@ai-sdk/amazon-bedrock/dist/index.mjs.map

The **[Amazon Bedrock provider](https://ai-sdk.dev/providers/ai-sdk-providers/amazon-bedrock)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the Amazon Bedrock [converse API](https://docs.aws.amazon.com/bedrock/latest/APIR

{"version":3,"sources":["../src/bedrock-provider.ts","../src/bedrock-chat-language-model.ts","../src/bedrock-api-types.ts","../src/bedrock-chat-options.ts","../src/bedrock-error.ts","../src/bedrock-event-stream-response-handler.ts","../src/bedrock-prepare-tools.ts","../src/convert-to-bedrock-chat-messages.ts","../src/map-bedrock-finish-reason.ts","../src/bedrock-embedding-model.ts","../src/bedrock-embedding-options.ts","../src/bedrock-image-model.ts","../src/bedrock-image-settings.ts","../src/headers-utils.ts","../src/bedrock-sigv4-fetch.ts"],"sourcesContent":["import {\n  EmbeddingModelV2,\n  ImageModelV2,\n  LanguageModelV2,\n  ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadOptionalSetting,\n  loadSetting,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport { anthropicTools } from '@ai-sdk/anthropic/internal';\nimport { BedrockChatLanguageModel } from './bedrock-chat-language-model';\nimport { BedrockChatModelId } from './bedrock-chat-options';\nimport { BedrockEmbeddingModel } from './bedrock-embedding-model';\nimport { BedrockEmbeddingModelId } from './bedrock-embedding-options';\nimport { BedrockImageModel } from './bedrock-image-model';\nimport { BedrockImageModelId } from './bedrock-image-settings';\nimport {\n  BedrockCredentials,\n  createSigV4FetchFunction,\n  createApiKeyFetchFunction,\n} from './bedrock-sigv4-fetch';\n\nexport interface AmazonBedrockProviderSettings {\n  /**\nThe AWS region to use for the Bedrock provider. Defaults to the value of the\n`AWS_REGION` environment variable.\n   */\n  region?: string;\n\n  /**\nAPI key for authenticating requests using Bearer token authentication.\nWhen provided, this will be used instead of AWS SigV4 authentication.\nDefaults to the value of the `AWS_BEARER_TOKEN_BEDROCK` environment variable.\n\n@example\n```typescript\n// Using API key directly\nconst bedrock = createAmazonBedrock({\n  apiKey: 'your-api-key-here',\n  region: 'us-east-1'\n});\n\n// Using environment variable AWS_BEARER_TOKEN_BEDROCK\nconst bedrock = createAmazonBedrock({\n  region: 'us-east-1'\n});\n```\n\nNote: When `apiKey` is provided, it takes precedence over AWS SigV4 authentication.\nIf neither `apiKey` nor `AWS_BEARER_TOKEN_BEDROCK` environment variable is set,\nthe provider will fall back to AWS SigV4 authentication using AWS credentials.\n   */\n  apiKey?: string;\n\n  /**\nThe AWS access key ID to use for the Bedrock provider. Defaults to the value of the\n`AWS_ACCESS_KEY_ID` environment variable.\n   */\n  accessKeyId?: string;\n\n  /**\nThe AWS secret access key to use for the Bedrock provider. Defaults to the value of the\n`AWS_SECRET_ACCESS_KEY` environment variable.\n   */\n  secretAccessKey?: string;\n\n  /**\nThe AWS session token to use for the Bedrock provider. Defaults to the value of the\n`AWS_SESSION_TOKEN` environment variable.\n   */\n  sessionToken?: string;\n\n  /**\nBase URL for the Bedrock API calls.\n   */\n  baseURL?: 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  /**\nThe AWS credential provider to use for the Bedrock provider to get dynamic\ncredentials similar to the AWS SDK. Setting a provider here will cause its\ncredential values to be used instead of the `accessKeyId`, `secretAccessKey`,\nand `sessionToken` settings.\n   */\n  credentialProvider?: () => PromiseLike<Omit<BedrockCredentials, 'region'>>;\n\n  // for testing\n  generateId?: () => string;\n}\n\nexport interface AmazonBedrockProvider extends ProviderV2 {\n  (modelId: BedrockChatModelId): LanguageModelV2;\n\n  languageModel(modelId: BedrockChatModelId): LanguageModelV2;\n\n  embedding(modelId: BedrockEmbeddingModelId): EmbeddingModelV2<string>;\n\n  /**\nCreates a model for image generation.\n   */\n  image(modelId: BedrockImageModelId): ImageModelV2;\n\n  /**\nCreates a model for image generation.\n   */\n  imageModel(modelId: BedrockImageModelId): ImageModelV2;\n\n  /**\nAnthropic-specific tools that can be used with Anthropic models on Bedrock.\n   */\n  tools: typeof anthropicTools;\n}\n\n/**\nCreate an Amazon Bedrock provider instance.\n */\nexport function createAmazonBedrock(\n  options: AmazonBedrockProviderSettings = {},\n): AmazonBedrockProvider {\n  // Check for API key authentication first\n  const rawApiKey = loadOptionalSetting({\n    settingValue: options.apiKey,\n    environmentVariableName: 'AWS_BEARER_TOKEN_BEDROCK',\n  });\n\n  // FIX 1: Validate API key to ensure proper fallback to SigV4\n  // Only use API key if it's a non-empty, non-whitespace string\n  const apiKey =\n    rawApiKey && rawApiKey.trim().length > 0 ? rawApiKey.trim() : undefined;\n\n  // Use API key authentication if available, otherwise fall back to SigV4\n  const fetchFunction = apiKey\n    ? createApiKeyFetchFunction(apiKey, options.fetch)\n    : createSigV4FetchFunction(async () => {\n        const region = loadSetting({\n          settingValue: options.region,\n          settingName: 'region',\n          environmentVariableName: 'AWS_REGION',\n          description: 'AWS region',\n        });\n\n        // If a credential provider is provided, use it to get the credentials.\n        if (options.credentialProvider) {\n          try {\n            return {\n              ...(await options.credentialProvider()),\n              region,\n            };\n          } catch (error) {\n            // Error handling for credential provider failures\n            const errorMessage =\n              error instanceof Error ? error.message : String(error);\n            throw new Error(\n              `AWS credential provider failed: ${errorMessage}. ` +\n                'Please ensure your credential provider returns valid AWS credentials ' +\n                'with accessKeyId and secretAccessKey properties.',\n            );\n          }\n        }\n\n        // Enhanced error handling for SigV4 credential loading\n        try {\n          return {\n            region,\n            accessKeyId: loadSetting({\n              settingValue: options.accessKeyId,\n              settingName: 'accessKeyId',\n              environmentVariableName: 'AWS_ACCESS_KEY_ID',\n              description: 'AWS access key ID',\n            }),\n            secretAccessKey: loadSetting({\n              settingValue: options.secretAccessKey,\n              settingName: 'secretAccessKey',\n              environmentVariableName: 'AWS_SECRET_ACCESS_KEY',\n              description: 'AWS secret access key',\n            }),\n            sessionToken: loadOptionalSetting({\n              settingValue: options.sessionToken,\n              environmentVariableName: 'AWS_SESSION_TOKEN',\n            }),\n          };\n        } catch (error) {\n          // Provide helpful error message for missing AWS credentials\n          const errorMessage =\n            error instanceof Error ? error.message : String(error);\n          if (\n            errorMessage.includes('AWS_ACCESS_KEY_ID') ||\n            errorMessage.includes('accessKeyId')\n          ) {\n            throw new Error(\n              'AWS SigV4 authentication requires AWS credentials. Please provide either:\\n' +\n                '1. Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables\\n' +\n                '2. Provide accessKeyId and secretAccessKey in options\\n' +\n                '3. Use a credentialProvider function\\n' +\n                '4. Use API key authentication with AWS_BEARER_TOKEN_BEDROCK or apiKey option\\n' +\n                `Original error: ${errorMessage}`,\n            );\n          }\n          if (\n            errorMessage.includes('AWS_SECRET_ACCESS_KEY') ||\n            errorMessage.includes('secretAccessKey')\n          ) {\n            throw new Error(\n              'AWS SigV4 authentication requires both AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. ' +\n                'Please ensure both credentials are provided.\\n' +\n                `Original error: ${errorMessage}`,\n            );\n          }\n          // Re-throw other errors as-is\n          throw error;\n        }\n      }, options.fetch);\n\n  const getBaseUrl = (): string =>\n    withoutTrailingSlash(\n      options.baseURL ??\n        `https://bedrock-runtime.${loadSetting({\n          settingValue: options.region,\n          settingName: 'region',\n          environmentVariableName: 'AWS_REGION',\n          description: 'AWS region',\n        })}.amazonaws.com`,\n    ) ?? `https://bedrock-runtime.us-east-1.amazonaws.com`;\n\n  const createChatModel = (modelId: BedrockChatModelId) =>\n    new BedrockChatLanguageModel(modelId, {\n      baseUrl: getBaseUrl,\n      headers: options.headers ?? {},\n      fetch: fetchFunction,\n      generateId,\n    });\n\n  const provider = function (modelId: BedrockChatModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Amazon Bedrock model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(modelId);\n  };\n\n  const createEmbeddingModel = (modelId: BedrockEmbeddingModelId) =>\n    new BedrockEmbeddingModel(modelId, {\n      baseUrl: getBaseUrl,\n      headers: options.headers ?? {},\n      fetch: fetchFunction,\n    });\n\n  const createImageModel = (modelId: BedrockImageModelId) =>\n    new BedrockImageModel(modelId, {\n      baseUrl: getBaseUrl,\n      headers: options.headers ?? {},\n      fetch: fetchFunction,\n    });\n\n  provider.languageModel = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.image = createImageModel;\n  provider.imageModel = createImageModel;\n  provider.tools = anthropicTools;\n\n  return provider;\n}\n\n/**\nDefault Bedrock provider instance.\n */\nexport const bedrock = createAmazonBedrock();\n","import {\n  JSONObject,\n  LanguageModelV2,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2Reasoning,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n  SharedV2ProviderMetadata,\n  LanguageModelV2FunctionTool,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  Resolvable,\n  combineHeaders,\n  createJsonErrorResponseHandler,\n  createJsonResponseHandler,\n  parseProviderOptions,\n  postJsonToApi,\n  resolve,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport {\n  BEDROCK_STOP_REASONS,\n  BedrockConverseInput,\n  BedrockStopReason,\n} from './bedrock-api-types';\nimport {\n  BedrockChatModelId,\n  bedrockProviderOptions,\n} from './bedrock-chat-options';\nimport { BedrockErrorSchema } from './bedrock-error';\nimport { createBedrockEventStreamResponseHandler } from './bedrock-event-stream-response-handler';\nimport { prepareTools } from './bedrock-prepare-tools';\nimport { convertToBedrockChatMessages } from './convert-to-bedrock-chat-messages';\nimport { mapBedrockFinishReason } from './map-bedrock-finish-reason';\n\ntype BedrockChatConfig = {\n  baseUrl: () => string;\n  headers: Resolvable<Record<string, string | undefined>>;\n  fetch?: FetchFunction;\n  generateId: () => string;\n};\n\nexport class BedrockChatLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\n  readonly provider = 'amazon-bedrock';\n\n  constructor(\n    readonly modelId: BedrockChatModelId,\n    private readonly config: BedrockChatConfig,\n  ) {}\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    tools,\n    toolChoice,\n    providerOptions,\n  }: Parameters<LanguageModelV2['doGenerate']>[0]): Promise<{\n    command: BedrockConverseInput;\n    warnings: LanguageModelV2CallWarning[];\n    usesJsonResponseTool: boolean;\n    betas: Set<string>;\n  }> {\n    // Parse provider options\n    const bedrockOptions =\n      (await parseProviderOptions({\n        provider: 'bedrock',\n        providerOptions,\n        schema: bedrockProviderOptions,\n      })) ?? {};\n\n    const warnings: LanguageModelV2CallWarning[] = [];\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 (\n      responseFormat != null &&\n      responseFormat.type !== 'text' &&\n      responseFormat.type !== 'json'\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'Only text and json response formats are supported.',\n      });\n    }\n\n    if (tools != null && responseFormat?.type === 'json') {\n      if (tools.length > 0) {\n        warnings.push({\n          type: 'other',\n          message:\n            'JSON response format does not support tools. ' +\n            'The provided tools are ignored.',\n        });\n      }\n    }\n\n    const jsonResponseTool: LanguageModelV2FunctionTool | undefined =\n      responseFormat?.type === 'json' && responseFormat.schema != null\n        ? {\n            type: 'function',\n            name: 'json',\n            description: 'Respond with a JSON object.',\n            inputSchema: responseFormat.schema,\n          }\n        : undefined;\n\n    const { toolConfig, additionalTools, toolWarnings, betas } = prepareTools({\n      tools: jsonResponseTool ? [jsonResponseTool, ...(tools ?? [])] : tools,\n      toolChoice:\n        jsonResponseTool != null\n          ? { type: 'tool', toolName: jsonResponseTool.name }\n          : toolChoice,\n      modelId: this.modelId,\n    });\n\n    warnings.push(...toolWarnings);\n\n    if (additionalTools) {\n      bedrockOptions.additionalModelRequestFields = {\n        ...bedrockOptions.additionalModelRequestFields,\n        ...additionalTools,\n      };\n    }\n\n    const isThinking = bedrockOptions.reasoningConfig?.type === 'enabled';\n    const thinkingBudget = bedrockOptions.reasoningConfig?.budgetTokens;\n\n    const inferenceConfig = {\n      ...(maxOutputTokens != null && { maxOutputTokens }),\n      ...(temperature != null && { temperature }),\n      ...(topP != null && { topP }),\n      ...(topK != null && { topK }),\n      ...(stopSequences != null && { stopSequences }),\n    };\n\n    // Adjust maxOutputTokens if thinking is enabled\n    if (isThinking && thinkingBudget != null) {\n      if (inferenceConfig.maxOutputTokens != null) {\n        inferenceConfig.maxOutputTokens += thinkingBudget;\n      } else {\n        inferenceConfig.maxOutputTokens = thinkingBudget + 4096; // Default + thinking budget maxOutputTokens = 4096, TODO update default in v5\n      }\n      // Add them to additional model request fields\n      // Add thinking config to additionalModelRequestFields\n      bedrockOptions.additionalModelRequestFields = {\n        ...bedrockOptions.additionalModelRequestFields,\n        thinking: {\n          type: bedrockOptions.reasoningConfig?.type,\n          budget_tokens: thinkingBudget,\n        },\n      };\n    }\n\n    // Remove temperature if thinking is enabled\n    if (isThinking && inferenceConfig.temperature != null) {\n      delete inferenceConfig.temperature;\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'temperature',\n        details: 'temperature is not supported when thinking is enabled',\n      });\n    }\n\n    // Remove topP if thinking is enabled\n    if (isThinking && inferenceConfig.topP != null) {\n      delete inferenceConfig.topP;\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topP',\n        details: 'topP is not supported when thinking is enabled',\n      });\n    }\n\n    if (isThinking && inferenceConfig.topK != null) {\n      delete inferenceConfig.topK;\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n        details: 'topK is not supported when thinking is enabled',\n      });\n    }\n\n    // Filter tool content from prompt when no tools are available\n    const hasAnyTools = (toolConfig.tools?.length ?? 0) > 0 || additionalTools;\n    let filteredPrompt = prompt;\n\n    if (!hasAnyTools) {\n      const hasToolContent = prompt.some(\n        message =>\n          'content' in message &&\n          Array.isArray(message.content) &&\n          message.content.some(\n            part => part.type === 'tool-call' || part.type === 'tool-result',\n          ),\n      );\n\n      if (hasToolContent) {\n        filteredPrompt = prompt\n          .map(message =>\n            message.role === 'system'\n              ? message\n              : {\n                  ...message,\n                  content: message.content.filter(\n                    part =>\n                      part.type !== 'tool-call' && part.type !== 'tool-result',\n                  ),\n                },\n          )\n          .filter(\n            message => message.role === 'system' || message.content.length > 0,\n          ) as typeof prompt;\n\n        warnings.push({\n          type: 'unsupported-setting',\n          setting: 'toolContent',\n          details:\n            'Tool calls and results removed from conversation because Bedrock does not support tool content without active tools.',\n        });\n      }\n    }\n\n    const { system, messages } =\n      await convertToBedrockChatMessages(filteredPrompt);\n\n    // Filter out reasoningConfig from providerOptions.bedrock to prevent sending it to Bedrock API\n    const { reasoningConfig: _, ...filteredBedrockOptions } =\n      providerOptions?.bedrock || {};\n\n    return {\n      command: {\n        system,\n        messages,\n        additionalModelRequestFields:\n          bedrockOptions.additionalModelRequestFields,\n        ...(Object.keys(inferenceConfig).length > 0 && {\n          inferenceConfig,\n        }),\n        ...filteredBedrockOptions,\n        ...(toolConfig.tools !== undefined && toolConfig.tools.length > 0\n          ? { toolConfig }\n          : {}),\n      },\n      warnings,\n      usesJsonResponseTool: jsonResponseTool != null,\n      betas,\n    };\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    // no supported urls for bedrock\n  };\n\n  private async getHeaders({\n    betas,\n    headers,\n  }: {\n    betas: Set<string>;\n    headers: Record<string, string | undefined> | undefined;\n  }) {\n    return combineHeaders(\n      await resolve(this.config.headers),\n      betas.size > 0 ? { 'anthropic-beta': Array.from(betas).join(',') } : {},\n      headers,\n    );\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const {\n      command: args,\n      warnings,\n      usesJsonResponseTool,\n      betas,\n    } = await this.getArgs(options);\n\n    const url = `${this.getUrl(this.modelId)}/converse`;\n    const { value: response, responseHeaders } = await postJsonToApi({\n      url,\n      headers: await this.getHeaders({ betas, headers: options.headers }),\n      body: args,\n      failedResponseHandler: createJsonErrorResponseHandler({\n        errorSchema: BedrockErrorSchema,\n        errorToMessage: error => `${error.message ?? 'Unknown error'}`,\n      }),\n      successfulResponseHandler: createJsonResponseHandler(\n        BedrockResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const content: Array<LanguageModelV2Content> = [];\n\n    // map response content to content array\n    for (const part of response.output.message.content) {\n      // text\n      if (part.text) {\n        // when a json response tool is used, the tool call is returned as text,\n        // so we ignore the text content:\n        if (!usesJsonResponseTool) {\n          content.push({ type: 'text', text: part.text });\n        }\n      }\n\n      // reasoning\n      if (part.reasoningContent) {\n        if ('reasoningText' in part.reasoningContent) {\n          const reasoning: LanguageModelV2Reasoning = {\n            type: 'reasoning',\n            text: part.reasoningContent.reasoningText.text,\n          };\n\n          if (part.reasoningContent.reasoningText.signature) {\n            reasoning.providerMetadata = {\n              bedrock: {\n                signature: part.reasoningContent.reasoningText.signature,\n              } satisfies BedrockReasoningMetadata,\n            };\n          }\n\n          content.push(reasoning);\n        } else if ('redactedReasoning' in part.reasoningContent) {\n          content.push({\n            type: 'reasoning',\n            text: '',\n            providerMetadata: {\n              bedrock: {\n                redactedData:\n                  part.reasoningContent.redactedReasoning.data ?? '',\n              } satisfies BedrockReasoningMetadata,\n            },\n          });\n        }\n      }\n\n      // tool calls\n      if (part.toolUse) {\n        content.push(\n          // when a json response tool is used, the tool call becomes the text:\n          usesJsonResponseTool\n            ? {\n                type: 'text',\n                text: JSON.stringify(part.toolUse.input),\n              }\n            : {\n                type: 'tool-call' as const,\n                toolCallId: part.toolUse?.toolUseId ?? this.config.generateId(),\n                toolName:\n                  part.toolUse?.name ?? `tool-${this.config.generateId()}`,\n                input: JSON.stringify(part.toolUse?.input ?? ''),\n              },\n        );\n      }\n    }\n\n    // provider metadata:\n    const providerMetadata =\n      response.trace || response.usage || usesJsonResponseTool\n        ? {\n            bedrock: {\n              ...(response.trace && typeof response.trace === 'object'\n                ? { trace: response.trace as JSONObject }\n                : {}),\n              ...(response.usage?.cacheWriteInputTokens != null && {\n                usage: {\n                  cacheWriteInputTokens: response.usage.cacheWriteInputTokens,\n                },\n              }),\n              ...(usesJsonResponseTool && { isJsonResponseFromTool: true }),\n            },\n          }\n        : undefined;\n\n    return {\n      content,\n      finishReason: mapBedrockFinishReason(\n        response.stopReason as BedrockStopReason,\n      ),\n      usage: {\n        inputTokens: response.usage?.inputTokens,\n        outputTokens: response.usage?.outputTokens,\n        totalTokens: response.usage?.inputTokens + response.usage?.outputTokens,\n        cachedInputTokens: response.usage?.cacheReadInputTokens ?? undefined,\n      },\n      response: {\n        // TODO add id, timestamp, etc\n        headers: responseHeaders,\n      },\n      warnings,\n      ...(providerMetadata && { providerMetadata }),\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const {\n      command: args,\n      warnings,\n      usesJsonResponseTool,\n      betas,\n    } = await this.getArgs(options);\n    const url = `${this.getUrl(this.modelId)}/converse-stream`;\n\n    const { value: response, responseHeaders } = await postJsonToApi({\n      url,\n      headers: await this.getHeaders({ betas, headers: options.headers }),\n      body: args,\n      failedResponseHandler: createJsonErrorResponseHandler({\n        errorSchema: BedrockErrorSchema,\n        errorToMessage: error => `${error.type}: ${error.message}`,\n      }),\n      successfulResponseHandler:\n        createBedrockEventStreamResponseHandler(BedrockStreamSchema),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n    let providerMetadata: SharedV2ProviderMetadata | undefined = undefined;\n\n    const contentBlocks: Record<\n      number,\n      | {\n          type: 'tool-call';\n          toolCallId: string;\n          toolName: string;\n          jsonText: string;\n        }\n      | { type: 'text' | 'reasoning' }\n    > = {};\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof BedrockStreamSchema>>,\n          LanguageModelV2StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            function enqueueError(bedrockError: Record<string, any>) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: bedrockError });\n            }\n\n            // Emit raw chunk if requested (before anything else)\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              enqueueError(chunk.error);\n              return;\n            }\n\n            const value = chunk.value;\n\n            // handle errors:\n            if (value.internalServerException) {\n              enqueueError(value.internalServerException);\n              return;\n            }\n            if (value.modelStreamErrorException) {\n              enqueueError(value.modelStreamErrorException);\n              return;\n            }\n            if (value.throttlingException) {\n              enqueueError(value.throttlingException);\n              return;\n            }\n            if (value.validationException) {\n              enqueueError(value.validationException);\n              return;\n            }\n\n            if (value.messageStop) {\n              finishReason = mapBedrockFinishReason(\n                value.messageStop.stopReason as BedrockStopReason,\n              );\n            }\n\n            if (value.metadata) {\n              usage.inputTokens =\n                value.metadata.usage?.inputTokens ?? usage.inputTokens;\n              usage.outputTokens =\n                value.metadata.usage?.outputTokens ?? usage.outputTokens;\n              usage.totalTokens =\n                (usage.inputTokens ?? 0) + (usage.outputTokens ?? 0);\n              usage.cachedInputTokens =\n                value.metadata.usage?.cacheReadInputTokens ??\n                usage.cachedInputTokens;\n\n              const cacheUsage =\n                value.metadata.usage?.cacheWriteInputTokens != null\n                  ? {\n                      usage: {\n                        cacheWriteInputTokens:\n                          value.metadata.usage.cacheWriteInputTokens,\n                      },\n                    }\n                  : undefined;\n\n              const trace = value.metadata.trace\n                ? {\n                    trace: value.metadata.trace as JSONObject,\n                  }\n                : undefined;\n\n              if (cacheUsage || trace || usesJsonResponseTool) {\n                providerMetadata = {\n                  bedrock: {\n                    ...cacheUsage,\n                    ...trace,\n                    ...(usesJsonResponseTool && {\n                      isJsonResponseFromTool: true,\n                    }),\n                  },\n                };\n              }\n            }\n\n            if (\n              value.contentBlockStart?.contentBlockIndex != null &&\n              !value.contentBlockStart?.start?.toolUse\n            ) {\n              const blockIndex = value.contentBlockStart.contentBlockIndex;\n              contentBlocks[blockIndex] = { type: 'text' };\n              controller.enqueue({\n                type: 'text-start',\n                id: String(blockIndex),\n              });\n            }\n\n            if (\n              value.contentBlockDelta?.delta &&\n              'text' in value.contentBlockDelta.delta &&\n              value.contentBlockDelta.delta.text\n            ) {\n              const blockIndex = value.contentBlockDelta.contentBlockIndex || 0;\n\n              if (contentBlocks[blockIndex] == null) {\n                contentBlocks[blockIndex] = { type: 'text' };\n\n                // when a json response tool is used, we don't emit text events\n                if (!usesJsonResponseTool) {\n                  controller.enqueue({\n                    type: 'text-start',\n                    id: String(blockIndex),\n                  });\n                }\n              }\n\n              // when a json response tool is used, we don't emit text events\n              if (!usesJsonResponseTool) {\n                controller.enqueue({\n                  type: 'text-delta',\n                  id: String(blockIndex),\n                  delta: value.contentBlockDelta.delta.text,\n                });\n              }\n            }\n\n            if (value.contentBlockStop?.contentBlockIndex != null) {\n              const blockIndex = value.contentBlockStop.contentBlockIndex;\n              const contentBlock = contentBlocks[blockIndex];\n\n              if (contentBlock != null) {\n                if (contentBlock.type === 'reasoning') {\n                  controller.enqueue({\n                    type: 'reasoning-end',\n                    id: String(blockIndex),\n                  });\n                } else if (contentBlock.type === 'text') {\n                  // when a json response tool is used, we don't emit text events\n                  if (!usesJsonResponseTool) {\n                    controller.enqueue({\n                      type: 'text-end',\n                      id: String(blockIndex),\n                    });\n                  }\n                } else if (contentBlock.type === 'tool-call') {\n                  if (usesJsonResponseTool) {\n                    // when a json response tool is used, emit the tool input as text\n                    controller.enqueue({\n                      type: 'text-start',\n                      id: String(blockIndex),\n                    });\n                    controller.enqueue({\n                      type: 'text-delta',\n                      id: String(blockIndex),\n                      delta: contentBlock.jsonText,\n                    });\n                    controller.enqueue({\n                      type: 'text-end',\n                      id: String(blockIndex),\n                    });\n                  } else {\n                    controller.enqueue({\n                      type: 'tool-input-end',\n                      id: contentBlock.toolCallId,\n                    });\n                    controller.enqueue({\n                      type: 'tool-call',\n                      toolCallId: contentBlock.toolCallId,\n                      toolName: contentBlock.toolName,\n                      input: contentBlock.jsonText,\n                    });\n                  }\n                }\n\n                delete contentBlocks[blockIndex];\n              }\n            }\n\n            if (\n              value.contentBlockDelta?.delta &&\n              'reasoningContent' in value.contentBlockDelta.delta &&\n              value.contentBlockDelta.delta.reasoningContent\n            ) {\n              const blockIndex = value.contentBlockDelta.contentBlockIndex || 0;\n              const reasoningContent =\n                value.contentBlockDelta.delta.reasoningContent;\n\n              if ('text' in reasoningContent && reasoningContent.text) {\n                if (contentBlocks[blockIndex] == null) {\n                  contentBlocks[blockIndex] = { type: 'reasoning' };\n                  controller.enqueue({\n                    type: 'reasoning-start',\n                    id: String(blockIndex),\n                  });\n                }\n\n                controller.enqueue({\n                  type: 'reasoning-delta',\n                  id: String(blockIndex),\n                  delta: reasoningContent.text,\n                });\n              } else if (\n                'signature' in reasoningContent &&\n                reasoningContent.signature\n              ) {\n                controller.enqueue({\n                  type: 'reasoning-delta',\n                  id: String(blockIndex),\n                  delta: '',\n                  providerMetadata: {\n                    bedrock: {\n                      signature: reasoningContent.signature,\n                    } satisfies BedrockReasoningMetadata,\n                  },\n                });\n              } else if ('data' in reasoningContent && reasoningContent.data) {\n                controller.enqueue({\n                  type: 'reasoning-delta',\n                  id: String(blockIndex),\n                  delta: '',\n                  providerMetadata: {\n                    bedrock: {\n                      redactedData: reasoningContent.data,\n                    } satisfies BedrockReasoningMetadata,\n                  },\n                });\n              }\n            }\n\n            const contentBlockStart = value.contentBlockStart;\n            if (contentBlockStart?.start?.toolUse != null) {\n              const toolUse = contentBlockStart.start.toolUse;\n              const blockIndex = contentBlockStart.contentBlockIndex!;\n              contentBlocks[blockIndex] = {\n                type: 'tool-call',\n                toolCallId: toolUse.toolUseId!,\n                toolName: toolUse.name!,\n                jsonText: '',\n              };\n\n              // when a json response tool is used, we don't emit tool events\n              if (!usesJsonResponseTool) {\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: toolUse.toolUseId!,\n                  toolName: toolUse.name!,\n                });\n              }\n            }\n\n            const contentBlockDelta = value.contentBlockDelta;\n            if (\n              contentBlockDelta?.delta &&\n              'toolUse' in contentBlockDelta.delta &&\n              contentBlockDelta.delta.toolUse\n            ) {\n              const blockIndex = contentBlockDelta.contentBlockIndex!;\n              const contentBlock = contentBlocks[blockIndex];\n\n              if (contentBlock?.type === 'tool-call') {\n                const delta = contentBlockDelta.delta.toolUse.input ?? '';\n\n                // when a json response tool is used, we don't emit tool events\n                if (!usesJsonResponseTool) {\n                  controller.enqueue({\n                    type: 'tool-input-delta',\n                    id: contentBlock.toolCallId,\n                    delta: delta,\n                  });\n                }\n\n                contentBlock.jsonText += delta;\n              }\n            }\n          },\n          flush(controller) {\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage,\n              ...(providerMetadata && { providerMetadata }),\n            });\n          },\n        }),\n      ),\n      // TODO request?\n      response: { headers: responseHeaders },\n    };\n  }\n\n  private getUrl(modelId: string) {\n    const encodedModelId = encodeURIComponent(modelId);\n    return `${this.config.baseUrl()}/model/${encodedModelId}`;\n  }\n}\n\nconst BedrockStopReasonSchema = z.union([\n  z.enum(BEDROCK_STOP_REASONS),\n  z.string(),\n]);\n\nconst BedrockToolUseSchema = z.object({\n  toolUseId: z.string(),\n  name: z.string(),\n  input: z.unknown(),\n});\n\nconst BedrockReasoningTextSchema = z.object({\n  signature: z.string().nullish(),\n  text: z.string(),\n});\n\nconst BedrockRedactedReasoningSchema = z.object({\n  data: z.string(),\n});\n\n// limited version of the schema, focused on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst BedrockResponseSchema = z.object({\n  metrics: z\n    .object({\n      latencyMs: z.number(),\n    })\n    .nullish(),\n  output: z.object({\n    message: z.object({\n      content: z.array(\n        z.object({\n          text: z.string().nullish(),\n          toolUse: BedrockToolUseSchema.nullish(),\n          reasoningContent: z\n            .union([\n              z.object({\n                reasoningText: BedrockReasoningTextSchema,\n              }),\n              z.object({\n                redactedReasoning: BedrockRedactedReasoningSchema,\n              }),\n            ])\n            .nullish(),\n        }),\n      ),\n      role: z.string(),\n    }),\n  }),\n  stopReason: BedrockStopReasonSchema,\n  trace: z.unknown().nullish(),\n  usage: z.object({\n    inputTokens: z.number(),\n    outputTokens: z.number(),\n    totalTokens: z.number(),\n    cacheReadInputTokens: z.number().nullish(),\n    cacheWriteInputTokens: z.number().nullish(),\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 BedrockStreamSchema = z.object({\n  contentBlockDelta: z\n    .object({\n      contentBlockIndex: z.number(),\n      delta: z\n        .union([\n          z.object({ text: z.string() }),\n          z.object({ toolUse: z.object({ input: z.string() }) }),\n          z.object({\n            reasoningContent: z.object({ text: z.string() }),\n          }),\n          z.object({\n            reasoningContent: z.object({\n              signature: z.string(),\n            }),\n          }),\n          z.object({\n            reasoningContent: z.object({ data: z.string() }),\n          }),\n        ])\n        .nullish(),\n    })\n    .nullish(),\n  contentBlockStart: z\n    .object({\n      contentBlockIndex: z.number(),\n      start: z\n        .object({\n          toolUse: BedrockToolUseSchema.nullish(),\n        })\n        .nullish(),\n    })\n    .nullish(),\n  contentBlockStop: z\n    .object({\n      contentBlockIndex: z.number(),\n    })\n    .nullish(),\n  internalServerException: z.record(z.string(), z.unknown()).nullish(),\n  messageStop: z\n    .object({\n      additionalModelResponseFields: z\n        .record(z.string(), z.unknown())\n        .nullish(),\n      stopReason: BedrockStopReasonSchema,\n    })\n    .nullish(),\n  metadata: z\n    .object({\n      trace: z.unknown().nullish(),\n      usage: z\n        .object({\n          cacheReadInputTokens: z.number().nullish(),\n          cacheWriteInputTokens: z.number().nullish(),\n          inputTokens: z.number(),\n          outputTokens: z.number(),\n        })\n        .nullish(),\n    })\n    .nullish(),\n  modelStreamErrorException: z.record(z.string(), z.unknown()).nullish(),\n  throttlingException: z.record(z.string(), z.unknown()).nullish(),\n  validationException: z.record(z.string(), z.unknown()).nullish(),\n});\n\nexport const bedrockReasoningMetadataSchema = z.object({\n  signature: z.string().optional(),\n  redactedData: z.string().optional(),\n});\n\nexport type BedrockReasoningMetadata = z.infer<\n  typeof bedrockReasoningMetadataSchema\n>;\n","import { JSONObject } from '@ai-sdk/provider';\n\nexport interface BedrockConverseInput {\n  system?: BedrockSystemMessages;\n  messages: BedrockMessages;\n  toolConfig?: BedrockToolConfiguration;\n  inferenceConfig?: {\n    maxOutputTokens?: number;\n    temperature?: number;\n    topP?: number;\n    topK?: number;\n    stopSequences?: string[];\n  };\n  additionalModelRequestFields?: Record<string, unknown>;\n  guardrailConfig?:\n    | BedrockGuardrailConfiguration\n    | BedrockGuardrailStreamConfiguration\n    | undefined;\n}\n\nexport type BedrockSystemMessages = Array<BedrockSystemContentBlock>;\n\nexport type BedrockMessages = Array<\n  BedrockAssistantMessage | BedrockUserMessage\n>;\n\nexport interface BedrockAssistantMessage {\n  role: 'assistant';\n  content: Array<BedrockContentBlock>;\n}\n\nexport interface BedrockUserMessage {\n  role: 'user';\n  content: Array<BedrockContentBlock>;\n}\n\nexport const BEDROCK_CACHE_POINT = {\n  cachePoint: { type: 'default' },\n} as const;\n\nexport type BedrockCachePoint = { cachePoint: { type: 'default' } };\nexport type BedrockSystemContentBlock = { text: string } | BedrockCachePoint;\n\nexport interface BedrockGuardrailConfiguration {\n  guardrails?: Array<{\n    name: string;\n    description?: string;\n    parameters?: Record<string, unknown>;\n  }>;\n}\n\nexport type BedrockGuardrailStreamConfiguration = BedrockGuardrailConfiguration;\n\nexport interface BedrockToolInputSchema {\n  json: Record<string, unknown>;\n}\n\nexport interface BedrockTool {\n  toolSpec: {\n    name: string;\n    description?: string;\n    inputSchema: { json: JSONObject };\n  };\n}\n\nexport interface BedrockToolConfiguration {\n  tools?: Array<BedrockTool | BedrockCachePoint>;\n  toolChoice?:\n    | { tool: { name: string } }\n    | { auto: {} }\n    | { any: {} }\n    | undefined;\n}\n\nexport const BEDROCK_STOP_REASONS = [\n  'stop',\n  'stop_sequence',\n  'end_turn',\n  'length',\n  'max_tokens',\n  'content-filter',\n  'content_filtered',\n  'guardrail_intervened',\n  'tool-calls',\n  'tool_use',\n] as const;\n\nexport type BedrockStopReason = (typeof BEDROCK_STOP_REASONS)[number];\n\n/**\n * @see https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ImageBlock.html\n */\nexport const BEDROCK_IMAGE_MIME_TYPES = {\n  'image/jpeg': 'jpeg',\n  'image/png': 'png',\n  'image/gif': 'gif',\n  'image/webp': 'webp',\n} as const;\ntype BedrockImageFormats = typeof BEDROCK_IMAGE_MIME_TYPES;\nexport type BedrockImageFormat = BedrockImageFormats[keyof BedrockImageFormats];\nexport type BedrockImageMimeType = keyof BedrockImageFormats;\n\n/**\n * @see https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_DocumentBlock.html\n */\nexport const BEDROCK_DOCUMENT_MIME_TYPES = {\n  'application/pdf': 'pdf',\n  'text/csv': 'csv',\n  'application/msword': 'doc',\n  'application/vnd.openxmlformats-officedocument.wordprocessingml.document':\n    'docx',\n  'application/vnd.ms-excel': 'xls',\n  'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet': 'xlsx',\n  'text/html': 'html',\n  'text/plain': 'txt',\n  'text/markdown': 'md',\n} as const;\ntype BedrockDocumentFormats = typeof BEDROCK_DOCUMENT_MIME_TYPES;\nexport type BedrockDocumentFormat =\n  BedrockDocumentFormats[keyof BedrockDocumentFormats];\nexport type BedrockDocumentMimeType = keyof BedrockDocumentFormats;\n\nexport interface BedrockDocumentBlock {\n  document: {\n    format: BedrockDocumentFormat;\n    name: string;\n    source: {\n      bytes: string;\n    };\n  };\n}\n\nexport interface BedrockGuardrailConverseContentBlock {\n  guardContent: unknown;\n}\n\nexport interface BedrockImageBlock {\n  image: {\n    format: BedrockImageFormat;\n    source: {\n      bytes: string;\n    };\n  };\n}\n\nexport interface BedrockToolResultBlock {\n  toolResult: {\n    toolUseId: string;\n    content: Array<BedrockTextBlock | BedrockImageBlock>;\n  };\n}\n\nexport interface BedrockToolUseBlock {\n  toolUse: {\n    toolUseId: string;\n    name: string;\n    input: Record<string, unknown>;\n  };\n}\n\nexport interface BedrockTextBlock {\n  text: string;\n}\n\nexport interface BedrockReasoningContentBlock {\n  reasoningContent: {\n    reasoningText: {\n      text: string;\n      signature?: string;\n    };\n  };\n}\n\nexport interface BedrockRedactedReasoningContentBlock {\n  reasoningContent: {\n    redactedReasoning: {\n      data: string;\n    };\n  };\n}\n\nexport type BedrockContentBlock =\n  | BedrockDocumentBlock\n  | BedrockGuardrailConverseContentBlock\n  | BedrockImageBlock\n  | BedrockTextBlock\n  | BedrockToolResultBlock\n  | BedrockToolUseBlock\n  | BedrockReasoningContentBlock\n  | BedrockRedactedReasoningContentBlock\n  | BedrockCachePoint;\n","import { z } from 'zod/v4';\n\n// https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html\nexport type BedrockChatModelId =\n  | 'amazon.titan-tg1-large'\n  | 'amazon.titan-text-express-v1'\n  | 'anthropic.claude-v2'\n  | 'anthropic.claude-v2:1'\n  | 'anthropic.claude-instant-v1'\n  | 'anthropic.claude-sonnet-4-20250514-v1:0'\n  | 'anthropic.claude-opus-4-20250514-v1:0'\n  | 'anthropic.claude-opus-4-1-20250805-v1:0'\n  | 'anthropic.claude-3-7-sonnet-20250219-v1:0'\n  | 'anthropic.claude-3-5-sonnet-20240620-v1:0'\n  | 'anthropic.claude-3-5-sonnet-20241022-v2:0'\n  | 'anthropic.claude-3-5-haiku-20241022-v1:0'\n  | 'anthropic.claude-3-sonnet-20240229-v1:0'\n  | 'anthropic.claude-3-haiku-20240307-v1:0'\n  | 'anthropic.claude-3-opus-20240229-v1:0'\n  | 'cohere.command-text-v14'\n  | 'cohere.command-light-text-v14'\n  | 'cohere.command-r-v1:0'\n  | 'cohere.command-r-plus-v1:0'\n  | 'meta.llama3-70b-instruct-v1:0'\n  | 'meta.llama3-8b-instruct-v1:0'\n  | 'meta.llama3-1-405b-instruct-v1:0'\n  | 'meta.llama3-1-70b-instruct-v1:0'\n  | 'meta.llama3-1-8b-instruct-v1:0'\n  | 'meta.llama3-2-11b-instruct-v1:0'\n  | 'meta.llama3-2-1b-instruct-v1:0'\n  | 'meta.llama3-2-3b-instruct-v1:0'\n  | 'meta.llama3-2-90b-instruct-v1:0'\n  | 'mistral.mistral-7b-instruct-v0:2'\n  | 'mistral.mixtral-8x7b-instruct-v0:1'\n  | 'mistral.mistral-large-2402-v1:0'\n  | 'mistral.mistral-small-2402-v1:0'\n  | 'openai.gpt-oss-120b-1:0'\n  | 'openai.gpt-oss-20b-1:0'\n  | 'amazon.titan-text-express-v1'\n  | 'amazon.titan-text-lite-v1'\n  | 'us.amazon.nova-premier-v1:0'\n  | 'us.amazon.nova-pro-v1:0'\n  | 'us.amazon.nova-micro-v1:0'\n  | 'us.amazon.nova-lite-v1:0'\n  | 'us.anthropic.claude-3-sonnet-20240229-v1:0'\n  | 'us.anthropic.claude-3-opus-20240229-v1:0'\n  | 'us.anthropic.claude-3-haiku-20240307-v1:0'\n  | 'us.anthropic.claude-3-5-sonnet-20240620-v1:0'\n  | 'us.anthropic.claude-3-5-haiku-20241022-v1:0'\n  | 'us.anthropic.claude-3-5-sonnet-20241022-v2:0'\n  | 'us.anthropic.claude-3-7-sonnet-20250219-v1:0'\n  | 'us.anthropic.claude-sonnet-4-20250514-v1:0'\n  | 'us.anthropic.claude-opus-4-20250514-v1:0'\n  | 'us.anthropic.claude-opus-4-1-20250805-v1:0'\n  | 'us.meta.llama3-2-11b-instruct-v1:0'\n  | 'us.meta.llama3-2-3b-instruct-v1:0'\n  | 'us.meta.llama3-2-90b-instruct-v1:0'\n  | 'us.meta.llama3-2-1b-instruct-v1:0'\n  | 'us.meta.llama3-1-8b-instruct-v1:0'\n  | 'us.meta.llama3-1-70b-instruct-v1:0'\n  | 'us.meta.llama3-3-70b-instruct-v1:0'\n  | 'us.deepseek.r1-v1:0'\n  | 'us.mistral.pixtral-large-2502-v1:0'\n  | 'us.meta.llama4-scout-17b-instruct-v1:0'\n  | 'us.meta.llama4-maverick-17b-instruct-v1:0'\n  | (string & {});\n\nexport const bedrockProviderOptions = z.object({\n  /**\n   * Additional inference parameters that the model supports,\n   * beyond the base set of inference parameters that Converse\n   * supports in the inferenceConfig field\n   */\n  additionalModelRequestFields: z.record(z.string(), z.any()).optional(),\n  reasoningConfig: z\n    .object({\n      type: z.union([z.literal('enabled'), z.literal('disabled')]).optional(),\n      budgetTokens: z.number().optional(),\n    })\n    .optional(),\n});\n\nexport type BedrockProviderOptions = z.infer<typeof bedrockProviderOptions>;\n","import { z } from 'zod/v4';\n\nexport const BedrockErrorSchema = z.object({\n  message: z.string(),\n  type: z.string().nullish(),\n});\n","import { EmptyResponseBodyError } from '@ai-sdk/provider';\nimport {\n  ParseResult,\n  safeParseJSON,\n  extractResponseHeaders,\n  ResponseHandler,\n  safeValidateTypes,\n} from '@ai-sdk/provider-utils';\nimport { EventStreamCodec } from '@smithy/eventstream-codec';\nimport { toUtf8, fromUtf8 } from '@smithy/util-utf8';\nimport { ZodType } from 'zod/v4';\n\n// https://docs.aws.amazon.com/lexv2/latest/dg/event-stream-encoding.html\nexport const createBedrockEventStreamResponseHandler =\n  <T>(\n    chunkSchema: ZodType<T, any>,\n  ): ResponseHandler<ReadableStream<ParseResult<T>>> =>\n  async ({ response }: { response: Response }) => {\n    const responseHeaders = extractResponseHeaders(response);\n\n    if (response.body == null) {\n      throw new EmptyResponseBodyError({});\n    }\n\n    const codec = new EventStreamCodec(toUtf8, fromUtf8);\n    let buffer = new Uint8Array(0);\n    const textDecoder = new TextDecoder();\n\n    return {\n      responseHeaders,\n      value: response.body.pipeThrough(\n        new TransformStream<Uint8Array, ParseResult<T>>({\n          async transform(chunk, controller) {\n            // Append new chunk to buffer.\n            const newBuffer = new Uint8Array(buffer.length + chunk.length);\n            newBuffer.set(buffer);\n            newBuffer.set(chunk, buffer.length);\n            buffer = newBuffer;\n\n            // Try to decode messages from buffer.\n            while (buffer.length >= 4) {\n              // The first 4 bytes are the total length (big-endian).\n              const totalLength = new DataView(\n                buffer.buffer,\n                buffer.byteOffset,\n                buffer.byteLength,\n              ).getUint32(0, false);\n\n              // If we don't have the full message yet, wait for more chunks.\n              if (buffer.length < totalLength) {\n                break;\n              }\n\n              try {\n                // Decode exactly the sub-slice for this event.\n                const su