@posthog/ai/dist/gemini/index.cjs.map

PostHog Node.js AI integrations

{"version":3,"file":"index.cjs","sources":["../../src/typeGuards.ts","../../src/sanitization.ts","../../src/utils.ts","../../src/gemini/index.ts"],"sourcesContent":["// Type guards for safer type checking\n\nexport const isString = (value: unknown): value is string => {\n  return typeof value === 'string'\n}\n\nexport const isObject = (value: unknown): value is Record<string, unknown> => {\n  return value !== null && typeof value === 'object' && !Array.isArray(value)\n}\n","import { isString, isObject } from './typeGuards'\n\nconst REDACTED_IMAGE_PLACEHOLDER = '[base64 image redacted]'\n\n// ============================================\n// Multimodal Feature Toggle\n// ============================================\n\nconst isMultimodalEnabled = (): boolean => {\n  const val = process.env._INTERNAL_LLMA_MULTIMODAL || ''\n  return val.toLowerCase() === 'true' || val === '1' || val.toLowerCase() === 'yes'\n}\n\n// ============================================\n// Base64 Detection Helpers\n// ============================================\n\nconst isBase64DataUrl = (str: string): boolean => {\n  return /^data:([^;]+);base64,/.test(str)\n}\n\nconst isValidUrl = (str: string): boolean => {\n  try {\n    new URL(str)\n    return true\n  } catch {\n    // Not an absolute URL, check if it's a relative URL or path\n    return str.startsWith('/') || str.startsWith('./') || str.startsWith('../')\n  }\n}\n\nconst isRawBase64 = (str: string): boolean => {\n  // Skip if it's a valid URL or path\n  if (isValidUrl(str)) {\n    return false\n  }\n\n  // Check if it's a valid base64 string\n  // Base64 images are typically at least a few hundred chars, but we'll be conservative\n  return str.length > 20 && /^[A-Za-z0-9+/]+=*$/.test(str)\n}\n\nexport function redactBase64DataUrl(str: string): string\nexport function redactBase64DataUrl(str: unknown): unknown\nexport function redactBase64DataUrl(str: unknown): unknown {\n  if (isMultimodalEnabled()) return str\n  if (!isString(str)) return str\n\n  // Check for data URL format\n  if (isBase64DataUrl(str)) {\n    return REDACTED_IMAGE_PLACEHOLDER\n  }\n\n  // Check for raw base64 (Vercel sends raw base64 for inline images)\n  if (isRawBase64(str)) {\n    return REDACTED_IMAGE_PLACEHOLDER\n  }\n\n  return str\n}\n\n// ============================================\n// Common Message Processing\n// ============================================\n\ntype ContentTransformer = (item: unknown) => unknown\n\nconst processMessages = (messages: unknown, transformContent: ContentTransformer): unknown => {\n  if (!messages) return messages\n\n  const processContent = (content: unknown): unknown => {\n    if (typeof content === 'string') return content\n\n    if (!content) return content\n\n    if (Array.isArray(content)) {\n      return content.map(transformContent)\n    }\n\n    // Handle single object content\n    return transformContent(content)\n  }\n\n  const processMessage = (msg: unknown): unknown => {\n    if (!isObject(msg) || !('content' in msg)) return msg\n    return { ...msg, content: processContent(msg.content) }\n  }\n\n  // Handle both arrays and single messages\n  if (Array.isArray(messages)) {\n    return messages.map(processMessage)\n  }\n\n  return processMessage(messages)\n}\n\n// ============================================\n// Provider-Specific Image Sanitizers\n// ============================================\n\nconst sanitizeOpenAIImage = (item: unknown): unknown => {\n  if (!isObject(item)) return item\n\n  // Handle image_url format\n  if (item.type === 'image_url' && 'image_url' in item && isObject(item.image_url) && 'url' in item.image_url) {\n    return {\n      ...item,\n      image_url: {\n        ...item.image_url,\n        url: redactBase64DataUrl(item.image_url.url),\n      },\n    }\n  }\n\n  // Handle audio format\n  if (item.type === 'audio' && 'data' in item) {\n    if (isMultimodalEnabled()) return item\n    return { ...item, data: REDACTED_IMAGE_PLACEHOLDER }\n  }\n\n  return item\n}\n\nconst sanitizeOpenAIResponseImage = (item: unknown): unknown => {\n  if (!isObject(item)) return item\n\n  // Handle input_image format\n  if (item.type === 'input_image' && 'image_url' in item) {\n    return {\n      ...item,\n      image_url: redactBase64DataUrl(item.image_url),\n    }\n  }\n\n  return item\n}\n\nconst sanitizeAnthropicImage = (item: unknown): unknown => {\n  if (isMultimodalEnabled()) return item\n  if (!isObject(item)) return item\n\n  // Handle Anthropic's image and document formats (same structure, different type field)\n  if (\n    (item.type === 'image' || item.type === 'document') &&\n    'source' in item &&\n    isObject(item.source) &&\n    item.source.type === 'base64' &&\n    'data' in item.source\n  ) {\n    return {\n      ...item,\n      source: {\n        ...item.source,\n        data: REDACTED_IMAGE_PLACEHOLDER,\n      },\n    }\n  }\n\n  return item\n}\n\nconst sanitizeGeminiPart = (part: unknown): unknown => {\n  if (isMultimodalEnabled()) return part\n  if (!isObject(part)) return part\n\n  // Handle Gemini's inline data format (images, audio, PDFs all use inlineData)\n  if ('inlineData' in part && isObject(part.inlineData) && 'data' in part.inlineData) {\n    return {\n      ...part,\n      inlineData: {\n        ...part.inlineData,\n        data: REDACTED_IMAGE_PLACEHOLDER,\n      },\n    }\n  }\n\n  return part\n}\n\nconst processGeminiItem = (item: unknown): unknown => {\n  if (!isObject(item)) return item\n\n  // If it has parts, process them\n  if ('parts' in item && item.parts) {\n    const parts = Array.isArray(item.parts) ? item.parts.map(sanitizeGeminiPart) : sanitizeGeminiPart(item.parts)\n\n    return { ...item, parts }\n  }\n\n  return item\n}\n\nconst sanitizeLangChainImage = (item: unknown): unknown => {\n  if (!isObject(item)) return item\n\n  // OpenAI style\n  if (item.type === 'image_url' && 'image_url' in item && isObject(item.image_url) && 'url' in item.image_url) {\n    return {\n      ...item,\n      image_url: {\n        ...item.image_url,\n        url: redactBase64DataUrl(item.image_url.url),\n      },\n    }\n  }\n\n  // Direct image with data field\n  if (item.type === 'image' && 'data' in item) {\n    return { ...item, data: redactBase64DataUrl(item.data) }\n  }\n\n  // Anthropic style\n  if (item.type === 'image' && 'source' in item && isObject(item.source) && 'data' in item.source) {\n    if (isMultimodalEnabled()) return item\n    return {\n      ...item,\n      source: {\n        ...item.source,\n        data: redactBase64DataUrl(item.source.data),\n      },\n    }\n  }\n\n  // Google style\n  if (item.type === 'media' && 'data' in item) {\n    return { ...item, data: redactBase64DataUrl(item.data) }\n  }\n\n  return item\n}\n\n// Export individual sanitizers for tree-shaking\nexport const sanitizeOpenAI = (data: unknown): unknown => {\n  return processMessages(data, sanitizeOpenAIImage)\n}\n\nexport const sanitizeOpenAIResponse = (data: unknown): unknown => {\n  return processMessages(data, sanitizeOpenAIResponseImage)\n}\n\nexport const sanitizeAnthropic = (data: unknown): unknown => {\n  return processMessages(data, sanitizeAnthropicImage)\n}\n\nexport const sanitizeGemini = (data: unknown): unknown => {\n  // Gemini has a different structure with 'parts' directly on items instead of 'content'\n  // So we need custom processing instead of using processMessages\n  if (!data) return data\n\n  if (Array.isArray(data)) {\n    return data.map(processGeminiItem)\n  }\n\n  return processGeminiItem(data)\n}\n\nexport const sanitizeLangChain = (data: unknown): unknown => {\n  return processMessages(data, sanitizeLangChainImage)\n}\n","import { EventMessage, PostHog } from 'posthog-node'\nimport OpenAIOrignal from 'openai'\nimport AnthropicOriginal from '@anthropic-ai/sdk'\nimport type { ChatCompletionTool } from 'openai/resources/chat/completions'\nimport type { ResponseCreateParamsWithTools } from 'openai/lib/ResponsesParser'\nimport type { Tool as GeminiTool } from '@google/genai'\nimport type { FormattedMessage, FormattedContent, TokenUsage } from './types'\nimport { version } from '../package.json'\nimport { v4 as uuidv4 } from 'uuid'\nimport { isString } from './typeGuards'\nimport { uuidv7, ErrorTracking as CoreErrorTracking } from '@posthog/core'\nimport { redactBase64DataUrl } from './sanitization'\n\ntype ChatCompletionCreateParamsBase = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParams\ntype MessageCreateParams = AnthropicOriginal.Messages.MessageCreateParams\ntype ResponseCreateParams = OpenAIOrignal.Responses.ResponseCreateParams\ntype EmbeddingCreateParams = OpenAIOrignal.EmbeddingCreateParams\ntype TranscriptionCreateParams = OpenAIOrignal.Audio.Transcriptions.TranscriptionCreateParams\ntype AnthropicTool = AnthropicOriginal.Tool\n\n// limit large outputs by truncating to 200kb (approx 200k bytes)\nexport const MAX_OUTPUT_SIZE = 200000\nconst STRING_FORMAT = 'utf8'\n\n/**\n * Safely converts content to a string, preserving structure for objects/arrays.\n * - If content is already a string, returns it as-is\n * - If content is an object or array, stringifies it with JSON.stringify to preserve structure\n * - Otherwise, converts to string with String()\n *\n * This prevents the \"[object Object]\" bug when objects are naively converted to strings.\n *\n * @param content - The content to convert to a string\n * @returns A string representation that preserves structure for complex types\n */\nexport function toContentString(content: unknown): string {\n  if (typeof content === 'string') {\n    return content\n  }\n  if (content !== undefined && content !== null && typeof content === 'object') {\n    try {\n      return JSON.stringify(content)\n    } catch {\n      // Fallback for circular refs, BigInt, or objects with throwing toJSON\n      return String(content)\n    }\n  }\n  return String(content)\n}\n\nexport interface MonitoringEventPropertiesWithDefaults {\n  distinctId?: string\n  traceId: string\n  properties?: Record<string, any>\n  privacyMode: boolean\n  groups?: Record<string, any>\n  modelOverride?: string\n  providerOverride?: string\n  costOverride?: CostOverride\n  captureImmediate?: boolean\n}\n\nexport type MonitoringEventProperties = Partial<MonitoringEventPropertiesWithDefaults>\n\nexport type MonitoringParams = {\n  [K in keyof MonitoringEventProperties as `posthog${Capitalize<string & K>}`]: MonitoringEventProperties[K]\n}\n\nexport interface CostOverride {\n  inputCost: number\n  outputCost: number\n}\n\nexport const getModelParams = (\n  params:\n    | ((\n        | ChatCompletionCreateParamsBase\n        | MessageCreateParams\n        | ResponseCreateParams\n        | ResponseCreateParamsWithTools\n        | EmbeddingCreateParams\n        | TranscriptionCreateParams\n      ) &\n        MonitoringParams)\n    | null\n): Record<string, any> => {\n  if (!params) {\n    return {}\n  }\n  const modelParams: Record<string, any> = {}\n  const paramKeys = [\n    'temperature',\n    'max_tokens',\n    'max_completion_tokens',\n    'top_p',\n    'frequency_penalty',\n    'presence_penalty',\n    'n',\n    'stop',\n    'stream',\n    'streaming',\n    'language',\n    'response_format',\n    'timestamp_granularities',\n  ] as const\n\n  for (const key of paramKeys) {\n    if (key in params && (params as any)[key] !== undefined) {\n      modelParams[key] = (params as any)[key]\n    }\n  }\n  return modelParams\n}\n\n/**\n * Helper to format responses (non-streaming) for consumption\n */\nexport const formatResponse = (response: any, provider: string): FormattedMessage[] => {\n  if (!response) {\n    return []\n  }\n  if (provider === 'anthropic') {\n    return formatResponseAnthropic(response)\n  } else if (provider === 'openai') {\n    return formatResponseOpenAI(response)\n  } else if (provider === 'gemini') {\n    return formatResponseGemini(response)\n  }\n  return []\n}\n\nexport const formatResponseAnthropic = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n  const content: FormattedContent = []\n\n  for (const choice of response.content ?? []) {\n    if (choice?.type === 'text' && choice?.text) {\n      content.push({ type: 'text', text: choice.text })\n    } else if (choice?.type === 'tool_use' && choice?.name && choice?.id) {\n      content.push({\n        type: 'function',\n        id: choice.id,\n        function: {\n          name: choice.name,\n          arguments: choice.input || {},\n        },\n      })\n    }\n  }\n\n  if (content.length > 0) {\n    output.push({\n      role: 'assistant',\n      content,\n    })\n  }\n\n  return output\n}\n\nexport const formatResponseOpenAI = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n\n  if (response.choices) {\n    for (const choice of response.choices) {\n      const content: FormattedContent = []\n      let role = 'assistant'\n\n      if (choice.message) {\n        if (choice.message.role) {\n          role = choice.message.role\n        }\n\n        if (choice.message.content) {\n          content.push({ type: 'text', text: choice.message.content })\n        }\n\n        if (choice.message.tool_calls) {\n          for (const toolCall of choice.message.tool_calls) {\n            content.push({\n              type: 'function',\n              id: toolCall.id,\n              function: {\n                name: toolCall.function.name,\n                arguments: toolCall.function.arguments,\n              },\n            })\n          }\n        }\n\n        // Handle audio output (gpt-4o-audio-preview)\n        if (choice.message.audio) {\n          content.push({\n            type: 'audio',\n            ...choice.message.audio,\n          })\n        }\n      }\n\n      if (content.length > 0) {\n        output.push({\n          role,\n          content,\n        })\n      }\n    }\n  }\n\n  // Handle Responses API format\n  if (response.output) {\n    const content: FormattedContent = []\n    let role = 'assistant'\n\n    for (const item of response.output) {\n      if (item.type === 'message') {\n        role = item.role\n\n        if (item.content && Array.isArray(item.content)) {\n          for (const contentItem of item.content) {\n            if (contentItem.type === 'output_text' && contentItem.text) {\n              content.push({ type: 'text', text: contentItem.text })\n            } else if (contentItem.text) {\n              content.push({ type: 'text', text: contentItem.text })\n            } else if (contentItem.type === 'input_image' && contentItem.image_url) {\n              content.push({\n                type: 'image',\n                image: contentItem.image_url,\n              })\n            }\n          }\n        } else if (item.content) {\n          content.push({ type: 'text', text: String(item.content) })\n        }\n      } else if (item.type === 'function_call') {\n        content.push({\n          type: 'function',\n          id: item.call_id || item.id || '',\n          function: {\n            name: item.name,\n            arguments: item.arguments || {},\n          },\n        })\n      }\n    }\n\n    if (content.length > 0) {\n      output.push({\n        role,\n        content,\n      })\n    }\n  }\n\n  return output\n}\n\nexport const formatResponseGemini = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n\n  if (response.candidates && Array.isArray(response.candidates)) {\n    for (const candidate of response.candidates) {\n      if (candidate.content && candidate.content.parts) {\n        const content: FormattedContent = []\n\n        for (const part of candidate.content.parts) {\n          if (part.text) {\n            content.push({ type: 'text', text: part.text })\n          } else if (part.functionCall) {\n            content.push({\n              type: 'function',\n              function: {\n                name: part.functionCall.name,\n                arguments: part.functionCall.args,\n              },\n            })\n          } else if (part.inlineData) {\n            // Handle audio/media inline data\n            const mimeType = part.inlineData.mimeType || 'audio/pcm'\n            let data = part.inlineData.data\n\n            // Handle binary data (Uint8Array/Buffer -> base64)\n            if (data instanceof Uint8Array) {\n              if (typeof Buffer !== 'undefined') {\n                data = Buffer.from(data).toString('base64')\n              } else {\n                let binary = ''\n                for (let i = 0; i < data.length; i++) {\n                  binary += String.fromCharCode(data[i])\n                }\n                data = btoa(binary)\n              }\n            }\n\n            // Sanitize base64 data for images and other large inline data\n            data = redactBase64DataUrl(data)\n\n            content.push({\n              type: 'audio',\n              mime_type: mimeType,\n              data: data,\n            })\n          }\n        }\n\n        if (content.length > 0) {\n          output.push({\n            role: 'assistant',\n            content,\n          })\n        }\n      } else if (candidate.text) {\n        output.push({\n          role: 'assistant',\n          content: [{ type: 'text', text: candidate.text }],\n        })\n      }\n    }\n  } else if (response.text) {\n    output.push({\n      role: 'assistant',\n      content: [{ type: 'text', text: response.text }],\n    })\n  }\n\n  return output\n}\n\nexport const mergeSystemPrompt = (params: MessageCreateParams & MonitoringParams, provider: string): any => {\n  if (provider == 'anthropic') {\n    const messages = params.messages || []\n    if (!(params as any).system) {\n      return messages\n    }\n    const systemMessage = (params as any).system\n    return [{ role: 'system', content: systemMessage }, ...messages]\n  }\n  return params.messages\n}\n\nexport const withPrivacyMode = (client: PostHog, privacyMode: boolean, input: any): any => {\n  return (client as any).privacy_mode || privacyMode ? null : input\n}\n\nfunction toSafeString(input: unknown): string {\n  if (input === undefined || input === null) {\n    return ''\n  }\n  if (typeof input === 'string') {\n    return input\n  }\n  try {\n    return JSON.stringify(input)\n  } catch {\n    console.warn('Failed to stringify input', input)\n    return ''\n  }\n}\n\nexport const truncate = (input: unknown): string => {\n  const str = toSafeString(input)\n  if (str === '') {\n    return ''\n  }\n\n  // Check if we need to truncate and ensure STRING_FORMAT is respected\n  const encoder = new TextEncoder()\n  const buffer = encoder.encode(str)\n  if (buffer.length <= MAX_OUTPUT_SIZE) {\n    // Ensure STRING_FORMAT is respected\n    return new TextDecoder(STRING_FORMAT).decode(buffer)\n  }\n\n  // Truncate the buffer and ensure a valid string is returned\n  const truncatedBuffer = buffer.slice(0, MAX_OUTPUT_SIZE)\n  // fatal: false means we get U+FFFD at the end if truncation broke the encoding\n  const decoder = new TextDecoder(STRING_FORMAT, { fatal: false })\n  let truncatedStr = decoder.decode(truncatedBuffer)\n  if (truncatedStr.endsWith('\\uFFFD')) {\n    truncatedStr = truncatedStr.slice(0, -1)\n  }\n  return `${truncatedStr}... [truncated]`\n}\n\n/**\n * Calculate web search count from raw API response.\n *\n * Uses a two-tier detection strategy:\n * Priority 1 (Exact Count): Count actual web search calls when available\n * Priority 2 (Binary Detection): Return 1 if web search indicators are present, 0 otherwise\n *\n * @param result - Raw API response from any provider (OpenAI, Perplexity, OpenRouter, Gemini, etc.)\n * @returns Number of web searches performed (exact count or binary 1/0)\n */\nexport function calculateWebSearchCount(result: unknown): number {\n  if (!result || typeof result !== 'object') {\n    return 0\n  }\n\n  // Priority 1: Exact Count\n  // Check for OpenAI Responses API web_search_call items\n  if ('output' in result && Array.isArray(result.output)) {\n    let count = 0\n\n    for (const item of result.output) {\n      if (typeof item === 'object' && item !== null && 'type' in item && item.type === 'web_search_call') {\n        count++\n      }\n    }\n\n    if (count > 0) {\n      return count\n    }\n  }\n\n  // Priority 2: Binary Detection (1 or 0)\n\n  // Check for citations at root level (Perplexity)\n  if ('citations' in result && Array.isArray(result.citations) && result.citations.length > 0) {\n    return 1\n  }\n\n  // Check for search_results at root level (Perplexity via OpenRouter)\n  if ('search_results' in result && Array.isArray(result.search_results) && result.search_results.length > 0) {\n    return 1\n  }\n\n  // Check for usage.search_context_size (Perplexity via OpenRouter)\n  if ('usage' in result && typeof result.usage === 'object' && result.usage !== null) {\n    if ('search_context_size' in result.usage && result.usage.search_context_size) {\n      return 1\n    }\n  }\n\n  // Check for annotations with url_citation in choices[].message or choices[].delta (OpenAI/Perplexity)\n  if ('choices' in result && Array.isArray(result.choices)) {\n    for (const choice of result.choices) {\n      if (typeof choice === 'object' && choice !== null) {\n        // Check both message (non-streaming) and delta (streaming) for annotations\n        const content = ('message' in choice ? choice.message : null) || ('delta' in choice ? choice.delta : null)\n\n        if (typeof content === 'object' && content !== null && 'annotations' in content) {\n          const annotations = content.annotations\n\n          if (Array.isArray(annotations)) {\n            const hasUrlCitation = annotations.some((ann: unknown) => {\n              return typeof ann === 'object' && ann !== null && 'type' in ann && ann.type === 'url_citation'\n            })\n\n            if (hasUrlCitation) {\n              return 1\n            }\n          }\n        }\n      }\n    }\n  }\n\n  // Check for annotations in output[].content[] (OpenAI Responses API)\n  if ('output' in result && Array.isArray(result.output)) {\n    for (const item of result.output) {\n      if (typeof item === 'object' && item !== null && 'content' in item) {\n        const content = item.content\n\n        if (Array.isArray(content)) {\n          for (const contentItem of content) {\n            if (typeof contentItem === 'object' && contentItem !== null && 'annotations' in contentItem) {\n              const annotations = contentItem.annotations\n\n              if (Array.isArray(annotations)) {\n                const hasUrlCitation = annotations.some((ann: unknown) => {\n                  return typeof ann === 'object' && ann !== null && 'type' in ann && ann.type === 'url_citation'\n                })\n\n                if (hasUrlCitation) {\n                  return 1\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n\n  // Check for grounding_metadata (Gemini)\n  if ('candidates' in result && Array.isArray(result.candidates)) {\n    for (const candidate of result.candidates) {\n      if (\n        typeof candidate === 'object' &&\n        candidate !== null &&\n        'grounding_metadata' in candidate &&\n        candidate.grounding_metadata\n      ) {\n        return 1\n      }\n    }\n  }\n\n  return 0\n}\n\n/**\n * Extract available tool calls from the request parameters.\n * These are the tools provided to the LLM, not the tool calls in the response.\n */\nexport const extractAvailableToolCalls = (\n  provider: string,\n  params: any\n): ChatCompletionTool[] | AnthropicTool[] | GeminiTool[] | null => {\n  if (provider === 'anthropic') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  } else if (provider === 'gemini') {\n    if (params.config && params.config.tools) {\n      return params.config.tools\n    }\n\n    return null\n  } else if (provider === 'openai') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  } else if (provider === 'vercel') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  }\n\n  return null\n}\n\nexport enum AIEvent {\n  Generation = '$ai_generation',\n  Embedding = '$ai_embedding',\n}\n\nexport type SendEventToPosthogParams = {\n  client: PostHog\n  eventType?: AIEvent\n  distinctId?: string\n  traceId: string\n  model?: string\n  provider: string\n  input: any\n  output: any\n  latency: number\n  timeToFirstToken?: number\n  baseURL: string\n  httpStatus: number\n  usage?: TokenUsage\n  params: (\n    | ChatCompletionCreateParamsBase\n    | MessageCreateParams\n    | ResponseCreateParams\n    | ResponseCreateParamsWithTools\n    | EmbeddingCreateParams\n    | TranscriptionCreateParams\n  ) &\n    MonitoringParams\n  error?: unknown\n  exceptionId?: string\n  tools?: ChatCompletionTool[] | AnthropicTool[] | GeminiTool[] | null\n  captureImmediate?: boolean\n}\n\nfunction sanitizeValues(obj: any): any {\n  if (obj === undefined || obj === null) {\n    return obj\n  }\n  const jsonSafe = JSON.parse(JSON.stringify(obj))\n  if (typeof jsonSafe === 'string') {\n    // Sanitize lone surrogates by round-tripping through UTF-8\n    return new TextDecoder().decode(new TextEncoder().encode(jsonSafe))\n  } else if (Array.isArray(jsonSafe)) {\n    return jsonSafe.map(sanitizeValues)\n  } else if (jsonSafe && typeof jsonSafe === 'object') {\n    return Object.fromEntries(Object.entries(jsonSafe).map(([k, v]) => [k, sanitizeValues(v)]))\n  }\n  return jsonSafe\n}\n\nconst POSTHOG_PARAMS_MAP: Record<keyof MonitoringParams, string> = {\n  posthogDistinctId: 'distinctId',\n  posthogTraceId: 'traceId',\n  posthogProperties: 'properties',\n  posthogPrivacyMode: 'privacyMode',\n  posthogGroups: 'groups',\n  posthogModelOverride: 'modelOverride',\n  posthogProviderOverride: 'providerOverride',\n  posthogCostOverride: 'costOverride',\n  posthogCaptureImmediate: 'captureImmediate',\n}\n\nexport function extractPosthogParams<T>(body: T & MonitoringParams): {\n  providerParams: T\n  posthogParams: MonitoringEventPropertiesWithDefaults\n} {\n  const providerParams: Record<string, unknown> = {}\n  const posthogParams: Record<string, unknown> = {}\n\n  for (const [key, value] of Object.entries(body)) {\n    if (POSTHOG_PARAMS_MAP[key as keyof MonitoringParams]) {\n      posthogParams[POSTHOG_PARAMS_MAP[key as keyof MonitoringParams]] = value\n    } else if (key.startsWith('posthog')) {\n      console.warn(`Unknown Posthog parameter ${key}`)\n    } else {\n      providerParams[key] = value\n    }\n  }\n\n  return {\n    providerParams: providerParams as T,\n    posthogParams: addDefaults(posthogParams),\n  }\n}\n\nfunction addDefaults(params: MonitoringEventProperties): MonitoringEventPropertiesWithDefaults {\n  return {\n    ...params,\n    privacyMode: params.privacyMode ?? false,\n    traceId: params.traceId ?? uuidv4(),\n  }\n}\n\nexport const sendEventWithErrorToPosthog = async ({\n  client,\n  traceId,\n  error,\n  ...args\n}: Omit<SendEventToPosthogParams, 'error' | 'httpStatus'> &\n  Required<Pick<SendEventToPosthogParams, 'error'>>): Promise<unknown> => {\n  const httpStatus =\n    error && typeof error === 'object' && 'status' in error ? ((error as { status?: number }).status ?? 500) : 500\n\n  const properties = { client, traceId, httpStatus, error: JSON.stringify(error), ...args }\n  const enrichedError = error as CoreErrorTracking.PreviouslyCapturedError\n\n  if (client.options?.enableExceptionAutocapture) {\n    // assign a uuid that can be used to link the trace and exception events\n    const exceptionId = uuidv7()\n    client.captureException(error, undefined, { $ai_trace_id: traceId }, exceptionId)\n    enrichedError.__posthog_previously_captured_error = true\n    properties.exceptionId = exceptionId\n  }\n\n  await sendEventToPosthog(properties)\n\n  return enrichedError\n}\n\nexport const sendEventToPosthog = async ({\n  client,\n  eventType = AIEvent.Generation,\n  distinctId,\n  traceId,\n  model,\n  provider,\n  input,\n  output,\n  latency,\n  timeToFirstToken,\n  baseURL,\n  params,\n  httpStatus = 200,\n  usage = {},\n  error,\n  exceptionId,\n  tools,\n  captureImmediate = false,\n}: SendEventToPosthogParams): Promise<void> => {\n  if (!client.capture) {\n    return Promise.resolve()\n  }\n  // sanitize input and output for UTF-8 validity\n  const safeInput = sanitizeValues(input)\n  const safeOutput = sanitizeValues(output)\n  const safeError = sanitizeValues(error)\n\n  let errorData = {}\n  if (error) {\n    errorData = {\n      $ai_is_error: true,\n      $ai_error: safeError,\n      $exception_event_id: exceptionId,\n    }\n  }\n  let costOverrideData = {}\n  if (params.posthogCostOverride) {\n    const inputCostUSD = (params.posthogCostOverride.inputCost ?? 0) * (usage.inputTokens ?? 0)\n    const outputCostUSD = (params.posthogCostOverride.outputCost ?? 0) * (usage.outputTokens ?? 0)\n    costOverrideData = {\n      $ai_input_cost_usd: inputCostUSD,\n      $ai_output_cost_usd: outputCostUSD,\n      $ai_total_cost_usd: inputCostUSD + outputCostUSD,\n    }\n  }\n\n  const additionalTokenValues = {\n    ...(usage.reasoningTokens ? { $ai_reasoning_tokens: usage.reasoningTokens } : {}),\n    ...(usage.cacheReadInputTokens ? { $ai_cache_read_input_tokens: usage.cacheReadInputTokens } : {}),\n    ...(usage.cacheCreationInputTokens ? { $ai_cache_creation_input_tokens: usage.cacheCreationInputTokens } : {}),\n    ...(usage.webSearchCount ? { $ai_web_search_count: usage.webSearchCount } : {}),\n    ...(usage.rawUsage ? { $ai_usage: usage.rawUsage } : {}),\n  }\n\n  const properties = {\n    $ai_lib: 'posthog-ai',\n    $ai_lib_version: version,\n    $ai_provider: params.posthogProviderOverride ?? provider,\n    $ai_model: params.posthogModelOverride ?? model,\n    $ai_model_parameters: getModelParams(params),\n    $ai_input: withPrivacyMode(client, params.posthogPrivacyMode ?? false, safeInput),\n    $ai_output_choices: withPrivacyMode(client, params.posthogPrivacyMode ?? false, safeOutput),\n    $ai_http_status: httpStatus,\n    $ai_input_tokens: usage.inputTokens ?? 0,\n    ...(usage.outputTokens !== undefined ? { $ai_output_tokens: usage.outputTokens } : {}),\n    ...additionalTokenValues,\n    $ai_latency: latency,\n    ...(timeToFirstToken !== undefined ? { $ai_time_to_first_token: timeToFirstToken } : {}),\n    $ai_trace_id: traceId,\n    $ai_base_url: baseURL,\n    ...params.posthogProperties,\n    ...(distinctId ? {} : { $process_person_profile: false }),\n    ...(tools ? { $ai_tools: tools } : {}),\n    ...errorData,\n    ...costOverrideData,\n  }\n\n  const event: EventMessage = {\n    distinctId: distinctId ?? traceId,\n    event: eventType,\n    properties,\n    groups: params.posthogGroups,\n  }\n\n  if (captureImmediate) {\n    // await capture promise to send single event in serverless environments\n    await client.captureImmediate(event)\n  } else {\n    client.capture(event)\n  }\n\n  return Promise.resolve()\n}\n\nexport function formatOpenAIResponsesInput(input: unknown, instructions?: string | null): FormattedMessage[] {\n  const messages: FormattedMessage[] = []\n\n  if (instructions) {\n    messages.push({\n      role: 'system',\n      content: instructions,\n    })\n  }\n\n  if (Array.isArray(input)) {\n    for (const item of input) {\n      if (typeof item === 'string') {\n        messages.push({ role: 'user', content: item })\n      } else if (item && typeof item === 'object') {\n        const obj = item as Record<string, unknown>\n        const role = isString(obj.role) ? obj.role : 'user'\n\n        // Handle content properly - preserve structure for objects/arrays\n        const content = obj.content ?? obj.text ?? item\n        messages.push({ role, content: toContentString(content) })\n      } else {\n        messages.push({ role: 'user', content: toContentString(item) })\n      }\n    }\n  } else if (typeof input === 'string') {\n    messages.push({ role: 'user', content: input })\n  } else if (input) {\n    messages.push({ role: 'user', content: toContentString(input) })\n  }\n\n  return messages\n}\n","import {\n  GoogleGenAI,\n  GenerateContentResponse as GeminiResponse,\n  GenerateContentParameters,\n  Part,\n  GenerateContentResponseUsageMetadata,\n} from '@google/genai'\nimport type { GoogleGenAIOptions } from '@google/genai'\nimport { PostHog } from 'posthog-node'\nimport {\n  MonitoringParams,\n  sendEventToPosthog,\n  extractAvailableToolCalls,\n  formatResponseGemini,\n  extractPosthogParams,\n  toContentString,\n  sendEventWithErrorToPosthog,\n} from '../utils'\nimport { sanitizeGemini } from '../sanitization'\nimport type { TokenUsage, FormattedContent, FormattedContentItem, FormattedMessage } from '../types'\nimport { isString } from '../typeGuards'\n\ninterface MonitoringGeminiConfig extends GoogleGenAIOptions {\n  posthog: PostHog\n}\n\nexport class PostHogGoogleGenAI {\n  private readonly phClient: PostHog\n  private readonly client: GoogleGenAI\n  public models: WrappedModels\n\n  constructor(config: MonitoringGeminiConfig) {\n    const { posthog, ...geminiConfig } = config\n    this.phClient = posthog\n    this.client = new GoogleGenAI(geminiConfig)\n    this.models = new WrappedModels(this.client, this.phClient)\n  }\n}\n\nexport class WrappedModels {\n  private readonly phClient: PostHog\n  private readonly client: GoogleGenAI\n\n  constructor(client: GoogleGenAI, phClient: PostHog) {\n    this.client = client\n    this.phClient = phClient\n  }\n\n  public async generateContent(params: GenerateContentParameters & MonitoringParams): Promise<GeminiResponse> {\n    const { providerParams: geminiParams, posthogParams } = extractPosthogParams(params)\n    const startTime = Date.now()\n\n    try {\n      const response = await this.client.models.generateContent(geminiParams as GenerateContentParameters)\n      const latency = (Date.now() - startTime) / 1000\n\n      const availableTools = extractAvailableToolCalls('gemini', geminiParams)\n\n      const metadata = response.usageMetadata\n      await sendEventToPosthog({\n        client: this.phClient,\n        ...posthogParams,\n        model: geminiParams.model,\n        provider: 'gemini',\n        input: this.formatInputForPostHog(geminiParams),\n        output: formatResponseGemini(response),\n        latency,\n        baseURL: 'https://generativelanguage.googleapis.com',\n        params: params as GenerateContentParameters & MonitoringParams,\n        httpStatus: 200,\n        usage: {\n          inputTokens: metadata?.promptTokenCount ?? 0,\n          outputTokens: metadata?.candidatesTokenCount ?? 0,\n          reasoningTokens:\n            (metadata as GenerateContentResponseUsageMetadata & { thoughtsTokenCount?: number })?.thoughtsTokenCount ??\n            0,\n          cacheReadInputTokens: metadata?.cachedContentTokenCount ?? 0,\n          webSearchCount: calculateGoogleWebSearchCount(response),\n          rawUsage: metadata,\n        },\n        tools: availableTools,\n      })\n\n      return response\n    } catch (error: unknown) {\n      const latency = (Date.now() - startTime) / 1000\n      const enrichedError = await sendEventWithErrorToPosthog({\n        client: this.phClient,\n        ...posthogParams,\n        model: geminiParams.model,\n        provider: 'gemini',\n        input: this.formatInputForPostHog(geminiParams),\n        output: [],\n        latency,\n        baseURL: 'https://generativelanguage.googleapis.com',\n        params: params as GenerateContentParameters & MonitoringParams,\n        usage: {\n          inputTokens: 0,\n          outputTokens: 0,\n        },\n        error: error,\n      })\n      throw enrichedError\n    }\n  }\n\n  public async *generateContentStream(\n    params: GenerateContentParameters & MonitoringParams\n  ): AsyncGenerator<GeminiResponse, void, unknown> {\n    const { providerParams: geminiParams, posthogParams } = extractPosthogParams(params)\n    const startTime = Date.now()\n    const accumulatedContent: FormattedContent = []\n    let firstTokenTime: number | undefined\n    let usage: TokenUsage = {\n      inputTokens: 0,\n      outputTokens: 0,\n      webSearchCount: 0,\n      rawUsage: undefined,\n    }\n\n    try {\n      const stream = await this.client.models.generateContentStream(geminiParams as GenerateContentParameters)\n\n      for await (const chunk of stream) {\n        // Track first token time when we get text content\n        if (firstTokenTime === undefined && chunk.text) {\n          firstTokenTime = Date.now()\n        }\n        const chunkWebSearchCount = calculateGoogleWebSearchCount(chunk)\n        if (chunkWebSearchCount > 0 && chunkWebSearchCount > (usage.webSearchCount ?? 0)) {\n          usage.webSearchCount = chunkWebSearchCount\n        }\n\n        // Handle text content\n        if (chunk.text) {\n          // Find if we already have a text item to append to\n          let lastTextItem: FormattedContentItem | undefined\n          for (let i = accumulatedContent.length - 1; i >= 0; i--) {\n            if (accumulatedContent[i].type === 'text') {\n              lastTextItem = accumulatedContent[i]\n              break\n            }\n          }\n\n          if (lastTextItem && lastTextItem.type === 'text') {\n            lastTextItem.text += chunk.text\n          } else {\n            accumulatedContent.push({ type: 'text', text: chunk.text })\n          }\n        }\n\n        // Handle function calls from candidates\n        if (chunk.candidates && Array.isArray(chunk.candidates)) {\n          for (const candidate of chunk.candidates) {\n            if (candidate.content && candidate.content.parts) {\n              for (const part of candidate.content.parts) {\n                // Type-safe check for functionCall\n                if ('functionCall' in part) {\n                  if (firstTokenTime === undefined) {\n                    firstTokenTime = Date.now()\n                  }\n                  const funcCall = (part as Part & { functionCall?: { name?: string; args?: unknown } }).functionCall\n                  if (funcCall?.name) {\n                    accumulatedContent.push({\n                      type: 'function',\n                      function: {\n                        name: funcCall.name,\n                        arguments: funcCall.args || {},\n                      },\n                    })\n                  }\n                }\n              }\n            }\n          }\n        }\n\n        // Update usage metadata - handle both old and new field names\n        if (chunk.usageMetadata) {\n          const metadata = chunk.usageMetadata as GenerateContentResponseUsageMetadata\n          usage = {\n            inputTokens: metadata.promptTokenCount ?? 0,\n            outputTokens: metadata.candidatesTokenCount ?? 0,\n            reasoningTokens:\n              (metadata as GenerateContentResponseUsageMetadata & { thoughtsTokenCount?: number }).thoughtsTokenCount ??\n              0,\n            cacheReadInputTokens: metadata.cachedContentTokenCount ?? 0,\n            webSearchCount: usage.webSearchCount,\n            rawUsage: metadata,\n          }\n        }\n        yield chunk\n      }\n\n      const latency = (Date.now() - startTime) / 1000\n      const timeToFirstToken = firstTokenTime !== undefined ? (firstTokenTime - startTime) / 1000 : undefined\n\n      const availableTools = extractAvailableToolCalls('gemini', geminiParams)\n\n      // Format output similar to formatResponseGemini\n      const output = accumulatedContent.length > 0 ? [{ role: 'assistant', content: accumulatedContent }] : []\n\n      await sendEventToPosthog({\n        client: this.phClient,\n        ...posthogParams,\n        model: geminiParams.model,\n        provider: 'gemini',\n        input: this.formatInputForPostHog(geminiParams),\n        output,\n        latency,\n        timeToFirstToken,\n        baseURL: 'https://generativelanguage.googleapis.com',\n        params: params as GenerateContentParameters & MonitoringParams,\n        httpStatus: 200,\n        usage: {\n          ...usage,\n          webSearchCount: usage.webSearchCount,\n          rawUsage: usage.rawUsage,\n        },\n        tools: availableTools,\n      })\n    } catch (error: unknown) {\n      const latency = (Date.now() - startTime) / 1000\n      const enrichedError = await sendEventWithErrorToPosthog({\n        client: this.phClient,\n        ...posthogParams,\n        model: geminiParams.model,\n        provider: 'gemini',\n        input: this.formatInputForPostHog(geminiParams),\n        output: [],\n        latency,\n        baseURL: 'https://generativelanguage.googleapis.com',\n        params: params as GenerateContentParameters & MonitoringParams,\n        usage: {\n          inputTokens: 0,\n          outputTokens: 0,\n        },\n        error: error,\n      })\n      throw enrichedError\n    }\n  }\n\n  private formatPartsAsContentBlocks(parts: unknown[]): FormattedContent {\n    const blocks: FormattedContent = []\n\n    for (const part of parts) {\n      // Handle dict/object with text field\n      if (part && typeof part === 'object' && 'text' in part && part.text) {\n        blocks.push({ type: 'text', text: String(part.text) })\n      }\n      // Handle string parts\n      else if (typeof part === 'string') {\n        blocks.push({ type: 'text', text: part })\n      }\n      // Handle inlineData (images, audio, PDFs)\n      else if (part && typeof part === 'object' && 'inlineData' in part) {\n        const inlineData = (part as any).inlineData\n        const mimeType = inlineData.mimeType || inlineData.mime_type || ''\n        const contentType = mimeType.startsWith('image/') ? 'image' : 'document'\n\n        blocks.push({\n          type: contentType,\n          inline_data: {\n            data: inlineData.data,\n            mime_type: mimeType,\n          },\n        } as FormattedContentItem)\n      }\n    }\n\n    return blocks\n  }\n\n  private formatInput(contents: unknown): FormattedMessage[] {\n    if (typeof contents === 'string') {\n      return [{ role: 'user', content: contents }]\n    }\n\n    if (Array.isArray(contents)) {\n      return contents.map((item) => {\n        if (typeof item === 'string') {\n          return { role: 'user', content: item }\n        }\n\n        if (item && typeof item === 'object') {\n          const obj = item as Record<string, unknown>\n          if ('text' in obj && obj.text) {\n            return { role: isString(obj.role) ? obj.role : 'user', content: obj.text }\n          }\n\n          if ('content' in obj && obj.content) {\n            // If content is a list, format it as content blocks\n            if (Array.isArray(obj.content)) {\n              const contentBlocks = this.formatPartsAsContentBlocks(obj.content)\n              return { role: isString(obj.role) ? obj.role : 'user', content: contentBlocks }\n            }\n            return { role: isString(obj.role) ? obj.role : 'user', content: obj.content }\n          }\n\n          if ('parts' in obj && Array.isArray(obj.parts)) {\n            const contentBlocks = this.formatPartsAsContentBlocks(obj.parts)\n            return {\n              role: isString(obj.role) ? obj.role : 'user',\n              content: contentBlocks,\n            }\n          }\n        }\n\n        return { role: 'user', content: toContentString(item) }\n      })\n    }\n\n    if (contents && typeof contents === 'object') {\n      const obj = contents as Record<string, unknown>\n      if ('text' in obj && obj.text) {\n        return [{ role: 'user', content: obj.text }]\n      }\n\n      if ('content' in obj && obj.content) {\n        return [{ role: 'user', content: obj.content }]\n      }\n    }\n\n    return [{ role: 'user', content: toContentString(contents) }]\n  }\n\n  private extractSystemInstruction(params: GenerateContentParameters): string | null {\n    if (!params || typeof params !== 'object' || !params.config) {\n      return null\n    }\n    const config = params.config as any\n    if (!('systemInstruction' in config)) {\n      return null\n    }\n    const systemInstruction = config.systemInstruction\n    if (typeof systemInstruction === 'string') {\n      return systemInstruction\n    }\n    if (systemInstruction && typeof systemInstruction === 'object' && 'text' in systemInstruction) {\n      return systemInstruction.text\n    }\n    if (\n      systemInstruction &&\n      typeof systemInstruction === 'object' &&\n      'parts' in systemInstruction &&\n      Array.isArray(systemInstruction.parts)\n    ) {\n      for (const part of systemInstruction.parts) {\n        if (part && typeof part === 'object' && 'text' in part && typeof part.text === 'string') {\n          return part.text\n        }\n      }\n    }\n    if (Array.isArray(systemInstruction)) {\n      for (const part of systemInstruction) {\n        if (typeof part === 'string') {\n          return part\n        }\n        if (part && typeof part === 'object' && 'text' in part && typeof part.text === 'string') {\n          return part.text\n        }\n      }\n    }\n    return null\n  }\n\n  private formatInputForPostHog(params: GenerateContentParameters): FormattedMessage[] {\n    const sanitized = sanitizeGemini(params.contents)\n    const messages = this.formatInput(sanitized)\n\n    const systemInstruction = this.extractSystemInstruction(params)\n\n    if (systemInstruction) {\n      const hasSystemMessage = messages.some((msg: FormattedMessage) => msg.role === 'system')\n\n      if (!hasSystemMessage) {\n        return [{ role: 'system', content: systemInstruction }, ...messages]\n      }\n    }\n\n    return messages\n  }\n}\n\n/**\n * Detect if Google Search grounding was used in the response.\n * Gemini bills per request that uses grounding, not per individual query.\n * Returns 1 if grounding was used, 0 otherwise.\n */\nfunction calculateGoogleWebSearchCount(response: unknown): number {\n  if (!response || typeof response !== 'object' || !('candidates' in response)) {\n    return 0\n  }\n\n  const candidates = response.candidates\n\n  if (!Array.isArray(candidates)) {\n    return 0\n  }\n\n  const hasGrounding = candidates.some((candidate: unknown) => {\n    if (!candidate || typeof candidate !== 'object') {\n      return false\n    }\n\n    // Check for grounding metadata\n    if ('groundingMetadata' in candidate && candidate.groundingMetadata) {\n      const metadata = candidate.groundingMetadata as any\n\n      if (typeof metadata === 'object') {\n        // Check if web_search_queries exists and is non-empty\n        if (\n          'webSearchQueries' in metadata &&\n          Array.isArray(metadata.webSearchQueries) &&\n          metadata.webSearchQueries.length > 0\n        ) {\n          return true\n        }\n\n        // Check if grounding_chunks exists and is non-empty\n        if (\n          'groundingChunks' in metadata &&\n          Array.isArray(metadata.groundingChunks) &&\n          metadata.groundingChunks.length > 0\n        ) {\n          return true\n        }\n      }\n    }\n\n    // Check for google search in function calls\n    if ('content' in candidate && candidate.content && typeof candidate.content === 'object') {\n      const content = candidate.content\n\n      if ('parts' in content && Array.isArray(content.parts)) {\n        return content.parts.some((part: unknown) => {\n          if (!part || typeof part !== 'object' || !('functionCall' in part)) {\n            return false\n          }\n\n          const functionCall = part.functionCall\n\n          if (\n            functionCall &&\n            typeof functionCall === 'object' &&\n            'name' in functionCall &&\n            typeof functionCall.name === 'string'\n          ) {\n            return functionCall.name.includes('google_search') || functionCall.name.includes('grounding')\n          }\n\n          return false\n        })\n      }\n    }\n\n    return false\n  })\n\n  return hasGrounding ? 1 : 0\n}\n\nexport default PostHogGoogleGenAI\nexport { PostHogGoogleGenAI as Gemini }\n"],"names":["isString","value","isObject","Array","isArray","REDACTED_IMAGE_PLACEHOLDER","isMultimodalEnabled","val","process","env","_INTERNAL_LLMA_MULTIMODAL","toLowerCase","isBase64DataUrl","str","test","isValidUrl","URL","startsWith","isRawBase64","length","redactBase64DataUrl","sanitizeGeminiPart","part","inlineData","data","processGeminiItem","item","parts","map","sanitizeGemini","toContentString","content","undefined","JSON","stringify","String","getModelParams","params","modelParams","paramKeys","key","formatResponseGemini","response","output","candidates","candidate","text","push","type","functionCall","function","name","arguments","args","mimeType","Uint8Array","Buffer","from","toString","binary","i","fromCharCode","btoa","mime_type","role","withPrivacyMode","client","privacyMode","input","privacy_mode","extractAvailableToolCalls","provider","config","tools","AIEvent","sanitizeValues","obj","jsonSafe","parse","TextDecoder","decode","TextEncoder","encode","Object","fromEntries","entries","k","v","POSTHOG_PARAMS_MAP","posthogDistinctId","posthogTraceId","posthogProperties","posthogPrivacyMode","posthogGroups","posthogModelOverride","posthogProviderOverride","posthogCostOverride","posthogCaptureImmediate","extractPosthogParams","body","providerParams","posthogParams","console","warn","addDefaults","traceId","uuidv4","sendEventWithErrorToPosthog","error","httpStatus","status","properties","enrichedError","options","enableExceptionAutocapture","exceptionId","uuidv7","captureException","$ai_trace_id","__posthog_previously_captured_error","sendEventToPosthog","eventType","Generation","distinctId","model","latency","timeToFirstToken","baseURL","usage","captureImmediate","capture","Promise","resolve","safeInput","safeOutput","safeError","errorData","$ai_is_erro