@posthog/ai/dist/openai/index.mjs.map

PostHog Node.js AI integrations

{"version":3,"file":"index.mjs","sources":["../../src/utils.ts","../../src/typeGuards.ts","../../src/sanitization.ts","../../src/openai/index.ts"],"sourcesContent":["import { PostHog } from 'posthog-node'\nimport { Buffer } from 'buffer'\nimport OpenAIOrignal from 'openai'\nimport AnthropicOriginal from '@anthropic-ai/sdk'\nimport type { ChatCompletionTool } from 'openai/resources/chat/completions'\nimport type { Tool as GeminiTool } from '@google/genai'\nimport type { FormattedMessage, FormattedContent, TokenUsage } from './types'\n\ntype ChatCompletionCreateParamsBase = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParams\ntype MessageCreateParams = AnthropicOriginal.Messages.MessageCreateParams\ntype ResponseCreateParams = OpenAIOrignal.Responses.ResponseCreateParams\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\nexport interface MonitoringParams {\n  posthogDistinctId?: string\n  posthogTraceId?: string\n  posthogProperties?: Record<string, any>\n  posthogPrivacyMode?: boolean\n  posthogGroups?: Record<string, any>\n  posthogModelOverride?: string\n  posthogProviderOverride?: string\n  posthogCostOverride?: CostOverride\n  posthogCaptureImmediate?: boolean\n}\n\nexport interface CostOverride {\n  inputCost: number\n  outputCost: number\n}\n\nexport const getModelParams = (\n  params: ((ChatCompletionCreateParamsBase | MessageCreateParams | ResponseCreateParams) & MonitoringParams) | 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  ] 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, mirroring Python's openai vs. anthropic approach.\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\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          }\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\nexport const truncate = (str: string): string => {\n  try {\n    const buffer = Buffer.from(str, STRING_FORMAT)\n    if (buffer.length <= MAX_OUTPUT_SIZE) {\n      return str\n    }\n    const truncatedBuffer = buffer.slice(0, MAX_OUTPUT_SIZE)\n    return `${truncatedBuffer.toString(STRING_FORMAT)}... [truncated]`\n  } catch (error) {\n    console.error('Error truncating, likely not a string')\n    return str\n  }\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    // Vercel AI SDK stores tools in params.mode.tools when mode type is 'regular'\n    if (params.mode?.type === 'regular' && params.mode.tools) {\n      return params.mode.tools\n    }\n\n    return null\n  }\n\n  return null\n}\n\nexport type SendEventToPosthogParams = {\n  client: PostHog\n  distinctId?: string\n  traceId: string\n  model: string\n  provider: string\n  input: any\n  output: any\n  latency: number\n  baseURL: string\n  httpStatus: number\n  usage?: TokenUsage\n  params: (ChatCompletionCreateParamsBase | MessageCreateParams | ResponseCreateParams) & MonitoringParams\n  isError?: boolean\n  error?: 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    return Buffer.from(jsonSafe, STRING_FORMAT).toString(STRING_FORMAT)\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\nexport const sendEventToPosthog = async ({\n  client,\n  distinctId,\n  traceId,\n  model,\n  provider,\n  input,\n  output,\n  latency,\n  baseURL,\n  params,\n  httpStatus = 200,\n  usage = {},\n  isError = false,\n  error,\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 (isError) {\n    errorData = {\n      $ai_is_error: true,\n      $ai_error: safeError,\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  }\n\n  const properties = {\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    $ai_output_tokens: usage.outputTokens ?? 0,\n    ...additionalTokenValues,\n    $ai_latency: latency,\n    $ai_trace_id: traceId,\n    $ai_base_url: baseURL,\n    ...params.posthogProperties,\n    ...(distinctId ? {} : { $process_person_profile: false }),\n    ...(tools ? { $ai_tools: tools } : {}),\n    ...errorData,\n    ...costOverrideData,\n  }\n\n  const event = {\n    distinctId: distinctId ?? traceId,\n    event: '$ai_generation',\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","// 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// 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 (!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  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 (!isObject(item)) return item\n\n  // Handle Anthropic's image format\n  if (\n    item.type === 'image' &&\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 (!isObject(part)) return part\n\n  // Handle Gemini's inline data format\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    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 { OpenAI as OpenAIOrignal, ClientOptions } from 'openai'\nimport { PostHog } from 'posthog-node'\nimport { v4 as uuidv4 } from 'uuid'\nimport { formatResponseOpenAI, MonitoringParams, sendEventToPosthog, extractAvailableToolCalls } from '../utils'\nimport type { APIPromise } from 'openai'\nimport type { Stream } from 'openai/streaming'\nimport type { ParsedResponse } from 'openai/resources/responses/responses'\nimport { sanitizeOpenAI, sanitizeOpenAIResponse } from '../sanitization'\n\nconst Chat = OpenAIOrignal.Chat\nconst Completions = Chat.Completions\nconst Responses = OpenAIOrignal.Responses\n\ntype ChatCompletion = OpenAIOrignal.ChatCompletion\ntype ChatCompletionChunk = OpenAIOrignal.ChatCompletionChunk\ntype ChatCompletionCreateParamsBase = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParams\ntype ChatCompletionCreateParamsNonStreaming = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParamsNonStreaming\ntype ChatCompletionCreateParamsStreaming = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParamsStreaming\ntype ResponsesCreateParamsBase = OpenAIOrignal.Responses.ResponseCreateParams\ntype ResponsesCreateParamsNonStreaming = OpenAIOrignal.Responses.ResponseCreateParamsNonStreaming\ntype ResponsesCreateParamsStreaming = OpenAIOrignal.Responses.ResponseCreateParamsStreaming\n\ninterface MonitoringOpenAIConfig extends ClientOptions {\n  apiKey: string\n  posthog: PostHog\n  baseURL?: string\n}\n\ntype RequestOptions = Record<string, any>\n\nexport class PostHogOpenAI extends OpenAIOrignal {\n  private readonly phClient: PostHog\n  public chat: WrappedChat\n  public responses: WrappedResponses\n\n  constructor(config: MonitoringOpenAIConfig) {\n    const { posthog, ...openAIConfig } = config\n    super(openAIConfig)\n    this.phClient = posthog\n    this.chat = new WrappedChat(this, this.phClient)\n    this.responses = new WrappedResponses(this, this.phClient)\n  }\n}\n\nexport class WrappedChat extends Chat {\n  constructor(parentClient: PostHogOpenAI, phClient: PostHog) {\n    super(parentClient)\n    this.completions = new WrappedCompletions(parentClient, phClient)\n  }\n\n  public completions: WrappedCompletions\n}\n\nexport class WrappedCompletions extends Completions {\n  private readonly phClient: PostHog\n\n  constructor(client: OpenAIOrignal, phClient: PostHog) {\n    super(client)\n    this.phClient = phClient\n  }\n\n  // --- Overload #1: Non-streaming\n  public create(\n    body: ChatCompletionCreateParamsNonStreaming & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<ChatCompletion>\n\n  // --- Overload #2: Streaming\n  public create(\n    body: ChatCompletionCreateParamsStreaming & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<Stream<ChatCompletionChunk>>\n\n  // --- Overload #3: Generic base\n  public create(\n    body: ChatCompletionCreateParamsBase & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<ChatCompletion | Stream<ChatCompletionChunk>>\n\n  // --- Implementation Signature\n  public create(\n    body: ChatCompletionCreateParamsBase & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<ChatCompletion | Stream<ChatCompletionChunk>> {\n    const {\n      posthogDistinctId,\n      posthogTraceId,\n      posthogProperties,\n      // eslint-disable-next-line @typescript-eslint/no-unused-vars\n      posthogPrivacyMode = false,\n      posthogGroups,\n      posthogCaptureImmediate,\n      ...openAIParams\n    } = body\n\n    const traceId = posthogTraceId ?? uuidv4()\n    const startTime = Date.now()\n\n    const parentPromise = super.create(openAIParams, options)\n\n    if (openAIParams.stream) {\n      return parentPromise.then((value) => {\n        if ('tee' in value) {\n          const [stream1, stream2] = value.tee()\n          ;(async () => {\n            try {\n              let accumulatedContent = ''\n              let usage: {\n                inputTokens?: number\n                outputTokens?: number\n                reasoningTokens?: number\n                cacheReadInputTokens?: number\n              } = {\n                inputTokens: 0,\n                outputTokens: 0,\n              }\n\n              for await (const chunk of stream1) {\n                const delta = chunk?.choices?.[0]?.delta?.content ?? ''\n                accumulatedContent += delta\n                if (chunk.usage) {\n                  usage = {\n                    inputTokens: chunk.usage.prompt_tokens ?? 0,\n                    outputTokens: chunk.usage.completion_tokens ?? 0,\n                    reasoningTokens: chunk.usage.completion_tokens_details?.reasoning_tokens ?? 0,\n                    cacheReadInputTokens: chunk.usage.prompt_tokens_details?.cached_tokens ?? 0,\n                  }\n                }\n              }\n\n              const latency = (Date.now() - startTime) / 1000\n              const availableTools = extractAvailableToolCalls('openai', openAIParams)\n              await sendEventToPosthog({\n                client: this.phClient,\n                distinctId: posthogDistinctId,\n                traceId,\n                model: openAIParams.model,\n                provider: 'openai',\n                input: sanitizeOpenAI(openAIParams.messages),\n                output: [{ content: accumulatedContent, role: 'assistant' }],\n                latency,\n                baseURL: (this as any).baseURL ?? '',\n                params: body,\n                httpStatus: 200,\n                usage,\n                tools: availableTools,\n                captureImmediate: posthogCaptureImmediate,\n              })\n            } catch (error: any) {\n              await sendEventToPosthog({\n                client: this.phClient,\n                distinctId: posthogDistinctId,\n                traceId,\n                model: openAIParams.model,\n                provider: 'openai',\n                input: sanitizeOpenAI(openAIParams.messages),\n                output: [],\n                latency: 0,\n                baseURL: (this as any).baseURL ?? '',\n                params: body,\n                httpStatus: error?.status ? error.status : 500,\n                usage: { inputTokens: 0, outputTokens: 0 },\n                isError: true,\n                error: JSON.stringify(error),\n                captureImmediate: posthogCaptureImmediate,\n              })\n            }\n          })()\n\n          // Return the other stream to the user\n          return stream2\n        }\n        return value\n      }) as APIPromise<Stream<ChatCompletionChunk>>\n    } else {\n      const wrappedPromise = parentPromise.then(\n        async (result) => {\n          if ('choices' in result) {\n            const latency = (Date.now() - startTime) / 1000\n            const availableTools = extractAvailableToolCalls('openai', openAIParams)\n            await sendEventToPosthog({\n              client: this.phClient,\n              distinctId: posthogDistinctId,\n              traceId,\n              model: openAIParams.model,\n              provider: 'openai',\n              input: sanitizeOpenAI(openAIParams.messages),\n              output: formatResponseOpenAI(result),\n              latency,\n              baseURL: (this as any).baseURL ?? '',\n              params: body,\n              httpStatus: 200,\n              usage: {\n                inputTokens: result.usage?.prompt_tokens ?? 0,\n                outputTokens: result.usage?.completion_tokens ?? 0,\n                reasoningTokens: result.usage?.completion_tokens_details?.reasoning_tokens ?? 0,\n                cacheReadInputTokens: result.usage?.prompt_tokens_details?.cached_tokens ?? 0,\n              },\n              tools: availableTools,\n              captureImmediate: posthogCaptureImmediate,\n            })\n          }\n          return result\n        },\n        async (error: any) => {\n          await sendEventToPosthog({\n            client: this.phClient,\n            distinctId: posthogDistinctId,\n            traceId,\n            model: openAIParams.model,\n            provider: 'openai',\n            input: sanitizeOpenAI(openAIParams.messages),\n            output: [],\n            latency: 0,\n            baseURL: (this as any).baseURL ?? '',\n            params: body,\n            httpStatus: error?.status ? error.status : 500,\n            usage: {\n              inputTokens: 0,\n              outputTokens: 0,\n            },\n            isError: true,\n            error: JSON.stringify(error),\n            captureImmediate: posthogCaptureImmediate,\n          })\n          throw error\n        }\n      ) as APIPromise<ChatCompletion>\n\n      return wrappedPromise\n    }\n  }\n}\n\nexport class WrappedResponses extends Responses {\n  private readonly phClient: PostHog\n\n  constructor(client: OpenAIOrignal, phClient: PostHog) {\n    super(client)\n    this.phClient = phClient\n  }\n\n  // --- Overload #1: Non-streaming\n  public create(\n    body: ResponsesCreateParamsNonStreaming & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<OpenAIOrignal.Responses.Response>\n\n  // --- Overload #2: Streaming\n  public create(\n    body: ResponsesCreateParamsStreaming & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<Stream<OpenAIOrignal.Responses.ResponseStreamEvent>>\n\n  // --- Overload #3: Generic base\n  public create(\n    body: ResponsesCreateParamsBase & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<OpenAIOrignal.Responses.Response | Stream<OpenAIOrignal.Responses.ResponseStreamEvent>>\n\n  // --- Implementation Signature\n  public create(\n    body: ResponsesCreateParamsBase & MonitoringParams,\n    options?: RequestOptions\n  ): APIPromise<OpenAIOrignal.Responses.Response | Stream<OpenAIOrignal.Responses.ResponseStreamEvent>> {\n    const {\n      posthogDistinctId,\n      posthogTraceId,\n      posthogProperties,\n      // eslint-disable-next-line @typescript-eslint/no-unused-vars\n      posthogPrivacyMode = false,\n      posthogGroups,\n      posthogCaptureImmediate,\n      ...openAIParams\n    } = body\n\n    const traceId = posthogTraceId ?? uuidv4()\n    const startTime = Date.now()\n\n    const parentPromise = super.create(openAIParams, options)\n\n    if (openAIParams.stream) {\n      return parentPromise.then((value) => {\n        if ('tee' in value && typeof (value as any).tee === 'function') {\n          const [stream1, stream2] = (value as any).tee()\n          ;(async () => {\n            try {\n              let finalContent: any[] = []\n              let usage: {\n                inputTokens?: number\n                outputTokens?: number\n                reasoningTokens?: number\n                cacheReadInputTokens?: number\n              } = {\n                inputTokens: 0,\n                outputTokens: 0,\n              }\n\n              for await (const chunk of stream1) {\n                if (\n                  chunk.type === 'response.completed' &&\n                  'response' in chunk &&\n                  chunk.response?.output &&\n                  chunk.response.output.length > 0\n                ) {\n                  finalContent = chunk.response.output\n                }\n                if ('response' in chunk && chunk.response?.usage) {\n                  usage = {\n                    inputTokens: chunk.response.usage.input_tokens ?? 0,\n                    outputTokens: chunk.response.usage.output_tokens ?? 0,\n                    reasoningTokens: chunk.response.usage.output_tokens_details?.reasoning_tokens ?? 0,\n                    cacheReadInputTokens: chunk.response.usage.input_tokens_details?.cached_tokens ?? 0,\n                  }\n                }\n              }\n\n              const latency = (Date.now() - startTime) / 1000\n              const availableTools = extractAvailableToolCalls('openai', openAIParams)\n              await sendEventToPosthog({\n                client: this.phClient,\n                distinctId: posthogDistinctId,\n                traceId,\n                //@ts-expect-error\n                model: openAIParams.model,\n                provider: 'openai',\n                input: sanitizeOpenAIResponse(openAIParams.input),\n                output: finalContent,\n                latency,\n                baseURL: (this as any).baseURL ?? '',\n                params: body,\n                httpStatus: 200,\n                usage,\n                tools: availableTools,\n                captureImmediate: posthogCaptureImmediate,\n              })\n            } catch (error: any) {\n              await sendEventToPosthog({\n                client: this.phClient,\n                distinctId: posthogDistinctId,\n                traceId,\n                //@ts-expect-error\n                model: openAIParams.model,\n                provider: 'openai',\n                input: sanitizeOpenAIResponse(openAIParams.input),\n                output: [],\n                latency: 0,\n                baseURL: (this as any).baseURL ?? '',\n                params: body,\n                httpStatus: error?.status ? error.status : 500,\n                usage: { inputTokens: 0, outputTokens: 0 },\n                isError: true,\n                error: JSON.stringify(error),\n                captureImmediate: posthogCaptureImmediate,\n              })\n            }\n          })()\n\n          return stream2\n        }\n        return value\n      }) as APIPromise<Stream<OpenAIOrignal.Responses.ResponseStreamEvent>>\n    } else {\n      const wrappedPromise = parentPromise.then(\n        async (result) => {\n          if ('output' in result) {\n            const latency = (Date.now() - startTime) / 1000\n            const availableTools = extractAvailableToolCalls('openai', openAIParams)\n            await sendEventToPosthog({\n              client: this.phClient,\n              distinctId: posthogDistinctId,\n              traceId,\n              //@ts-expect-error\n              model: openAIParams.model,\n              provider: 'openai',\n              input: sanitizeOpenAIResponse(openAIParams.input),\n              output: formatResponseOpenAI({ output: result.output }),\n              latency,\n              baseURL: (this as any).baseURL ?? '',\n              params: body,\n              httpStatus: 200,\n              usage: {\n                inputTokens: result.usage?.input_tokens ?? 0,\n                outputTokens: result.usage?.output_tokens ?? 0,\n                reasoningTokens: result.usage?.output_tokens_details?.reasoning_tokens ?? 0,\n                cacheReadInputTokens: result.usage?.input_tokens_details?.cached_tokens ?? 0,\n              },\n              tools: availableTools,\n              captureImmediate: posthogCaptureImmediate,\n            })\n          }\n          return result\n        },\n        async (error: any) => {\n          await sendEventToPosthog({\n            client: this.phClient,\n            distinctId: posthogDistinctId,\n            traceId,\n            //@ts-expect-error\n            model: openAIParams.model,\n            provider: 'openai',\n            input: sanitizeOpenAIResponse(openAIParams.input),\n            output: [],\n            latency: 0,\n            baseURL: (this as any).baseURL ?? 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'',\n            params: body,\n            httpStatus: 200,\n            usage: {\n              inputTokens: result.usage?.input_tokens ?? 0,\n              outputTokens: result.usage?.output_tokens ?? 0,\n              reasoningTokens: result.usage?.output_tokens_details?.reasoning_tokens ?? 0,\n              cacheReadInputTokens: result.usage?.input_tokens_details?.cached_tokens ?? 0,\n            },\n            captureImmediate: posthogCaptureImmediate,\n          })\n          return result\n        },\n        async (error: any) => {\n          await sendEventToPosthog({\n            client: this.phClient,\n            distinctId: posthogDistinctId,\n            traceId,\n            //@ts-expect-error\n            model: openAIParams.model,\n            provider: 'openai',\n            input: sanitizeOpenAIResponse(openAIParams.input),\n            output: [],\n            latency: 0,\n            baseURL: (this as any).baseURL ?? 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