anthropic-vertex-ai/dist/index.mjs.map

[nalaso/anthropic-vertex-ai](https://github.com/nalaso/anthropic-vertex-ai) is a community provider that uses Anthropic models through Vertex AI to provide language model support for the Vercel AI SDK.

{"version":3,"sources":["../src/anthropic-vertex-provider.ts","../src/anthropic-messages-language-model.ts","../src/anthropic-error.ts","../src/convert-to-anthropic-messages-prompt.ts","../src/map-anthropic-stop-reason.ts"],"sourcesContent":["import { loadApiKey, loadSetting, withoutTrailingSlash } from '@ai-sdk/provider-utils';\nimport { GoogleAuth, GoogleAuthOptions } from 'google-auth-library';\nimport { AnthropicMessagesLanguageModel } from './anthropic-messages-language-model';\nimport {\n  AnthropicMessagesModelId,\n  AnthropicMessagesSettings,\n} from './anthropic-messages-settings';\n\nexport interface AnthropicVertexProvider {\n  /**\nCreates a model for text generation.\n*/\n  (\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): AnthropicMessagesLanguageModel;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): AnthropicMessagesLanguageModel;\n\n  /**\nCreates a model for text generation.\n*/\n  chat(\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ): AnthropicMessagesLanguageModel;\n}\n\nexport interface AnthropicVertexProviderSettings {\n  /**\nYour Google Vertex region. Defaults to the environment variable `GOOGLE_VERTEX_REGION`.\n   */\n  region?: string;\n\n  /**\nYour Google Vertex project. Defaults to the environment variable `GOOGLE_VERTEX_PROJECT_ID`.\n  */\n  projectId?: string;\n\n  /**\n Optional. The Authentication options provided by google-auth-library.\nComplete list of authentication options is documented in the\nGoogleAuthOptions interface:\nhttps://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts.\n   */\n  googleAuth?: GoogleAuth;\n\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.anthropic.com/v1`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `x-api-key` header.\nIt defaults to the `ANTHROPIC_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?: typeof fetch;\n\n  generateId?: () => string;\n}\n\n/**\nCreate an Anthropic provider instance.\n */\nexport function createAnthropicVertex(\n  options: AnthropicVertexProviderSettings = {},\n): AnthropicVertexProvider {\n\n  const getConfig = () => {\n    const config = {\n      projectId: loadSetting({\n        settingValue: options.projectId,\n        settingName: 'projectId',\n        environmentVariableName: 'GOOGLE_VERTEX_PROJECT_ID',\n        description: 'Google Vertex project id',\n      }),\n      region: loadSetting({\n        settingValue: options.region,\n        settingName: 'region',\n        environmentVariableName: 'GOOGLE_VERTEX_REGION',\n        description: 'Google Vertex region',\n      }),\n      googleAuth: options.googleAuth,\n    };\n\n    if (!config.region) {\n      throw new Error(\n        'No region was given. The client should be instantiated with the `region` option or the `GOOGLE_VERTEX_REGION` environment variable should be set.',\n      );\n    }\n\n    if (!config.projectId) {\n      throw new Error(\n        'No project was given. The client should be instantiated with the `projectID` option or the `GOOGLE_VERTEX_PROJECT_ID` environment variable should be set.',\n      );\n    }\n\n    return config;\n  }\n\n  const createChatModel = (\n    modelId: AnthropicMessagesModelId,\n    settings: AnthropicMessagesSettings = {},\n  ) => {\n    \n    const config = getConfig();\n\n    const baseURL =\n      withoutTrailingSlash(options.baseURL) ??\n      `https://${config.region}-aiplatform.googleapis.com/v1`;\n\n    const auth =\n      options.googleAuth ?? new GoogleAuth({ scopes: 'https://www.googleapis.com/auth/cloud-platform' });\n\n    return new AnthropicMessagesLanguageModel(modelId, settings, {\n      provider: 'anthropic.messages',\n      baseURL,\n      headers: () => ({\n        ...options.headers\n      }),\n      fetch: options.fetch,\n      projectId: config.projectId,\n      region: config.region,\n      googleAuth: auth\n    });\n  }\n\n  const provider = function (\n    modelId: AnthropicMessagesModelId,\n    settings?: AnthropicMessagesSettings,\n  ) {\n    if (new.target) {\n      throw new Error(\n        'The Anthropic 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\n  return provider as AnthropicVertexProvider;\n}\n\n/**\nDefault Anthropic provider instance.\n */\nexport const anthropicVertex = createAnthropicVertex();","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1FunctionToolCall,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\nimport { anthropicFailedResponseHandler } from './anthropic-error';\nimport {\n  AnthropicMessagesModelId,\n  AnthropicMessagesSettings,\n} from './anthropic-messages-settings';\nimport { convertToAnthropicMessagesPrompt } from './convert-to-anthropic-messages-prompt';\nimport { mapAnthropicStopReason } from './map-anthropic-stop-reason';\nimport { GoogleAuth } from 'google-auth-library';\n\ntype AnthropicMessagesConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: typeof fetch;\n  projectId?: string;\n  region?: string;\n  googleAuth?: GoogleAuth;\n};\n\nexport class AnthropicMessagesLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n  readonly defaultObjectGenerationMode = 'tool';\n  readonly supportsImageUrls = false;\n  readonly defaultVersion = 'vertex-2023-10-16';\n\n  readonly modelId: AnthropicMessagesModelId;\n  readonly settings: AnthropicMessagesSettings;\n  readonly path: string;\n\n  private readonly config: AnthropicMessagesConfig;\n\n  constructor(\n    modelId: AnthropicMessagesModelId,\n    settings: AnthropicMessagesSettings,\n    config: AnthropicMessagesConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n    this.path = `/projects/${config.projectId}/locations/${config.region}/publishers/anthropic/models/${modelId}`;\n  }\n\n  get provider(): string {\n    return this.config.provider;\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  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\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 (seed != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'seed',\n      });\n    }\n\n    if (responseFormat != null && responseFormat.type !== 'text') {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'JSON response format is not supported.',\n      });\n    }\n\n    const messagesPrompt = convertToAnthropicMessagesPrompt(prompt);\n\n    const baseArgs = {\n      anthropic_version: this.defaultVersion,\n      // model specific settings:\n      top_k: topK ?? this.settings.topK,\n\n      // standardized settings:\n      max_tokens: maxTokens ?? 4096, // 4096: max model output tokens\n      temperature,\n      top_p: topP,\n      stop_sequences: stopSequences,\n\n      // prompt:\n      system: messagesPrompt.system,\n      messages: messagesPrompt.messages,\n    };\n\n    switch (type) {\n      case 'regular': {\n        return {\n          args: { ...baseArgs, ...prepareToolsAndToolChoice(mode) },\n          warnings,\n        };\n      }\n\n      case 'object-json': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'json-mode object generation',\n        });\n      }\n\n      case 'object-tool': {\n        const { name, description, parameters } = mode.tool;\n\n        return {\n          args: {\n            ...baseArgs,\n            tools: [{ name, description, input_schema: parameters }],\n            tool_choice: { type: 'tool', name },\n          },\n          warnings,\n        };\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { args, warnings } = await this.getArgs(options);\n\n    const authClient = await this.config.googleAuth?.getClient(); \n    const authHeaders = await authClient?.getRequestHeaders();\n\n    options.headers = { ...authHeaders, ...options.headers };\n\n    const specifier = 'rawPredict';\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}${this.path}:${specifier}`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        anthropicMessagesResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n\n    // extract text\n    let text = '';\n    for (const content of response.content) {\n      if (content.type === 'text') {\n        text += content.text;\n      }\n    }\n\n    // extract tool calls\n    let toolCalls: LanguageModelV1FunctionToolCall[] | undefined = undefined;\n    if (response.content.some(content => content.type === 'tool_use')) {\n      toolCalls = [];\n      for (const content of response.content) {\n        if (content.type === 'tool_use') {\n          toolCalls.push({\n            toolCallType: 'function',\n            toolCallId: content.id,\n            toolName: content.name,\n            args: JSON.stringify(content.input),\n          });\n        }\n      }\n    }\n\n    return {\n      text,\n      toolCalls,\n      finishReason: mapAnthropicStopReason(response.stop_reason),\n      usage: {\n        promptTokens: response.usage.input_tokens,\n        completionTokens: response.usage.output_tokens,\n      },\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      warnings,\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);\n\n    const authClient = await this.config.googleAuth?.getClient(); \n    const authHeaders = await authClient?.getRequestHeaders();\n\n    options.headers = { ...authHeaders, ...options.headers };\n\n    const specifier = 'streamRawPredict';\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}${this.path}:${specifier}`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: {\n        ...args,\n        stream: true,\n      },\n      failedResponseHandler: anthropicFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        anthropicMessagesChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n\n    let finishReason: LanguageModelV1FinishReason = 'other';\n    const usage: { promptTokens: number; completionTokens: number } = {\n      promptTokens: Number.NaN,\n      completionTokens: Number.NaN,\n    };\n\n    const toolCallContentBlocks: Record<\n      number,\n      {\n        toolCallId: string;\n        toolName: string;\n        jsonText: string;\n      }\n    > = {};\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof anthropicMessagesChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            switch (value.type) {\n              case 'ping': {\n                return; // ignored\n              }\n\n              case 'content_block_start': {\n                const contentBlockType = value.content_block.type;\n\n                switch (contentBlockType) {\n                  case 'text': {\n                    return; // ignored\n                  }\n\n                  case 'tool_use': {\n                    toolCallContentBlocks[value.index] = {\n                      toolCallId: value.content_block.id,\n                      toolName: value.content_block.name,\n                      jsonText: '',\n                    };\n                    return;\n                  }\n\n                  default: {\n                    const _exhaustiveCheck: never = contentBlockType;\n                    throw new Error(\n                      `Unsupported content block type: ${_exhaustiveCheck}`,\n                    );\n                  }\n                }\n              }\n\n              case 'content_block_stop': {\n                // when finishing a tool call block, send the full tool call:\n                if (toolCallContentBlocks[value.index] != null) {\n                  const contentBlock = toolCallContentBlocks[value.index];\n\n                  controller.enqueue({\n                    type: 'tool-call',\n                    toolCallType: 'function',\n                    toolCallId: contentBlock.toolCallId,\n                    toolName: contentBlock.toolName,\n                    args: contentBlock.jsonText,\n                  });\n\n                  delete toolCallContentBlocks[value.index];\n                }\n\n                return;\n              }\n\n              case 'content_block_delta': {\n                const deltaType = value.delta.type;\n                switch (deltaType) {\n                  case 'text_delta': {\n                    controller.enqueue({\n                      type: 'text-delta',\n                      textDelta: value.delta.text,\n                    });\n\n                    return;\n                  }\n\n                  case 'input_json_delta': {\n                    const contentBlock = toolCallContentBlocks[value.index];\n\n                    controller.enqueue({\n                      type: 'tool-call-delta',\n                      toolCallType: 'function',\n                      toolCallId: contentBlock.toolCallId,\n                      toolName: contentBlock.toolName,\n                      argsTextDelta: value.delta.partial_json,\n                    });\n\n                    contentBlock.jsonText += value.delta.partial_json;\n\n                    return;\n                  }\n\n                  default: {\n                    const _exhaustiveCheck: never = deltaType;\n                    throw new Error(\n                      `Unsupported delta type: ${_exhaustiveCheck}`,\n                    );\n                  }\n                }\n              }\n\n              case 'message_start': {\n                usage.promptTokens = value.message.usage.input_tokens;\n                usage.completionTokens = value.message.usage.output_tokens;\n                return;\n              }\n\n              case 'message_delta': {\n                usage.completionTokens = value.usage.output_tokens;\n                finishReason = mapAnthropicStopReason(value.delta.stop_reason);\n                return;\n              }\n\n              case 'message_stop': {\n                controller.enqueue({ type: 'finish', finishReason, usage });\n                return;\n              }\n\n              default: {\n                const _exhaustiveCheck: never = value;\n                throw new Error(`Unsupported chunk type: ${_exhaustiveCheck}`);\n              }\n            }\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\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 anthropicMessagesResponseSchema = z.object({\n  type: z.literal('message'),\n  content: z.array(\n    z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('text'),\n        text: z.string(),\n      }),\n      z.object({\n        type: z.literal('tool_use'),\n        id: z.string(),\n        name: z.string(),\n        input: z.unknown(),\n      }),\n    ]),\n  ),\n  stop_reason: z.string().optional().nullable(),\n  usage: z.object({\n    input_tokens: z.number(),\n    output_tokens: z.number(),\n  }),\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst anthropicMessagesChunkSchema = z.discriminatedUnion('type', [\n  z.object({\n    type: z.literal('message_start'),\n    message: z.object({\n      usage: z.object({\n        input_tokens: z.number(),\n        output_tokens: z.number(),\n      }),\n    }),\n  }),\n  z.object({\n    type: z.literal('content_block_start'),\n    index: z.number(),\n    content_block: z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('text'),\n        text: z.string(),\n      }),\n      z.object({\n        type: z.literal('tool_use'),\n        id: z.string(),\n        name: z.string(),\n      }),\n    ]),\n  }),\n  z.object({\n    type: z.literal('content_block_delta'),\n    index: z.number(),\n    delta: z.discriminatedUnion('type', [\n      z.object({\n        type: z.literal('input_json_delta'),\n        partial_json: z.string(),\n      }),\n      z.object({\n        type: z.literal('text_delta'),\n        text: z.string(),\n      }),\n    ]),\n  }),\n  z.object({\n    type: z.literal('content_block_stop'),\n    index: z.number(),\n  }),\n  z.object({\n    type: z.literal('message_delta'),\n    delta: z.object({ stop_reason: z.string().optional().nullable() }),\n    usage: z.object({ output_tokens: z.number() }),\n  }),\n  z.object({\n    type: z.literal('message_stop'),\n  }),\n  z.object({\n    type: z.literal('ping'),\n  }),\n]);\n\nfunction prepareToolsAndToolChoice(\n  mode: Parameters<LanguageModelV1['doGenerate']>[0]['mode'] & {\n    type: 'regular';\n  },\n) {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  const tools = mode.tools?.length ? mode.tools : undefined;\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: undefined };\n  }\n\n  const mappedTools = tools.map(tool => ({\n    name: tool.name,\n    description: tool.description,\n    input_schema: tool.parameters,\n  }));\n\n  const toolChoice = mode.toolChoice;\n\n  if (toolChoice == null) {\n    return { tools: mappedTools, tool_choice: undefined };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n      return { tools: mappedTools, tool_choice: { type: 'auto' } };\n    case 'required':\n      return { tools: mappedTools, tool_choice: { type: 'any' } };\n    case 'none':\n      // Anthropic does not support 'none' tool choice, so we remove the tools:\n      return { tools: undefined, tool_choice: undefined };\n    case 'tool':\n      return {\n        tools: mappedTools,\n        tool_choice: { type: 'tool', name: toolChoice.toolName },\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new Error(`Unsupported tool choice type: ${_exhaustiveCheck}`);\n    }\n  }\n}\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\n\nconst anthropicErrorDataSchema = z.object({\n  type: z.literal('error'),\n  error: z.object({\n    type: z.string(),\n    message: z.string(),\n  }),\n});\n\nexport type AnthropicErrorData = z.infer<typeof anthropicErrorDataSchema>;\n\nexport const anthropicFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: anthropicErrorDataSchema,\n  errorToMessage: data => data.error.message,\n});\n","import {\n  LanguageModelV1Message,\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/provider-utils';\nimport {\n  AnthropicMessage,\n  AnthropicMessagesPrompt,\n  AnthropicUserMessage,\n} from './anthropic-messages-prompt';\n\nexport function convertToAnthropicMessagesPrompt(\n  prompt: LanguageModelV1Prompt,\n): AnthropicMessagesPrompt {\n  const blocks = groupIntoBlocks(prompt);\n\n  let system: string | undefined = undefined;\n  const messages: AnthropicMessage[] = [];\n\n  for (let i = 0; i < blocks.length; i++) {\n    const block = blocks[i];\n    const type = block.type;\n\n    switch (type) {\n      case 'system': {\n        if (system != null) {\n          throw new UnsupportedFunctionalityError({\n            functionality:\n              'Multiple system messages that are separated by user/assistant messages',\n          });\n        }\n\n        system = block.messages.map(({ content }) => content).join('\\n');\n        break;\n      }\n\n      case 'user': {\n        // combines all user and tool messages in this block into a single message:\n        const anthropicContent: AnthropicUserMessage['content'] = [];\n\n        for (const { role, content } of block.messages) {\n          switch (role) {\n            case 'user': {\n              for (const part of content) {\n                switch (part.type) {\n                  case 'text': {\n                    anthropicContent.push({ type: 'text', text: part.text });\n                    break;\n                  }\n                  case 'image': {\n                    if (part.image instanceof URL) {\n                      // The AI SDK automatically downloads images for user image parts with URLs\n                      throw new UnsupportedFunctionalityError({\n                        functionality: 'Image URLs in user messages',\n                      });\n                    }\n\n                    anthropicContent.push({\n                      type: 'image',\n                      source: {\n                        type: 'base64',\n                        media_type: part.mimeType ?? 'image/jpeg',\n                        data: convertUint8ArrayToBase64(part.image),\n                      },\n                    });\n\n                    break;\n                  }\n                }\n              }\n\n              break;\n            }\n            case 'tool': {\n              for (const part of content) {\n                anthropicContent.push({\n                  type: 'tool_result',\n                  tool_use_id: part.toolCallId,\n                  content: JSON.stringify(part.result),\n                  is_error: part.isError,\n                });\n              }\n\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = role;\n              throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n            }\n          }\n        }\n\n        messages.push({ role: 'user', content: anthropicContent });\n\n        break;\n      }\n\n      case 'assistant': {\n        if (block.messages.length > 1) {\n          throw new UnsupportedFunctionalityError({\n            functionality: 'Multiple assistant messages in block',\n          });\n        }\n\n        const { content } = block.messages[0];\n\n        messages.push({\n          role: 'assistant',\n          content: content.map((part, j) => {\n            switch (part.type) {\n              case 'text': {\n                // trim the last text part if it's the last message in the block\n                // because Anthropic does not allow trailing whitespace\n                // in pre-filled assistant responses\n                if (\n                  i === blocks.length - 1 &&\n                  j === block.messages.length - 1\n                ) {\n                  return { type: 'text', text: part.text.trim() };\n                }\n\n                return { type: 'text', text: part.text };\n              }\n              case 'tool-call': {\n                return {\n                  type: 'tool_use',\n                  id: part.toolCallId,\n                  name: part.toolName,\n                  input: part.args,\n                };\n              }\n            }\n          }),\n        });\n\n        break;\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return {\n    system,\n    messages,\n  };\n}\n\ntype SystemBlock = {\n  type: 'system';\n  messages: Array<LanguageModelV1Message & { role: 'system' }>;\n};\ntype AssistantBlock = {\n  type: 'assistant';\n  messages: Array<LanguageModelV1Message & { role: 'assistant' }>;\n};\ntype UserBlock = {\n  type: 'user';\n  messages: Array<LanguageModelV1Message & { role: 'user' | 'tool' }>;\n};\n\nfunction groupIntoBlocks(\n  prompt: LanguageModelV1Prompt,\n): Array<SystemBlock | AssistantBlock | UserBlock> {\n  const blocks: Array<SystemBlock | AssistantBlock | UserBlock> = [];\n  let currentBlock: SystemBlock | AssistantBlock | UserBlock | undefined =\n    undefined;\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        if (currentBlock?.type !== 'system') {\n          currentBlock = { type: 'system', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push({ role, content });\n        break;\n      }\n      case 'assistant': {\n        if (currentBlock?.type !== 'assistant') {\n          currentBlock = { type: 'assistant', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push({ role, content });\n        break;\n      }\n      case 'user': {\n        if (currentBlock?.type !== 'user') {\n          currentBlock = { type: 'user', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push({ role, content });\n        break;\n      }\n      case 'tool': {\n        if (currentBlock?.type !== 'user') {\n          currentBlock = { type: 'user', messages: [] };\n          blocks.push(currentBlock);\n        }\n\n        currentBlock.messages.push({ role, content });\n        break;\n      }\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return blocks;\n}\n","import { LanguageModelV1FinishReason } from '@ai-sdk/provider';\n\nexport function mapAnthropicStopReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\n  switch (finishReason) {\n    case 'end_turn':\n    case 'stop_sequence':\n      return 'stop';\n    case 'tool_use':\n      return 'tool-calls';\n    case 'max_tokens':\n      return 'length';\n    default:\n      return 'other';\n  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