zhipu-ai-provider/dist/index.mjs.map

Vercel AI Custom Provider for Services from Zhipu

{"version":3,"sources":["../src/zhipu-provider.ts","../src/zhipu-chat-language-model.ts","../src/convert-to-zhipu-chat-messages.ts","../src/map-zhipu-finish-reason.ts","../src/zhipu-error.ts","../src/get-response-metadata.ts","../src/zhipu-prepare-tools.ts","../src/zhipu-embedding-model.ts"],"sourcesContent":["import {\n  EmbeddingModelV1,\n  LanguageModelV1,\n  ProviderV1,\n} from \"@ai-sdk/provider\";\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from \"@ai-sdk/provider-utils\";\nimport { ZhipuChatLanguageModel } from \"./zhipu-chat-language-model\";\nimport { ZhipuChatModelId, ZhipuChatSettings } from \"./zhipu-chat-settings\";\nimport { ZhipuEmbeddingModel } from \"./zhipu-embedding-model\";\nimport {\n  ZhipuEmbeddingModelId,\n  ZhipuEmbeddingSettings,\n} from \"./zhipu-embedding-settings\";\n\nexport interface ZhipuProvider extends ProviderV1 {\n  (modelId: ZhipuChatModelId, settings?: ZhipuChatSettings): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(\n    modelId: ZhipuChatModelId,\n    settings?: ZhipuChatSettings,\n  ): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  chat(\n    modelId: ZhipuChatModelId,\n    settings?: ZhipuChatSettings,\n  ): LanguageModelV1;\n\n  /**\n@deprecated Use `textEmbeddingModel()` instead.\n   */\n  embedding(\n    modelId: ZhipuEmbeddingModelId,\n    settings?: ZhipuEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n\n  /**\n@deprecated Use `textEmbeddingModel()` instead.\n   */\n  textEmbedding(\n    modelId: ZhipuEmbeddingModelId,\n    settings?: ZhipuEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n\n  textEmbeddingModel: (\n    modelId: ZhipuEmbeddingModelId,\n    settings?: ZhipuEmbeddingSettings,\n  ) => EmbeddingModelV1<string>;\n}\n\nexport interface ZhipuProviderSettings {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://open.bigmodel.cn/api/paas/v4`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `Authorization` header.\nIt defaults to the `ZHIPU_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: Record<string, string>;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n}\n\n/**\nCreate a Zhipu AI provider instance.\n */\nexport function createZhipu(\n  options: ZhipuProviderSettings = {},\n): ZhipuProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ??\n    \"https://open.bigmodel.cn/api/paas/v4\";\n\n  const getHeaders = () => ({\n    Authorization: `Bearer ${loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: \"ZHIPU_API_KEY\",\n      description: \"ZHIPU API key\",\n    })}`,\n    ...options.headers,\n  });\n\n  const createChatModel = (\n    modelId: ZhipuChatModelId,\n    settings: ZhipuChatSettings = {},\n  ) =>\n    new ZhipuChatLanguageModel(modelId, settings, {\n      provider: \"zhipu.chat\",\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createEmbeddingModel = (\n    modelId: ZhipuEmbeddingModelId,\n    settings: ZhipuEmbeddingSettings = {},\n  ) =>\n    new ZhipuEmbeddingModel(modelId, settings, {\n      provider: \"zhipu.embedding\",\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (\n    modelId: ZhipuChatModelId,\n    settings?: ZhipuChatSettings,\n  ) {\n    if (new.target) {\n      throw new Error(\n        \"The Zhipu model function cannot be called with the new keyword.\",\n      );\n    }\n\n    return createChatModel(modelId, settings);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n\n  return provider;\n}\n\n/**\nDefault Zhipu provider instance.\n */\nexport const zhipu = createZhipu();\n","import {\n  InvalidResponseDataError,\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1StreamPart,\n} from \"@ai-sdk/provider\";\nimport {\n  isParsableJson,\n  generateId,\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from \"@ai-sdk/provider-utils\";\nimport { z } from \"zod\";\nimport { convertToZhipuChatMessages } from \"./convert-to-zhipu-chat-messages\";\nimport { mapZhipuFinishReason } from \"./map-zhipu-finish-reason\";\nimport { ZhipuChatModelId, ZhipuChatSettings } from \"./zhipu-chat-settings\";\nimport { zhipuFailedResponseHandler } from \"./zhipu-error\";\nimport { getResponseMetadata } from \"./get-response-metadata\";\nimport { prepareTools } from \"./zhipu-prepare-tools\";\n\ntype ZhipuChatConfig = {\n  provider: string;\n  baseURL: string;\n  isMultiModel?: boolean;\n  isReasoningModel?: boolean;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n  \n};\n\nexport class ZhipuChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = \"v1\";\n  readonly defaultObjectGenerationMode = \"json\";\n  readonly supportsImageUrls = false;\n\n  readonly modelId: ZhipuChatModelId;\n  readonly settings: ZhipuChatSettings;\n\n  private readonly config: ZhipuChatConfig;\n\n  /**\n   * Constructs a new ZhipuChatLanguageModel.\n   * @param modelId - The model identifier.\n   * @param settings - Settings for the chat.\n   * @param config - Model configuration.\n   */\n  constructor(\n    modelId: ZhipuChatModelId,\n    settings: ZhipuChatSettings,\n    config: ZhipuChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n    this.config.isMultiModel = this.modelId.includes(\"4v\");\n    this.config.isReasoningModel = this.modelId.includes(\"zero\");\n  }\n\n  /**\n   * Getter for the provider name.\n   */\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private getArgs({\n    mode,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n  }: Parameters<LanguageModelV1[\"doGenerate\"]>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\n\n    if (!this.config.isMultiModel && prompt.every(msg => msg.role === \"user\" && !msg.content.every(part => part.type === 'text'))) {\n      warnings.push({\n        type: \"other\",\n        message: \"Non-vision models does not support message parts\",\n      });\n    }\n\n    if (topK != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"topK\",\n      });\n    }\n\n    if (frequencyPenalty != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"frequencyPenalty\",\n      });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"presencePenalty\",\n      });\n    }\n\n    if (stopSequences != null && this.config.isMultiModel) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"stopSequences\",\n        details: \"Stop sequences are not supported for vision model\",\n      });\n    }\n\n    if (stopSequences != null && stopSequences.length > 1) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"stopSequences\",\n        details: \"Only supports one stop sequence\",\n      });\n    }\n\n    if (seed != null) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"seed\",\n      });\n    }\n\n    if (\n      responseFormat != null &&\n      responseFormat.type === \"json\" &&\n      (this.config.isMultiModel || this.config.isReasoningModel)\n    ) {\n      warnings.push({\n        type: \"unsupported-setting\",\n        setting: \"responseFormat\",\n        details:\n          \"JSON response format is not supported with vision and reasoning models.\",\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:\n          \"JSON response format schema is only supported with structuredOutputs, provide the schema.\",\n      });\n    }\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      user_id: this.settings.userId,\n      do_sample: this.settings.doSample,\n      request_id: this.settings.requestId,\n\n      // standardized settings:\n      max_tokens: maxTokens,\n      temperature: temperature,\n      top_p: topP,\n\n      // response format:\n      response_format:\n        responseFormat?.type === \"json\" ? { type: \"json_object\" } : undefined,\n\n      // messages:\n      messages: convertToZhipuChatMessages(prompt),\n    };\n\n    switch (type) {\n      case \"regular\": {\n        const { tools, tool_choice, toolWarnings } = prepareTools(mode);\n\n        return {\n          args: { ...baseArgs, tools, tool_choice },\n          warnings: [...warnings, ...toolWarnings],\n        };\n      }\n\n      case \"object-json\": {\n        return {\n          args: {\n            ...baseArgs,\n            response_format: { type: \"json_object\" },\n          },\n          warnings,\n        };\n      }\n\n      case \"object-tool\": {\n        return {\n          args: {\n            ...baseArgs,\n            tool_choice: \"auto\",\n            tools: [mode.tool],\n          },\n          warnings,\n        };\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1[\"doGenerate\"]>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1[\"doGenerate\"]>>> {\n    const { args, warnings } = this.getArgs(options);\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: zhipuFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        zhipuChatResponseSchema,\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    return {\n      text: choice.message.content ?? undefined,\n      toolCalls: choice.message.tool_calls?.map((toolCall) => ({\n        toolCallType: \"function\",\n        toolCallId: toolCall.id,\n        toolName: toolCall.function.name,\n        args: toolCall.function.arguments!,\n      })),\n      finishReason: mapZhipuFinishReason(choice.finish_reason),\n      usage: {\n        promptTokens: response.usage.prompt_tokens,\n        completionTokens: response.usage.completion_tokens ?? NaN,\n      },\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      request: { body: JSON.stringify(args) },\n      response: getResponseMetadata(response),\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1[\"doStream\"]>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1[\"doStream\"]>>> {\n    const { args, warnings } = this.getArgs(options);\n\n    const body = { ...args, stream: true };\n    // const metadataExtractor = this.config.metadataExtractor?.createStreamExtractor();\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: zhipuFailedResponseHandler,\n      successfulResponseHandler:\n        createEventSourceResponseHandler(zhipuChatChunkSchema),\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    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof zhipuChatChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            // handle failed chunk parsing / validation:\n            if (chunk.success == false) {\n              finishReason = \"error\";\n              controller.enqueue({ type: \"error\", error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            // handle error chunks:\n            // if ('error' in value) {\n            //   finishReason = 'error';\n            //   controller.enqueue({ type: 'error', error: value.error });\n            //   return;\n            // }\n\n            if (isFirstChunk) {\n              isFirstChunk = false;\n\n              controller.enqueue({\n                type: \"response-metadata\",\n                ...getResponseMetadata(value),\n              });\n            }\n\n            if (value.usage != null) {\n              const { prompt_tokens, completion_tokens } = value.usage;\n\n              usage = {\n                promptTokens: prompt_tokens ?? undefined,\n                completionTokens: completion_tokens ?? undefined,\n              };\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              if (choice.finish_reason === \"network_error\") {\n                controller.enqueue({\n                  type: \"error\",\n                  error: new Error(`Error: Network Error`),\n                });\n                return;\n              }\n\n              finishReason = mapZhipuFinishReason(choice.finish_reason);\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            if (delta.content != null) {\n              controller.enqueue({\n                type: \"text-delta\",\n                textDelta: delta.content,\n              });\n            }\n\n            const mappedToolCalls = delta.tool_calls;\n\n            if (mappedToolCalls != null) {\n              for (const toolCallDelta of mappedToolCalls) {\n                const index = toolCallDelta.index;\n\n                // Tool call start. OpenAI returns all information except the arguments in the first chunk.\n                if (toolCalls[index] == null) {\n                  if (toolCallDelta.type !== \"function\") {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function' type.`,\n                    });\n                  }\n\n                  if (toolCallDelta.id == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'id' to be a string.`,\n                    });\n                  }\n\n                  if (toolCallDelta.function?.name == null) {\n                    throw new InvalidResponseDataError({\n                      data: toolCallDelta,\n                      message: `Expected 'function.name' to be a string.`,\n                    });\n                  }\n\n                  toolCalls[index] = {\n                    id: toolCallDelta.id,\n                    type: \"function\",\n                    function: {\n                      name: toolCallDelta.function.name,\n                      arguments: toolCallDelta.function.arguments ?? \"\",\n                    },\n                    hasFinished: false,\n                  };\n\n                  const toolCall = toolCalls[index];\n\n                  if (\n                    toolCall.function?.name != null &&\n                    toolCall.function?.arguments != null\n                  ) {\n                    // send delta if the argument text has already started:\n                    if (toolCall.function.arguments.length > 0) {\n                      controller.enqueue({\n                        type: \"tool-call-delta\",\n                        toolCallType: \"function\",\n                        toolCallId: toolCall.id,\n                        toolName: toolCall.function.name,\n                        argsTextDelta: toolCall.function.arguments,\n                      });\n                    }\n\n                    // check if tool call is complete\n                    // (some providers send the full tool call in one chunk):\n                    if (isParsableJson(toolCall.function.arguments)) {\n                      controller.enqueue({\n                        type: \"tool-call\",\n                        toolCallType: \"function\",\n                        toolCallId: toolCall.id ?? generateId(),\n                        toolName: toolCall.function.name,\n                        args: toolCall.function.arguments,\n                      });\n                      toolCall.hasFinished = true;\n                    }\n                  }\n\n                  continue;\n                }\n\n                // existing tool call, merge if not finished\n                const toolCall = toolCalls[index];\n\n                if (toolCall.hasFinished) {\n                  continue;\n                }\n\n                if (toolCallDelta.function?.arguments != null) {\n                  toolCall.function!.arguments +=\n                    toolCallDelta.function?.arguments ?? \"\";\n                }\n\n                // send delta\n                controller.enqueue({\n                  type: \"tool-call-delta\",\n                  toolCallType: \"function\",\n                  toolCallId: toolCall.id,\n                  toolName: toolCall.function.name,\n                  argsTextDelta: toolCallDelta.function.arguments ?? \"\",\n                });\n\n                // check if tool call is complete\n                if (\n                  toolCall.function?.name != null &&\n                  toolCall.function?.arguments != null &&\n                  isParsableJson(toolCall.function.arguments)\n                ) {\n                  controller.enqueue({\n                    type: \"tool-call\",\n                    toolCallType: \"function\",\n                    toolCallId: toolCall.id ?? generateId(),\n                    toolName: toolCall.function.name,\n                    args: toolCall.function.arguments,\n                  });\n                  toolCall.hasFinished = true;\n                }\n              }\n            }\n          },\n\n          flush(controller) {\n            controller.enqueue({\n              type: \"finish\",\n              finishReason,\n              usage: {\n                promptTokens: usage.promptTokens ?? NaN,\n                completionTokens: usage.completionTokens ?? NaN,\n              },\n            });\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      request: { body: JSON.stringify(body) },\n      warnings,\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 zhipuChatResponseSchema = 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\"),\n        content: z.string().nullish(),\n        tool_calls: z\n          .array(\n            z.object({\n              id: z.string(),\n              index: z.number().nullish(),\n              type: z.literal(\"function\"),\n              function: z.object({ name: z.string(), arguments: z.string() }),\n            }),\n          )\n          .nullish(),\n      }),\n      index: z.number(),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  usage: z.object({\n    prompt_tokens: z.number(),\n    completion_tokens: z.number().nullish(),\n    total_tokens: z.number().nullish(),\n  }),\n  web_search: z\n    .object({\n      icon: z.string(),\n      title: z.string(),\n      link: z.string(),\n      media: z.string(),\n      content: z.string(),\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 zhipuChatChunkSchema = 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.object({\n        role: z.enum([\"assistant\"]).optional(),\n        content: z.string().nullish(),\n        tool_calls: z\n          .array(\n            z.object({\n              id: z.string(),\n              index: z.number(),\n              type: z.literal(\"function\"),\n              function: z.object({ name: z.string(), arguments: z.string() }),\n            }),\n          )\n          .nullish(),\n      }),\n      finish_reason: z.string().nullish(),\n      index: z.number(),\n    }),\n  ),\n  usage: z\n    .object({\n      prompt_tokens: z.number(),\n      completion_tokens: z.number().nullish(),\n      total_tokens: z.number().nullish(),\n    })\n    .nullish(),\n  web_search: z\n    .object({\n      icon: z.string(),\n      title: z.string(),\n      link: z.string(),\n      media: z.string(),\n      content: z.string(),\n    })\n    .nullish(),\n});\n// zhipuErrorDataSchema\n","import {\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\nimport { convertUint8ArrayToBase64 } from \"@ai-sdk/provider-utils\";\nimport { ZhipuPrompt } from \"./zhipu-chat-prompt\";\n\nexport function convertToZhipuChatMessages(\n  prompt: LanguageModelV1Prompt,\n): ZhipuPrompt {\n  const messages: ZhipuPrompt = [];\n\n  for (let i = 0; i < prompt.length; i++) {\n    const { role, content } = prompt[i];\n    const isLastMessage = i === prompt.length - 1;\n\n    switch (role) {\n      case \"system\": {\n        messages.push({ role: \"system\", content });\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        if (content.every(part => part.type === 'text')) {\n          messages.push({\n            role: 'user',\n            content: content.map(part => part.text).join(''),\n          });\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        break;\n      }\n\n      case \"assistant\": {\n        let text = \"\";\n        const toolCalls: Array<{\n          id: string;\n          type: \"function\";\n          function: { name: string; arguments: string };\n        }> = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case \"text\": {\n              text += part.text;\n              break;\n            }\n            case \"redacted-reasoning\":\n            case \"reasoning\": {\n              break; // ignored\n            }\n            case \"tool-call\": {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: \"function\",\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.args),\n                },\n              });\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = part;\n              throw new Error(`Unsupported part: ${_exhaustiveCheck}`);\n            }\n          }\n        }\n\n        messages.push({\n          role: \"assistant\",\n          content: text,\n          prefix: isLastMessage ? true : undefined,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        });\n\n        break;\n      }\n      case \"tool\": {\n        for (const toolResponse of content) {\n          messages.push({\n            role: \"tool\",\n            content: JSON.stringify(toolResponse.result),\n            tool_call_id: toolResponse.toolCallId,\n          });\n        }\n        break;\n      }\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return messages;\n}\n","import { LanguageModelV1FinishReason } from \"@ai-sdk/provider\";\n\nexport function mapZhipuFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\n  switch (finishReason) {\n    case \"stop\":\n      return \"stop\";\n    case \"length\":\n      return \"length\";\n    case \"tool_calls\":\n      return \"tool-calls\";\n    case \"sensitive\":\n      return \"content-filter\";\n    case \"network_error\":\n      return \"error\";\n    default:\n      return \"unknown\";\n  }\n}\n","import { createJsonErrorResponseHandler } from \"@ai-sdk/provider-utils\";\nimport { z } from \"zod\";\n\nexport const zhipuErrorDataSchema = z.object({\n  error: z.object({\n    message: z.string(),\n    code: z.union([z.string(), z.number()]).nullish(),\n  }),\n});\n\nexport type ZhipuErrorData = z.infer<typeof zhipuErrorDataSchema>;\n\nexport const zhipuFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: zhipuErrorDataSchema,\n  errorToMessage: (data) => data.error.message,\n});\n","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | null;\n  model?: string | undefined | null;\n}) {\n  return {\n    id: id ?? undefined,\n    modelId: model ?? undefined,\n    timestamp: created != null ? new Date(created * 1000) : undefined,\n  };\n}\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  UnsupportedFunctionalityError,\n} from \"@ai-sdk/provider\";\n\nexport function prepareTools(\n  mode: Parameters<LanguageModelV1[\"doGenerate\"]>[0][\"mode\"] & {\n    type: \"regular\";\n  },\n): {\n  tools:\n    | Array<{\n        type: \"function\";\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }>\n    | undefined;\n  tool_choice: \"auto\" | undefined;\n  toolWarnings: LanguageModelV1CallWarning[];\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  const tools = mode.tools?.length ? mode.tools : undefined;\n  const toolWarnings: LanguageModelV1CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: undefined, toolWarnings };\n  }\n\n  const zhipuTools: Array<{\n    type: \"function\";\n    function: {\n      name: string;\n      description: string | undefined;\n      parameters: unknown;\n    };\n  }> = [];\n\n  for (const tool of tools) {\n    if (tool.type === \"provider-defined\") {\n      toolWarnings.push({ type: \"unsupported-tool\", tool });\n    } else {\n      zhipuTools.push({\n        type: \"function\",\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.parameters,\n        },\n      });\n    }\n  }\n\n  const toolChoice = mode.toolChoice;\n\n  if (toolChoice == null) {\n    return { tools: zhipuTools, tool_choice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case \"none\":\n      return { tools: zhipuTools, tool_choice: undefined, toolWarnings };\n    case \"auto\":\n    case \"required\":\n      return { tools: zhipuTools, tool_choice: \"auto\", toolWarnings };\n\n    // zhipu does not support tool mode directly,\n    // so we filter the tools and force the tool choice through 'any'\n    case \"tool\":\n      return {\n        tools: zhipuTools.filter(\n          (tool) => tool.function.name === toolChoice.toolName,\n        ),\n        tool_choice: \"auto\",\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  EmbeddingModelV1,\n  TooManyEmbeddingValuesForCallError,\n} from \"@ai-sdk/provider\";\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  postJsonToApi,\n} from \"@ai-sdk/provider-utils\";\nimport { z } from \"zod\";\nimport {\n  ZhipuEmbeddingModelId,\n  ZhipuEmbeddingSettings,\n} from \"./zhipu-embedding-settings\";\nimport { zhipuFailedResponseHandler } from \"./zhipu-error\";\n\ntype ZhipuEmbeddingConfig = {\n  /**\n  Override the maximum number of embeddings per call.\n   */\n  maxEmbeddingsPerCall?: number;\n\n  /**\n  Override the parallelism of embedding calls.\n   */\n  supportsParallelCalls?: boolean;\n\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class ZhipuEmbeddingModel implements EmbeddingModelV1<string> {\n  readonly specificationVersion = \"v1\";\n  readonly modelId: ZhipuEmbeddingModelId;\n\n  private readonly config: ZhipuEmbeddingConfig;\n  private readonly settings: ZhipuEmbeddingSettings;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get maxEmbeddingsPerCall(): number {\n    return this.config.maxEmbeddingsPerCall ?? 64;\n  }\n\n  get supportsParallelCalls(): boolean {\n    return this.config.supportsParallelCalls ?? true;\n  }\n\n  constructor(\n    modelId: ZhipuEmbeddingModelId,\n    settings: ZhipuEmbeddingSettings,\n    config: ZhipuEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    abortSignal,\n    headers,\n  }: Parameters<EmbeddingModelV1<string>[\"doEmbed\"]>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV1<string>[\"doEmbed\"]>>\n  > {\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values,\n      });\n    }\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/embeddings`,\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        input: values,\n        dimension: this.settings.dimensions,\n      },\n      failedResponseHandler: zhipuFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        ZhipuTextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      embeddings: 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