{"version":3,"file":"instrumentation.js","sources":["../../../../../src/integrations/tracing/vercelai/instrumentation.ts"],"sourcesContent":["import type { InstrumentationConfig, InstrumentationModuleDefinition } from '@opentelemetry/instrumentation';\nimport { InstrumentationBase, InstrumentationNodeModuleDefinition } from '@opentelemetry/instrumentation';\nimport {\n  _INTERNAL_cleanupToolCallSpanContext,\n  _INTERNAL_getSpanContextForToolCallId,\n  addNonEnumerableProperty,\n  captureException,\n  getActiveSpan,\n  getClient,\n  handleCallbackErrors,\n  SDK_VERSION,\n  withScope,\n} from '@sentry/core';\nimport { INTEGRATION_NAME } from './constants';\nimport type { TelemetrySettings, VercelAiIntegration } from './types';\n\nconst SUPPORTED_VERSIONS = ['>=3.0.0 <7'];\n\n// List of patched methods\n// From: https://sdk.vercel.ai/docs/ai-sdk-core/telemetry#collected-data\nconst INSTRUMENTED_METHODS = [\n  'generateText',\n  'streamText',\n  'generateObject',\n  'streamObject',\n  'embed',\n  'embedMany',\n  'rerank',\n] as const;\n\ninterface MethodFirstArg extends Record<string, unknown> {\n  experimental_telemetry?: TelemetrySettings;\n}\n\ntype MethodArgs = [MethodFirstArg, ...unknown[]];\n\ntype PatchedModuleExports = Record<(typeof INSTRUMENTED_METHODS)[number], (...args: MethodArgs) => unknown> &\n  Record<string, unknown>;\n\ninterface RecordingOptions {\n  recordInputs?: boolean;\n  recordOutputs?: boolean;\n}\n\ninterface ToolError {\n  type: 'tool-error' | 'tool-result' | 'tool-call';\n  toolCallId: string;\n  toolName: string;\n  input?: {\n    [key: string]: unknown;\n  };\n  error: Error;\n  dynamic?: boolean;\n}\n\nfunction isToolError(obj: unknown): obj is ToolError {\n  if (typeof obj !== 'object' || obj === null) {\n    return false;\n  }\n\n  const candidate = obj as Record<string, unknown>;\n  return (\n    'type' in candidate &&\n    'error' in candidate &&\n    'toolName' in candidate &&\n    'toolCallId' in candidate &&\n    candidate.type === 'tool-error' &&\n    candidate.error instanceof Error\n  );\n}\n\n/**\n * Process tool call results: capture tool errors and clean up span context mappings.\n *\n * Error checking runs first (needs span context for linking), then cleanup removes all entries.\n * Tool errors are not rejected in Vercel AI V5 — they appear as metadata in the result content.\n */\nexport function processToolCallResults(result: unknown): void {\n  if (typeof result !== 'object' || result === null || !('content' in result)) {\n    return;\n  }\n\n  const resultObj = result as { content: Array<object> };\n  if (!Array.isArray(resultObj.content)) {\n    return;\n  }\n\n  captureToolErrors(resultObj.content);\n  cleanupToolCallSpanContexts(resultObj.content);\n}\n\nfunction captureToolErrors(content: Array<object>): void {\n  for (const item of content) {\n    if (!isToolError(item)) {\n      continue;\n    }\n\n    // Try to get the span context associated with this tool call ID\n    const spanContext = _INTERNAL_getSpanContextForToolCallId(item.toolCallId);\n\n    if (spanContext) {\n      // We have the span context, so link the error using span and trace IDs\n      withScope(scope => {\n        scope.setContext('trace', {\n          trace_id: spanContext.traceId,\n          span_id: spanContext.spanId,\n        });\n\n        scope.setTag('vercel.ai.tool.name', item.toolName);\n        scope.setTag('vercel.ai.tool.callId', item.toolCallId);\n        scope.setLevel('error');\n\n        captureException(item.error, {\n          mechanism: {\n            type: 'auto.vercelai.otel',\n            handled: false,\n          },\n        });\n      });\n    } else {\n      // Fallback: capture without span linking\n      withScope(scope => {\n        scope.setTag('vercel.ai.tool.name', item.toolName);\n        scope.setTag('vercel.ai.tool.callId', item.toolCallId);\n        scope.setLevel('error');\n\n        captureException(item.error, {\n          mechanism: {\n            type: 'auto.vercelai.otel',\n            handled: false,\n          },\n        });\n      });\n    }\n  }\n}\n\n/**\n * Remove span context entries for all completed tool calls in the content array.\n */\nexport function cleanupToolCallSpanContexts(content: Array<object>): void {\n  for (const item of content) {\n    if (\n      typeof item === 'object' &&\n      item !== null &&\n      'toolCallId' in item &&\n      typeof (item as Record<string, unknown>).toolCallId === 'string'\n    ) {\n      _INTERNAL_cleanupToolCallSpanContext((item as Record<string, unknown>).toolCallId as string);\n    }\n  }\n}\n\n/**\n * Determines whether to record inputs and outputs for Vercel AI telemetry based on the configuration hierarchy.\n *\n * The order of precedence is:\n * 1. The vercel ai integration options\n * 2. The experimental_telemetry options in the vercel ai method calls\n * 3. When telemetry is explicitly enabled (isEnabled: true), default to recording\n * 4. Otherwise, use the dataCollection.genAI settings from client options\n */\nexport function determineRecordingSettings(\n  integrationRecordingOptions: RecordingOptions | undefined,\n  methodTelemetryOptions: RecordingOptions,\n  telemetryExplicitlyEnabled: boolean | undefined,\n  defaultInputsEnabled: boolean,\n  defaultOutputsEnabled: boolean,\n): { recordInputs: boolean; recordOutputs: boolean } {\n  const recordInputs =\n    integrationRecordingOptions?.recordInputs !== undefined\n      ? integrationRecordingOptions.recordInputs\n      : methodTelemetryOptions.recordInputs !== undefined\n        ? methodTelemetryOptions.recordInputs\n        : telemetryExplicitlyEnabled === true\n          ? true // When telemetry is explicitly enabled, default to recording inputs\n          : defaultInputsEnabled;\n\n  const recordOutputs =\n    integrationRecordingOptions?.recordOutputs !== undefined\n      ? integrationRecordingOptions.recordOutputs\n      : methodTelemetryOptions.recordOutputs !== undefined\n        ? methodTelemetryOptions.recordOutputs\n        : telemetryExplicitlyEnabled === true\n          ? true // When telemetry is explicitly enabled, default to recording outputs\n          : defaultOutputsEnabled;\n\n  return { recordInputs, recordOutputs };\n}\n\n/**\n * This detects is added by the Sentry Vercel AI Integration to detect if the integration should\n * be enabled.\n *\n * It also patches the `ai` module to enable Vercel AI telemetry automatically for all methods.\n */\nexport class SentryVercelAiInstrumentation extends InstrumentationBase {\n  private _isPatched = false;\n  private _callbacks: (() => void)[] = [];\n\n  public constructor(config: InstrumentationConfig = {}) {\n    super('@sentry/instrumentation-vercel-ai', SDK_VERSION, config);\n  }\n\n  /**\n   * Initializes the instrumentation by defining the modules to be patched.\n   */\n  public init(): InstrumentationModuleDefinition {\n    const module = new InstrumentationNodeModuleDefinition('ai', SUPPORTED_VERSIONS, this._patch.bind(this));\n    return module;\n  }\n\n  /**\n   * Call the provided callback when the module is patched.\n   * If it has already been patched, the callback will be called immediately.\n   */\n  public callWhenPatched(callback: () => void): void {\n    if (this._isPatched) {\n      callback();\n    } else {\n      this._callbacks.push(callback);\n    }\n  }\n\n  /**\n   * Patches module exports to enable Vercel AI telemetry.\n   */\n  private _patch(moduleExports: PatchedModuleExports): unknown {\n    this._isPatched = true;\n\n    this._callbacks.forEach(callback => callback());\n    this._callbacks = [];\n\n    const generatePatch = <T extends (...args: MethodArgs) => unknown>(originalMethod: T): T => {\n      return new Proxy(originalMethod, {\n        apply: (target, thisArg, args: MethodArgs) => {\n          const existingExperimentalTelemetry = args[0].experimental_telemetry || {};\n          const isEnabled = existingExperimentalTelemetry.isEnabled;\n\n          const client = getClient();\n          const integration = client?.getIntegrationByName<VercelAiIntegration>(INTEGRATION_NAME);\n          const integrationOptions = integration?.options;\n          const genAI = integration ? client?.getDataCollectionOptions().genAI : undefined;\n\n          const { recordInputs, recordOutputs } = determineRecordingSettings(\n            integrationOptions,\n            existingExperimentalTelemetry,\n            isEnabled,\n            Boolean(genAI?.inputs),\n            Boolean(genAI?.outputs),\n          );\n\n          args[0].experimental_telemetry = {\n            ...existingExperimentalTelemetry,\n            isEnabled: isEnabled !== undefined ? isEnabled : true,\n            recordInputs,\n            recordOutputs,\n          };\n\n          return handleCallbackErrors(\n            () => Reflect.apply(target, thisArg, args),\n            error => {\n              // This error bubbles up to unhandledrejection handler (if not handled before),\n              // where we do not know the active span anymore\n              // So to circumvent this, we set the active span on the error object\n              // which is picked up by the unhandledrejection handler\n              if (error && typeof error === 'object') {\n                addNonEnumerableProperty(error, '_sentry_active_span', getActiveSpan());\n              }\n            },\n            () => {},\n            result => {\n              processToolCallResults(result);\n            },\n          );\n        },\n      });\n    };\n\n    // Is this an ESM module?\n    // https://tc39.es/ecma262/#sec-module-namespace-objects\n    if (Object.prototype.toString.call(moduleExports) === '[object Module]') {\n      // In ESM we take the usual route and just replace the exports we want to instrument\n      for (const method of INSTRUMENTED_METHODS) {\n        // Skip methods that don't exist in this 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