarvam model for Speech translation.\n  */\n  speechTranslation(\n    modelId: SarvamSpeechTranslationModelId,\n  ): TranscriptionModelV1;\n\n  /**\n  * Creates a Sarvam model for speech.\n  */\n  speech(\n    modelId: SarvamSpeechModelId,\n    languageCode: SarvamLanguageCode,\n    settings?: SarvamSpeechSettings,\n  ): SpeechModelV1;\n\n  /**\n  * Creates an Sarvam model for transliterate.\n  */\n  transliterate(settings: SarvamTransliterateSettings): LanguageModelV1;\n\n  /**\n  * Creates an Sarvam model for translation.\n  */\n  translation(settings: SarvamTranslationSettings): LanguageModelV1;\n\n  /**\n  * Creates an Sarvam model for language identification.\n  */\n  languageIdentification(): LanguageModelV1;\n}\n\nexport interface SarvamProviderSettings {\n  /**\n  * URL for the Sarvam API calls.\n  * @default https://api.sarvam.ai\n  */\n  baseURL?: string;\n\n  /**\n  * API key for authenticating requests.\n  * @default process.env.SARVAM_API_KEY\n  */\n  apiKey?: string;\n\n  /**\n  * Custom headers to include in the requests.\n  * @default\n    Authorization: `Bearer ${process.env.SARVAM_API_KEY}`,\n    \"api-subscription-key\": process.env.SARVAM_API_KEY\n  */\n  headers?: Record<string, string>;\n\n  /**\n  * Custom fetch implementation. You can use it as a middleware to intercept requests,\n  * or to provide a custom fetch implementation for e.g. testing.\n  */\n  fetch?: FetchFunction;\n}\n\n/**\n* Create an Sarvam provider instance.\n*/\nexport function createSarvam(\n  options: SarvamProviderSettings = {},\n): SarvamProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? \"https://api.sarvam.ai\";\n\n  const getApiKey = () => loadApiKey({\n    apiKey: options.apiKey,\n    environmentVariableName: \"SARVAM_API_KEY\",\n    description: \"Sarvam\",\n  });\n\n  const getHeaders = () => ({\n    Authorization: `Bearer ${getApiKey()}`,\n    \"api-subscription-key\": getApiKey(),\n    ...options.headers,\n  });\n\n  const createChatModel = (\n    modelId: SarvamChatModelId,\n    settings: SarvamChatSettings = {},\n  ) =>\n    new SarvamChatLanguageModel(modelId, settings, {\n      provider: \"sarvam.chat\",\n      url: ({ path }) => `${baseURL}/v1${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createLanguageModel = (\n    modelId: SarvamChatModelId,\n    settings?: SarvamChatSettings,\n  ) => {\n    if (new.target) {\n      throw new Error(\n        \"The Sarvam model function cannot be called with the new keyword.\",\n      );\n    }\n\n    return createChatModel(modelId, settings);\n  };\n\n  const createTranscriptionModel = (\n    modelId: SarvamTranscriptionModelId,\n    languageCode: SarvamLanguageCode | \"unknown\" = \"unknown\",\n    settings?: SarvamTranscriptionCallOptions,\n  ) => new SarvamTranscriptionModel(modelId, languageCode, {\n      provider: \"sarvam.transcription\",\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      transcription: settings,\n    });\n\n  const createSpeechTranslation = (\n    modelId: SarvamTranscriptionModelId\n  ) => new SarvamSpeechTranslationModel(modelId, {\n      provider: \"sarvam.transcription\",\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createSpeechModel = (\n    modelId: SarvamSpeechModelId,\n    languageCode: SarvamLanguageCode,\n    settings?: SarvamSpeechSettings,\n  ) =>\n    new SarvamSpeechModel(modelId, languageCode, {\n      provider: \"sarvam.speech\",\n      url: ({ path }) => `${baseURL}${path}`,\n      headers: getHeaders,\n      fetch: options.fetch,\n      speech: settings,\n    });\n\n  const createTransliterateModel = (settings: SarvamTransliterateSettings) =>\n    new SarvamTransliterateModel(\n        settings,\n      {\n        provider: \"sarvam.transliterate\",\n        url: ({ path }) => `${baseURL}${path}`,\n        headers: getHeaders,\n        fetch: options.fetch,\n      },\n    );\n\n  const createTranslationModel = (settings: SarvamTranslationSettings) =>\n    new SarvamTranslationModel(\n        settings,\n      {\n        provider: \"sarvam.translation\",\n        url: ({ path }) => `${baseURL}${path}`,\n        headers: getHeaders,\n        fetch: options.fetch,\n      },\n    );\n\n  const createLidModel = () =>\n    new SarvamLidModel(\n      {\n        provider: \"sarvam.lid\",\n        url: ({ path }) => `${baseURL}${path}`,\n        headers: getHeaders,\n        fetch: options.fetch,\n      },\n    );\n\n  const provider = (\n    modelId: SarvamChatModelId,\n    settings?: SarvamChatSettings,\n  ) => createLanguageModel(modelId, settings);\n\n  provider.languageModel = createLanguageModel;\n  provider.chat = createChatModel;\n  provider.transcription = createTranscriptionModel;\n  provider.speechTranslation = createSpeechTranslation;\n  provider.speech = createSpeechModel;\n  provider.transliterate = createTransliterateModel;\n  provider.translation = createTranslationModel;\n  provider.languageIdentification = createLidModel;\n\n  return provider;\n}\n\n/**\n* Default Sarvam provider instance.\n*/\nexport const sarvam = createSarvam();\n","import {\n  InvalidResponseDataError,\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1FunctionToolCall,\n  LanguageModelV1Prompt,\n  LanguageModelV1ProviderMetadata,\n  LanguageModelV1StreamPart,\n} from \"@ai-sdk/provider\";\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  generateId,\n  isParsableJson,\n  parseProviderOptions,\n  postJsonToApi,\n} from \"@ai-sdk/provider-utils\";\nimport { z } from \"zod\";\nimport { convertToSarvamChatMessages } from \"./convert-to-sarvam-chat-messages\";\nimport { getResponseMetadata } from \"./get-response-metadata\";\nimport { mapSarvamFinishReason } from \"./map-sarvam-finish-reason\";\nimport { SarvamChatModelId, SarvamChatSettings } from \"./sarvam-chat-settings\";\nimport {\n  sarvamErrorDataSchema,\n  sarvamFailedResponseHandler,\n} from \"./sarvam-error\";\nimport {\n  extractToolCallData,\n  parseJSON,\n  prepareTools,\n  simulateJsonSchema,\n  simulateToolCalling,\n} from \"./sarvam-prepare-tools\";\n\ntype SarvamChatConfig = {\n  provider: string;\n  headers: () => Record<string, string | undefined>;\n  url: (options: { modelId: string; path: string }) => string;\n  fetch?: FetchFunction;\n};\n\nexport class SarvamChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = \"v1\";\n\n  readonly supportsStructuredOutputs = false;\n  readonly defaultObjectGenerationMode = \"json\";\n\n  readonly modelId: SarvamChatModelId;\n  readonly settings: SarvamChatSettings;\n\n  private readonly config: SarvamChatConfig;\n\n  constructor(\n    modelId: SarvamChatModelId,\n    settings: SarvamChatSettings,\n    config: SarvamChatConfig,\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  get supportsImageUrls(): boolean {\n    // image urls can be sent if downloadImages is disabled (default):\n    return !this.settings.downloadImages;\n  }\n\n  private async getArgs({\n    mode,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    stream,\n    providerMetadata,\n  }: Parameters<LanguageModelV1[\"doGenerate\"]>[0] & {\n    stream: boolean;\n  }) {\n    const type = mode.type;\n    const simulate = this.settings.simulate\n\n    if (type === \"object-json\" && simulate === \"tool-calling\")\n        throw new Error('Use { simulate: \"json-object\" } with generateObject()')\n\n    if (type === \"regular\" && simulate === \"json-object\")\n        throw new Error('Use { simulate: \"tool-calling\" } with generateText()')\n\n    const warnings: LanguageModelV1CallWarning[] = [];\n\n    if (stream) {\n      warnings.push({\n        type: \"other\",\n        message: \"Streaming is still experimental for Sarvam\",\n      });\n    }\n\n    if (topK != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"topK\",\n      });\n    }\n\n    if (\n      responseFormat != null &&\n      responseFormat.type === \"json\" &&\n      responseFormat.schema != null\n    ) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"responseFormat\",\n        details: \"JSON response format schema is not supported\",\n      });\n    }\n\n    const sarvamOptions = parseProviderOptions({\n      provider: \"sarvam\",\n      providerOptions: providerMetadata,\n      schema: z.object({\n        reasoningFormat: z.enum([\"parsed\", \"raw\", \"hidden\"]).nullish(),\n      }),\n    });\n\n    const baseArgs = (\n      prompt: LanguageModelV1Prompt,\n      extraSystemPrompt?: string,\n    ) => ({\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\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      stop: stopSequences,\n      seed,\n\n      // response format:\n      response_format:\n        // json object response format is not supported for streaming:\n        stream === false && responseFormat?.type === \"json\"\n          ? { type: \"json_object\" }\n          : undefined,\n\n      // provider options:\n      reasoning_format: sarvamOptions?.reasoningFormat,\n\n      // messages:\n      messages: convertToSarvamChatMessages(prompt, extraSystemPrompt),\n    });\n\n    switch (type) {\n      case \"regular\": {\n        const { tools, tool_choice, toolWarnings } = prepareTools({\n          mode,\n        });\n\n        const extraSystemPrompt =\n          tools && simulate === \"tool-calling\"\n            ? await simulateToolCalling(tools)\n            : undefined;\n\n        return {\n          args: {\n            ...baseArgs(prompt, extraSystemPrompt),\n            tools,\n            tool_choice,\n          },\n          warnings: [...warnings, ...toolWarnings],\n        };\n      }\n\n      case \"object-json\": {\n        const extraSystemPrompt = simulate === \"json-object\"\n          ? simulateJsonSchema()\n          : undefined;\n\n        return {\n          args: {\n            ...baseArgs(prompt, extraSystemPrompt),\n            response_format:\n              // json object response format is not supported for streaming:\n              stream === false ? { type: \"json_object\" } : undefined,\n          },\n          warnings,\n        };\n      }\n\n      case \"object-tool\": {\n        return {\n          args: {\n            ...baseArgs(prompt),\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                },\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<LanguageModelV1[\"doGenerate\"]>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1[\"doGenerate\"]>>> {\n    const { args, warnings } = await this.getArgs({\n      ...options,\n      stream: false,\n    });\n\n    const body = JSON.stringify(args);\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: args,\n      failedResponseHandler: sarvamFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        sarvamChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n    const choice = response.choices[0];\n\n    let text = choice.message.content ?? undefined;\n\n    let toolCalls = 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    })) as LanguageModelV1FunctionToolCall[] | undefined;\n\n    // simulate tool calling through prompt engineering\n    if (this.settings.simulate === \"tool-calling\") {\n        if (text && text.length !== 0) {\n          const jsonObject = parseJSON(text);\n          if (jsonObject) {\n            const newTools = extractToolCallData(jsonObject);\n            if (newTools) {\n              toolCalls = [newTools];\n              text = undefined;\n            }\n          }\n        }\n    }\n\n    // simulate JSON object generation through prompt engineering\n    if (this.settings.simulate === \"json-object\") {\n      if (text && text.length !== 0) {\n        const jsonObject = parseJSON(text);\n        if (jsonObject) {\n          const newTools = extractToolCallData(jsonObject);\n            text = JSON.stringify(jsonObject);\n        }\n      }\n    }\n\n    return {\n      text,\n      toolCalls,\n      reasoning: choice.message.reasoning ?? undefined,\n      finishReason: mapSarvamFinishReason(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      response: getResponseMetadata(response),\n      warnings,\n      request: { body },\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1[\"doStream\"]>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1[\"doStream\"]>>> {\n    const { args, warnings } = await this.getArgs({ ...options, stream: true });\n\n    const body = JSON.stringify({ ...args, stream: true });\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        ...args,\n        stream: true,\n      },\n      failedResponseHandler: sarvamFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        sarvamChatChunkSchema,\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 isFirstChunk = true;\n\n    let providerMetadata: LanguageModelV1ProviderMetadata | undefined;\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof sarvamChatChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = \"error\";\n              controller.enqueue({\n                type: \"error\",\n                error: chunk.error,\n              });\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({\n                type: \"error\",\n                error: value.error,\n              });\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.x_sarvam?.usage != null) {\n              usage = {\n                promptTokens: value.x_sarvam.usage.prompt_tokens ?? undefined,\n                completionTokens:\n                  value.x_sarvam.usage.completion_tokens ?? undefined,\n              };\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = mapSarvamFinishReason(choice.finish_reason);\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            if (delta.reasoning != null && delta.reasoning.length > 0) {\n              controller.enqueue({\n                type: \"reasoning\",\n                textDelta: delta.reasoning,\n              });\n            }\n\n            if (delta.content != null && delta.content.length > 0) {\n              controller.enqueue({\n                type: \"text-delta\",\n                textDelta: 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.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              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      warnings,\n      request: { 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 sarvamChatResponseSchema = 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        content: z.string().nullish(),\n        reasoning: z.string().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      finish_reason: z.string().nullish(),\n    }),\n  ),\n  usage: z\n    .object({\n      prompt_tokens: z.number().nullish(),\n      completion_tokens: z.number().nullish(),\n    })\n    .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 sarvamChatChunkSchema = 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            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                  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        finish_reason: z.string().nullable().optional(),\n        index: z.number(),\n      }),\n    ),\n    x_sarvam: z\n      .object({\n        usage: z\n          .object({\n            prompt_tokens: z.number().nullish(),\n            completion_tokens: z.number().nullish(),\n          })\n          .nullish(),\n      })\n      .nullish(),\n  }),\n  sarvamErrorDataSchema,\n]);\n","import {\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\nimport { convertUint8ArrayToBase64 } from \"@ai-sdk/provider-utils\";\nimport { SarvamChatPrompt } from \"./sarvam-api-types\";\n\nexport function convertToSarvamChatMessages(\n  prompt: LanguageModelV1Prompt,\n  fakeToolSystemPrompt?: string,\n): SarvamChatPrompt {\n  const messages: SarvamChatPrompt = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case \"system\": {\n        const contentData = fakeToolSystemPrompt\n          ? `${content}\\n\\n${fakeToolSystemPrompt}`\n          : content;\n        messages.push({ role: \"system\", content: contentData });\n        break;\n      }\n\n      case \"user\": {\n        if (content.length === 1 && content[0].type === \"text\") {\n          messages.push({ role: \"user\", content: content[0].text });\n          break;\n        }\n\n        messages.push({\n          role: \"user\",\n          content: content.map((part) => {\n            switch (part.type) {\n              case \"text\": {\n                return { type: \"text\", text: part.text };\n              }\n              case \"image\": {\n                return {\n                  type: \"image_url\",\n                  image_url: {\n                    url:\n                      part.image instanceof URL\n                        ? part.image.toString()\n                        : `data:${\n                            part.mimeType ?? \"image/jpeg\"\n                          };base64,${convertUint8ArrayToBase64(part.image)}`,\n                  },\n                };\n              }\n              case \"file\": {\n                throw new UnsupportedFunctionalityError({\n                  functionality: \"File content parts in user messages\",\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 \"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          }\n        }\n\n        messages.push({\n          role: \"assistant\",\n          content: text,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        });\n\n        break;\n      }\n\n      case \"tool\": {\n        for (const toolResponse of content) {\n          messages.push({\n            role: \"tool\",\n            tool_call_id: toolResponse.toolCallId,\n            content: JSON.stringify(toolResponse.result),\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 { LanguageModelV1FinishReason } from \"@ai-sdk/provider\";\n\nexport function mapSarvamFinishReason(\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-sdk/provider-utils\";\n\nexport const sarvamErrorDataSchema = z.object({\n    error: z.object({\n        message: z.string(),\n        type: z.string(),\n    }),\n});\n\nexport type SarvamErrorData = z.infer<typeof sarvamErrorDataSchema>;\n\nexport const sarvamFailedResponseHandler = createJsonErrorResponseHandler({\n    errorSchema: sarvamErrorDataSchema,\n    errorToMessage: (data) => data.error.message,\n});\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FunctionToolCall,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\nimport { generateId } from \"@ai-sdk/provider-utils\";\n\ntype SarvamTools = Array<{\n  type: \"function\";\n  function: {\n    name: string;\n    description: string | undefined;\n    parameters: unknown;\n  };\n}>;\n\nexport function prepareTools({\n  mode,\n}: {\n  mode: Parameters<LanguageModelV1[\"doGenerate\"]>[0][\"mode\"] & {\n    type: \"regular\";\n  };\n}): {\n  tools:\n    | undefined\n    | Array<{\n        type: \"function\";\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }>;\n  tool_choice:\n    | { type: \"function\"; function: { name: string } }\n    | \"auto\"\n    | \"none\"\n    | \"required\"\n    | undefined;\n  toolWarnings: LanguageModelV1CallWarning[];\n  fakeTools?: string;\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  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  const sarvamTools: SarvamTools = [];\n\n  for (const tool of tools) {\n    if (tool.type === \"provider-defined\") {\n      toolWarnings.push({ type: \"unsupported-tool\", tool });\n    } else {\n      sarvamTools.push({\n        type: \"function\",\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.parameters,\n        },\n      });\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: sarvamTools, 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: sarvamTools, tool_choice: type, toolWarnings };\n    case \"tool\":\n      return {\n        tools: sarvamTools,\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\nimport { compile } from \"json-schema-to-typescript\";\n\nexport const simulateToolCalling = async (\n  tools: SarvamTools,\n): Promise<string> => {\n  const context = [];\n  const names = [];\n\n  for (const tool of tools) {\n    names.push(tool.function.name);\n\n    const tsType = await compile(\n      tool.function.parameters as object,\n      tool.function.name,\n      {\n        bannerComment: \"\",\n        format: false,\n        declareExternallyReferenced: true,\n        enableConstEnums: true,\n        unreachableDefinitions: false,\n        strictIndexSignatures: false,\n      },\n    );\n    const toolContext = tsType\n      .replace(\n        /export interface (\\w+) \\{/,\n        (_: any, name: string) => `type ${tool.function.name} = {`,\n      )\n      .replace(/\\/\\*\\*\\s*\\n\\s*\\*\\s*(.*?)\\s*\\n\\s*\\*\\//, \"// $1\");\n    context.push(`// ${tool.function.description}\\n${toolContext}`);\n  }\n\n  const text = `These are the available tool you can execute.\n\n${context.join(\"\\n\")}\n\ntype YourToolChoices = {\n 'toolName': '${names.join(\"' | '\")}',\n 'toolData': ${names.join(\" | \")}\n}\n\nRespond normally.\nIf user request to execute any tool, respond with pure JSON format\nMake sure to translate toolData to English.\n\neg:\nconst myChoice: YourToolChoices = {\n    \"toolName\": <name>,\n    \"toolData\": <data>\n}`;\n\n  return text;\n};\n\nexport const extractToolCallData = (\n  jsonObject: object,\n): LanguageModelV1FunctionToolCall | void => {\n\n    type ToolFunction = {\n      toolName: string;\n      toolData: any;\n    };\n    const toolFunction = jsonObject as ToolFunction\n\n    if (!(\"toolName\" in toolFunction)) return;\n    if (!(\"toolData\" in toolFunction)) return;\n\n    return {\n        args: JSON.stringify(toolFunction.toolData),\n        toolCallId: generateId(),\n        toolCallType: \"function\",\n        toolName: toolFunction.toolName,\n    };\n\n};\n\nexport const parseJSON = <T extends object>(\n    text: string,\n):T | void => {\n    const jsonRegex = /\\{(?:[^{}]*|\\{[^{}]*\\})*\\}/g;\n    const jsonMatches = text.match(jsonRegex);\n\n    if (jsonMatches && jsonMatches[0]) {\n        try {\n            const jsonObject = JSON.parse(jsonMatches[0])\n            return jsonObject\n        }\n        catch (error) {}\n    }\n}\n\nexport const simulateJsonSchema = () => \"If user doen't specify, make sure to translate json data content into pure English.\"\n","import type { SpeechModelV1, SpeechModelV1CallWarning } from \"@ai-sdk/provider\";\nimport {\n    combineHeaders,\n    createBinaryResponseHandler,\n    createJsonResponseHandler,\n    parseProviderOptions,\n    postJsonToApi,\n} from \"@ai-sdk/provider-utils\";\nimport type { SarvamConfig, SarvamLanguageCode } from \"./sarvam-config\";\nimport { sarvamFailedResponseHandler } from \"./sarvam-error\";\nimport {\n    SarvamProviderOptionsSchema,\n    SarvamSpeechSettings,\n    SpeakerSchema,\n    type SarvamSpeechModelId,\n} from \"./sarvam-speech-settings\";\nimport type { SarvamSpeechAPITypes } from \"./sarvam-api-types\";\nimport { z } from \"zod\";\n\ninterface SarvamSpeechModelConfig extends SarvamConfig {\n    _internal?: {\n        currentDate?: () => Date;\n    };\n    speech?: SarvamSpeechSettings;\n}\n\nexport class SarvamSpeechModel implements SpeechModelV1 {\n    readonly specificationVersion = \"v1\";\n\n    get provider(): string {\n        return this.config.provider;\n    }\n\n    constructor(\n        readonly modelId: SarvamSpeechModelId,\n        readonly languageCode: SarvamLanguageCode,\n        private readonly config: SarvamSpeechModelConfig,\n    ) {}\n\n    private getArgs({\n        text,\n        voice,\n        outputFormat = \"wav\",\n        // speed,\n        // instructions,\n        providerOptions,\n    }: Parameters<SpeechModelV1[\"doGenerate\"]>[0]) {\n        const warnings: SpeechModelV1CallWarning[] = [];\n\n        // Parse provider options\n        const sarvamOptions = parseProviderOptions({\n            provider: \"sarvam\",\n            providerOptions: {\n                sarvam: {\n                    ...providerOptions?.sarvam,\n                    ...this.config.speech,\n                },\n            },\n            schema: SarvamProviderOptionsSchema,\n        });\n\n        const getSpeaker = (): SarvamSpeechCallOptions[\"speaker\"] => {\n            if (sarvamOptions?.speaker) return sarvamOptions.speaker;\n            if (voice) {\n                return SpeakerSchema.parse(voice);\n            }\n\n            switch (this.modelId) {\n                case \"bulbul:v1\":\n                    return \"meera\";\n                case \"bulbul:v2\":\n                    return \"manisha\";\n            }\n\n            return \"meera\";\n        };\n\n        // Create request body\n        const requestBody: Record<string, unknown> = {\n            model: this.modelId,\n            text: text,\n            target_language_code: this.languageCode,\n            speaker: getSpeaker(),\n            // response_format: \"wav\",\n            // speed,\n            // instructions,\n        };\n\n        if (outputFormat) {\n            if (\n                [\"mp3\", \"opus\", \"aac\", \"flac\", \"wav\", \"pcm\"].includes(\n                    outputFormat,\n                )\n            ) {\n                requestBody.response_format = outputFormat;\n            } else {\n                warnings.push({\n                    type: \"unsupported-setting\",\n                    setting: \"outputFormat\",\n                    details: `Unsupported output format: ${outputFormat}. Using mp3 instead.`,\n                });\n            }\n        }\n\n        // Add provider-specific options\n        if (sarvamOptions) {\n            const speechModelOptions: SarvamSpeechAPITypes = {};\n\n            for (const key in speechModelOptions) {\n                const value =\n                    speechModelOptions[key as keyof SarvamSpeechAPITypes];\n                if (value !== undefined) {\n                    requestBody[key] = value;\n                }\n            }\n        }\n\n        return {\n            requestBody,\n            warnings,\n        };\n    }\n\n    async doGenerate(\n        options: Parameters<SpeechModelV1[\"doGenerate\"]>[0],\n    ): Promise<Awaited<ReturnType<SpeechModelV1[\"doGenerate\"]>>> {\n        const currentDate =\n            this.config._internal?.currentDate?.() ?? new Date();\n        const { requestBody, warnings } = this.getArgs(options);\n\n        const {\n            value,\n            responseHeaders,\n            rawValue: rawResponse,\n        } = await postJsonToApi({\n            url: this.config.url({\n                path: \"/text-to-speech\",\n                modelId: this.modelId,\n            }),\n            headers: combineHeaders(this.config.headers(), options.headers),\n            body: requestBody,\n            failedResponseHandler: sarvamFailedResponseHandler,\n            successfulResponseHandler: createJsonResponseHandler(\n                z.object({\n                    request_id: z.string(),\n                    audios: z.array(z.string()),\n                }),\n            ),\n            abortSignal: options.abortSignal,\n            fetch: this.config.fetch,\n        });\n\n        const audio = value.audios[0];\n\n        return {\n            audio,\n            warnings,\n            request: {\n                body: JSON.stringify(requestBody),\n            },\n            response: {\n                timestamp: currentDate,\n                modelId: this.modelId,\n                headers: responseHeaders,\n                body: rawResponse,\n            },\n        };\n    }\n}\n","import { z } from \"zod\";\n\nexport type SarvamSpeechModelId = \"bulbul:v1\" | \"bulbul:v2\" | (string & {});\n\nexport type SarvamSpeechVoices = z.infer<typeof SpeakerSchema>;\n\nexport const SpeakerSchema = z\n    .enum([\n        \"meera\",\n        \"pavithra\",\n        \"maitreyi\",\n        \"arvind\",\n        \"amol\",\n        \"amartya\",\n        \"diya\",\n        \"neel\",\n        \"misha\",\n        \"vian\",\n        \"arjun\",\n        \"maya\",\n        \"anushka\",\n        \"abhilash\",\n        \"manisha\",\n        \"vidya\",\n        \"arya\",\n        \"karun\",\n        \"hitesh\",\n    ])\n    .default(\"meera\");\n\n// https://docs.sarvam.ai/api-reference-docs/text-to-speech/convert\nexport const SarvamProviderOptionsSchema = z\n    .object({\n        speaker: SpeakerSchema,\n        pitch: z.number().min(-0.75).max(0.75).default(0.0),\n        pace: z.number().min(0.5).max(2.0).default(1.0),\n        loudness: z.number().min(0.3).max(3.0).default(1.0),\n        speech_sample_rate: z\n            .union([\n                z.literal(8000),\n                z.literal(16000),\n                z.literal(22050),\n                z.literal(24000),\n            ])\n            .default(22050),\n        enable_preprocessing: z.boolean().default(false),\n    })\n    .partial();\n\n/**\n * Configuration settings for Sarvam Text-to-Speech API.\n *\n * This type defines the customizable options for generating speech audio\n * using the Sarvam Text-to-Speech API. Each property corresponds to a specific\n * feature or parameter supported by the API.\n */\nexport type SarvamSpeechSettings = {\n    /**\n     * The speaker voice to be used for the output audio.\n     *\n     * @default \"meera\"\n     * @example \"meera\" (Default female voice for bulbul:v1)\n     * @example \"arvind\" (Male voice for bulbul:v1)\n     * @example \"anushka\" (Female voice for bulbul:v2)\n     */\n    speaker?: SarvamSpeechVoices;\n\n    /**\n     * Controls the pitch of the audio.\n     *\n     * @default 0.0\n     * @example -0.5 (Deeper voice)\n     * @example 0.5 (Sharper voice)\n     */\n    pitch?: number;\n\n    /**\n     * Controls the speed of the audio.\n     *\n     * @default 1.0\n     * @example 0.5 (Slower speech)\n     * @example 2.0 (Faster speech)\n     */\n    pace?: number;\n\n    /**\n     * Controls the loudness of the audio.\n     *\n     * @default 1.0\n     * @example 0.3 (Quieter audio)\n     * @example 2.5 (Louder audio)\n     */\n    loudness?: number;\n\n    /**\n     * Specifies the sample rate of the output audio.\n     *\n     * @default 22050\n     * @example 8000 (Low-quality audio)\n     * @example 24000 (High-quality audio)\n     */\n    speech_sample_rate?: 8000 | 16000 | 22050 | 24000;\n\n    /**\n     * Enables preprocessing for normalization of English words and numeric entities\n     * (e.g., numbers, dates) in the input text.\n     *\n     * @default false\n     * @example true (Enable preprocessing)\n     * @example false (Disable preprocessing)\n     */\n    enable_preprocessing?: boolean;\n};\n","import {\n    TranscriptionModelV1,\n    TranscriptionModelV1CallWarning,\n} from \"@ai-sdk/provider\";\nimport {\n    combineHeaders,\n    createJsonResponseHandler,\n    parseProviderOptions,\n    postFormDataToApi,\n} from \"@ai-sdk/provider-utils\";\nimport { z } from \"zod\";\nimport { SarvamConfig, SarvamLanguageCode } from \"./sarvam-config\";\nimport { sarvamFailedResponseHandler } from \"./sarvam-error\";\nimport {\n    SarvamProviderOptionsSchema,\n    SarvamTranscriptionCallOptions,\n    SarvamTranscriptionModelId,\n} from \"./sarvam-transcription-settings\";\n\n// https://docs.sarvam.ai/api-reference-docs/speech-to-text/transcribe\ninterface SarvamTranscriptionModelConfig extends SarvamConfig {\n    _internal?: {\n        currentDate?: () => Date;\n    };\n    transcription?: SarvamTranscriptionCallOptions;\n}\n\nexport class SarvamTranscriptionModel implements TranscriptionModelV1 {\n    readonly specificationVersion = \"v1\";\n\n    constructor(\n        readonly modelId: SarvamTranscriptionModelId,\n        readonly languageCode: SarvamLanguageCode | \"unknown\",\n        private readonly config: SarvamTranscriptionModelConfig,\n    ) {}\n\n    get provider(): string {\n        return this.config.provider;\n    }\n\n    private getArgs({\n        audio,\n        mediaType,\n        providerOptions,\n    }: Parameters<TranscriptionModelV1[\"doGenerate\"]>[0]) {\n        const warnings: TranscriptionModelV1CallWarning[] = [];\n\n        if (this.modelId === \"saarika:v1\" && this.languageCode === \"unknown\")\n            throw new Error(\n                \"Language code unknown is not supported for model saarika:v1\",\n            );\n\n        const sarvamOptions = parseProviderOptions({\n            provider: \"sarvam\",\n            providerOptions: {\n                sarvam: {\n                    ...providerOptions?.sarvam,\n                    ...this.config.transcription,\n                },\n            },\n            schema: SarvamProviderOptionsSchema,\n        });\n\n        const formData = new FormData();\n        const blob =\n            audio instanceof Blob\n                ? audio\n                : new Blob([audio], { type: mediaType });\n\n        formData.append(\"file\", blob);\n        formData.append(\"model\", this.modelId);\n        if (sarvamOptions) {\n            formData.append(\"language_code\", this.languageCode);\n            formData.append(\n                \"with_timestamps\",\n                sarvamOptions.with_timestamps ? \"true\" : \"false\",\n            );\n            formData.append(\n                \"with_diarization\",\n                sarvamOptions.with_diarization ? \"true\" : \"false\",\n            );\n            if (\n                sarvamOptions.num_speakers !== null &&\n                sarvamOptions.num_speakers !== undefined\n            ) {\n                formData.append(\n                    \"num_speakers\",\n                    sarvamOptions.num_speakers.toString(),\n                );\n            }\n        }\n\n        return {\n            formData,\n            warnings,\n        };\n    }\n\n    async doGenerate(\n        options: Parameters<TranscriptionModelV1[\"doGenerate\"]>[0],\n    ): Promise<Awaited<ReturnType<TranscriptionModelV1[\"doGenerate\"]>>> {\n        const currentDate =\n            this.config._internal?.currentDate?.() ?? new Date();\n        const { formData, warnings } = this.getArgs(options);\n\n        const {\n            value: response,\n            responseHeaders,\n            rawValue: rawResponse,\n        } = await postFormDataToApi({\n            url: this.config.url({\n                path: \"/speech-to-text\",\n                modelId: this.modelId,\n            }),\n            headers: combineHeaders(this.config.headers(), options.headers),\n            formData,\n            failedResponseHandler: sarvamFailedResponseHandler,\n            successfulResponseHandler: