Marc Mintel
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{"version":3,"file":"index.js","sources":["../../../../src/tracing/google-genai/index.ts"],"sourcesContent":["import { getClient } from '../../currentScopes';\nimport { captureException } from '../../exports';\nimport { SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '../../semanticAttributes';\nimport { SPAN_STATUS_ERROR } from '../../tracing';\nimport { startSpan, startSpanManual } from '../../tracing/trace';\nimport type { Span, SpanAttributeValue } from '../../types-hoist/span';\nimport { handleCallbackErrors } from '../../utils/handleCallbackErrors';\nimport {\n GEN_AI_INPUT_MESSAGES_ATTRIBUTE,\n GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE,\n GEN_AI_OPERATION_NAME_ATTRIBUTE,\n GEN_AI_REQUEST_AVAILABLE_TOOLS_ATTRIBUTE,\n GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE,\n GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE,\n GEN_AI_REQUEST_MODEL_ATTRIBUTE,\n GEN_AI_REQUEST_PRESENCE_PENALTY_ATTRIBUTE,\n GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE,\n GEN_AI_REQUEST_TOP_K_ATTRIBUTE,\n GEN_AI_REQUEST_TOP_P_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_TEXT_ATTRIBUTE,\n GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n GEN_AI_SYSTEM_ATTRIBUTE,\n GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE,\n GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { truncateGenAiMessages } from '../ai/messageTruncation';\nimport { buildMethodPath, extractSystemInstructions, getFinalOperationName, getSpanOperation } from '../ai/utils';\nimport { CHAT_PATH, CHATS_CREATE_METHOD, GOOGLE_GENAI_SYSTEM_NAME } from './constants';\nimport { instrumentStream } from './streaming';\nimport type {\n Candidate,\n ContentPart,\n GoogleGenAIIstrumentedMethod,\n GoogleGenAIOptions,\n GoogleGenAIResponse,\n} from './types';\nimport type { ContentListUnion, ContentUnion, Message, PartListUnion } from './utils';\nimport { contentUnionToMessages, isStreamingMethod, shouldInstrument } from './utils';\n\n/**\n * Extract model from parameters or chat context object\n * For chat instances, the model is available on the chat object as 'model' (older versions) or 'modelVersion' (newer versions)\n */\nexport function extractModel(params: Record<string, unknown>, context?: unknown): string {\n if ('model' in params && typeof params.model === 'string') {\n return params.model;\n }\n\n // Try to get model from chat context object (chat instance has model property)\n if (context && typeof context === 'object') {\n const contextObj = context as Record<string, unknown>;\n\n // Check for 'model' property (older versions, and streaming)\n if ('model' in contextObj && typeof contextObj.model === 'string') {\n return contextObj.model;\n }\n\n // Check for 'modelVersion' property (newer versions)\n if ('modelVersion' in contextObj && typeof contextObj.modelVersion === 'string') {\n return contextObj.modelVersion;\n }\n }\n\n return 'unknown';\n}\n\n/**\n * Extract generation config parameters\n */\nfunction extractConfigAttributes(config: Record<string, unknown>): Record<string, SpanAttributeValue> {\n const attributes: Record<string, SpanAttributeValue> = {};\n\n if ('temperature' in config && typeof config.temperature === 'number') {\n attributes[GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE] = config.temperature;\n }\n if ('topP' in config && typeof config.topP === 'number') {\n attributes[GEN_AI_REQUEST_TOP_P_ATTRIBUTE] = config.topP;\n }\n if ('topK' in config && typeof config.topK === 'number') {\n attributes[GEN_AI_REQUEST_TOP_K_ATTRIBUTE] = config.topK;\n }\n if ('maxOutputTokens' in config && typeof config.maxOutputTokens === 'number') {\n attributes[GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE] = config.maxOutputTokens;\n }\n if ('frequencyPenalty' in config && typeof config.frequencyPenalty === 'number') {\n attributes[GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE] = config.frequencyPenalty;\n }\n if ('presencePenalty' in config && typeof config.presencePenalty === 'number') {\n attributes[GEN_AI_REQUEST_PRESENCE_PENALTY_ATTRIBUTE] = config.presencePenalty;\n }\n\n return attributes;\n}\n\n/**\n * Extract request attributes from method arguments\n * Builds the base attributes for span creation including system info, model, and config\n */\nfunction extractRequestAttributes(\n methodPath: string,\n params?: Record<string, unknown>,\n context?: unknown,\n): Record<string, SpanAttributeValue> {\n const attributes: Record<string, SpanAttributeValue> = {\n [GEN_AI_SYSTEM_ATTRIBUTE]: GOOGLE_GENAI_SYSTEM_NAME,\n [GEN_AI_OPERATION_NAME_ATTRIBUTE]: getFinalOperationName(methodPath),\n [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',\n };\n\n if (params) {\n attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] = extractModel(params, context);\n\n // Extract generation config parameters\n if ('config' in params && typeof params.config === 'object' && params.config) {\n const config = params.config as Record<string, unknown>;\n Object.assign(attributes, extractConfigAttributes(config));\n\n // Extract available tools from config\n if ('tools' in config && Array.isArray(config.tools)) {\n const functionDeclarations = config.tools.flatMap(\n (tool: { functionDeclarations: unknown[] }) => tool.functionDeclarations,\n );\n attributes[GEN_AI_REQUEST_AVAILABLE_TOOLS_ATTRIBUTE] = JSON.stringify(functionDeclarations);\n }\n }\n } else {\n attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] = extractModel({}, context);\n }\n\n return attributes;\n}\n\n/**\n * Add private request attributes to spans.\n * This is only recorded if recordInputs is true.\n * Handles different parameter formats for different Google GenAI methods.\n */\nfunction addPrivateRequestAttributes(span: Span, params: Record<string, unknown>): void {\n const messages: Message[] = [];\n\n // config.systemInstruction: ContentUnion\n if (\n 'config' in params &&\n params.config &&\n typeof params.config === 'object' &&\n 'systemInstruction' in params.config &&\n params.config.systemInstruction\n ) {\n messages.push(...contentUnionToMessages(params.config.systemInstruction as ContentUnion, 'system'));\n }\n\n // For chats.create: history contains the conversation history\n if ('history' in params) {\n messages.push(...contentUnionToMessages(params.history as PartListUnion, 'user'));\n }\n\n // For models.generateContent: ContentListUnion\n if ('contents' in params) {\n messages.push(...contentUnionToMessages(params.contents as ContentListUnion, 'user'));\n }\n\n // For chat.sendMessage: message can be PartListUnion\n if ('message' in params) {\n messages.push(...contentUnionToMessages(params.message as PartListUnion, 'user'));\n }\n\n if (Array.isArray(messages) && messages.length) {\n const { systemInstructions, filteredMessages } = extractSystemInstructions(messages);\n\n if (systemInstructions) {\n span.setAttribute(GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE, systemInstructions);\n }\n\n const filteredLength = Array.isArray(filteredMessages) ? filteredMessages.length : 0;\n span.setAttributes({\n [GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE]: filteredLength,\n [GEN_AI_INPUT_MESSAGES_ATTRIBUTE]: JSON.stringify(truncateGenAiMessages(filteredMessages as unknown[])),\n });\n }\n}\n\n/**\n * Add response attributes from the Google GenAI response\n * @see https://github.com/googleapis/js-genai/blob/v1.19.0/src/types.ts#L2313\n */\nfunction addResponseAttributes(span: Span, response: GoogleGenAIResponse, recordOutputs?: boolean): void {\n if (!response || typeof response !== 'object') return;\n\n if (response.modelVersion) {\n span.setAttribute(GEN_AI_RESPONSE_MODEL_ATTRIBUTE, response.modelVersion);\n }\n\n // Add usage metadata if present\n if (response.usageMetadata && typeof response.usageMetadata === 'object') {\n const usage = response.usageMetadata;\n if (typeof usage.promptTokenCount === 'number') {\n span.setAttributes({\n [GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE]: usage.promptTokenCount,\n });\n }\n if (typeof usage.candidatesTokenCount === 'number') {\n span.setAttributes({\n [GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE]: usage.candidatesTokenCount,\n });\n }\n if (typeof usage.totalTokenCount === 'number') {\n span.setAttributes({\n [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: usage.totalTokenCount,\n });\n }\n }\n\n // Add response text if recordOutputs is enabled\n if (recordOutputs && Array.isArray(response.candidates) && response.candidates.length > 0) {\n const responseTexts = response.candidates\n .map((candidate: Candidate) => {\n if (candidate.content?.parts && Array.isArray(candidate.content.parts)) {\n return candidate.content.parts\n .map((part: ContentPart) => (typeof part.text === 'string' ? part.text : ''))\n .filter((text: string) => text.length > 0)\n .join('');\n }\n return '';\n })\n .filter((text: string) => text.length > 0);\n\n if (responseTexts.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: responseTexts.join(''),\n });\n }\n }\n\n // Add tool calls if recordOutputs is enabled\n if (recordOutputs && response.functionCalls) {\n const functionCalls = response.functionCalls;\n if (Array.isArray(functionCalls) && functionCalls.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(functionCalls),\n });\n }\n }\n}\n\n/**\n * Instrument any async or synchronous genai method with Sentry spans\n * Handles operations like models.generateContent and chat.sendMessage and chats.create\n * @see https://docs.sentry.io/platforms/javascript/guides/node/tracing/instrumentation/ai-agents-module/#manual-instrumentation\n */\nfunction instrumentMethod<T extends unknown[], R>(\n originalMethod: (...args: T) => R | Promise<R>,\n methodPath: GoogleGenAIIstrumentedMethod,\n context: unknown,\n options: GoogleGenAIOptions,\n): (...args: T) => R | Promise<R> {\n const isSyncCreate = methodPath === CHATS_CREATE_METHOD;\n\n return new Proxy(originalMethod, {\n apply(target, _, args: T): R | Promise<R> {\n const params = args[0] as Record<string, unknown> | undefined;\n const requestAttributes = extractRequestAttributes(methodPath, params, context);\n const model = requestAttributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] ?? 'unknown';\n const operationName = getFinalOperationName(methodPath);\n\n // Check if this is a streaming method\n if (isStreamingMethod(methodPath)) {\n // Use startSpanManual for streaming methods to control span lifecycle\n return startSpanManual(\n {\n name: `${operationName} ${model} stream-response`,\n op: getSpanOperation(methodPath),\n attributes: requestAttributes,\n },\n async (span: Span) => {\n try {\n if (options.recordInputs && params) {\n addPrivateRequestAttributes(span, params);\n }\n const stream = await target.apply(context, args);\n return instrumentStream(stream, span, Boolean(options.recordOutputs)) as R;\n } catch (error) {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: 'internal_error' });\n captureException(error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.google_genai',\n data: { function: methodPath },\n },\n });\n span.end();\n throw error;\n }\n },\n );\n }\n // Single span for both sync and async operations\n return startSpan(\n {\n name: isSyncCreate ? `${operationName} ${model} create` : `${operationName} ${model}`,\n op: getSpanOperation(methodPath),\n attributes: requestAttributes,\n },\n (span: Span) => {\n if (options.recordInputs && params) {\n addPrivateRequestAttributes(span, params);\n }\n\n return handleCallbackErrors(\n () => target.apply(context, args),\n error => {\n captureException(error, {\n mechanism: { handled: false, type: 'auto.ai.google_genai', data: { function: methodPath } },\n });\n },\n () => {},\n result => {\n // Only add response attributes for content-producing methods, not for chats.create\n if (!isSyncCreate) {\n addResponseAttributes(span, result, options.recordOutputs);\n }\n },\n );\n },\n );\n },\n }) as (...args: T) => R | Promise<R>;\n}\n\n/**\n * Create a deep proxy for Google GenAI client instrumentation\n * Recursively instruments methods and handles special cases like chats.create\n */\nfunction createDeepProxy<T extends object>(target: T, currentPath = '', options: GoogleGenAIOptions): T {\n return new Proxy(target, {\n get: (t, prop, receiver) => {\n const value = Reflect.get(t, prop, receiver);\n const methodPath = buildMethodPath(currentPath, String(prop));\n\n if (typeof value === 'function' && shouldInstrument(methodPath)) {\n // Special case: chats.create is synchronous but needs both instrumentation AND result proxying\n if (methodPath === CHATS_CREATE_METHOD) {\n const instrumentedMethod = instrumentMethod(value as (...args: unknown[]) => unknown, methodPath, t, options);\n return function instrumentedAndProxiedCreate(...args: unknown[]): unknown {\n const result = instrumentedMethod(...args);\n // If the result is an object (like a chat instance), proxy it too\n if (result && typeof result === 'object') {\n return createDeepProxy(result, CHAT_PATH, options);\n }\n return result;\n };\n }\n\n return instrumentMethod(value as (...args: unknown[]) => Promise<unknown>, methodPath, t, options);\n }\n\n if (typeof value === 'function') {\n // Bind non-instrumented functions to preserve the original `this` context\n return value.bind(t);\n }\n\n if (value && typeof value === 'object') {\n return createDeepProxy(value, methodPath, options);\n }\n\n return value;\n },\n });\n}\n\n/**\n * Instrument a Google GenAI client with Sentry tracing\n * Can be used across Node.js, Cloudflare Workers, and Vercel Edge\n *\n * @template T - The type of the client that extends client object\n * @param client - The Google GenAI client to instrument\n * @param options - Optional configuration for recording inputs and outputs\n * @returns The instrumented client with the same type as the input\n *\n * @example\n * ```typescript\n * import { GoogleGenAI } from '@google/genai';\n * import { instrumentGoogleGenAIClient } from '@sentry/core';\n *\n * const genAI = new GoogleGenAI({ apiKey: process.env.GOOGLE_GENAI_API_KEY });\n * const instrumentedClient = instrumentGoogleGenAIClient(genAI);\n *\n * // Now both chats.create and sendMessage will be instrumented\n * const chat = instrumentedClient.chats.create({ model: 'gemini-1.5-pro' });\n * const response = await chat.sendMessage({ message: 'Hello' });\n * ```\n */\nexport function instrumentGoogleGenAIClient<T extends object>(client: T, options?: GoogleGenAIOptions): T {\n const sendDefaultPii = Boolean(getClient()?.getOptions().sendDefaultPii);\n\n const _options = {\n recordInputs: sendDefaultPii,\n recordOutputs: sendDefaultPii,\n ...options,\n };\n return createDeepProxy(client, '', 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