{"version":3,"file":"index.mjs","sources":["../src/utils.ts","../src/openai/index.ts","../src/openai/azure.ts","../src/vercel/middleware.ts","../src/anthropic/index.ts","../src/gemini/index.ts","../../node_modules/decamelize/index.js","../../node_modules/camelcase/index.js","../../node_modules/@langchain/core/dist/load/map_keys.js","../../node_modules/@langchain/core/dist/load/serializable.js","../../node_modules/@langchain/core/dist/utils/env.js","../../node_modules/@langchain/core/dist/callbacks/base.js","../src/langchain/callbacks.ts"],"sourcesContent":["import { PostHog } from 'posthog-node'\nimport { Buffer } from 'buffer'\nimport OpenAIOrignal from 'openai'\nimport AnthropicOriginal from '@anthropic-ai/sdk'\n\ntype ChatCompletionCreateParamsBase = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParams\ntype MessageCreateParams = AnthropicOriginal.Messages.MessageCreateParams\ntype ResponseCreateParams = OpenAIOrignal.Responses.ResponseCreateParams\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\n posthogPrivacyMode?: boolean\n posthogGroups?: Record\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 => {\n if (!params) {\n return {}\n }\n const modelParams: Record = {}\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): Array<{ role: string; content: string }> => {\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): Array<{ role: string; content: string }> => {\n // Example approach if \"response.content\" holds array of text segments, etc.\n const output: Array<{ role: string; content: string }> = []\n for (const choice of response.content ?? []) {\n if (choice?.text) {\n output.push({\n role: 'assistant',\n content: choice.text,\n })\n }\n }\n return output\n}\n\nexport const formatResponseOpenAI = (response: any): Array<{ role: string; content: string }> => {\n const output: Array<{ role: string; content: string }> = []\n for (const choice of response.choices ?? []) {\n if (choice.message?.content) {\n output.push({\n role: choice.message.role,\n content: choice.message.content,\n })\n }\n }\n return output\n}\n\nexport const formatResponseGemini = (response: any): Array<{ role: string; content: string }> => {\n const output: Array<{ role: string; content: string }> = []\n\n if (response.text) {\n output.push({\n role: 'assistant',\n content: response.text,\n })\n return output\n }\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 text = candidate.content.parts\n .filter((part: any) => part.text)\n .map((part: any) => part.text)\n .join('')\n if (text) {\n output.push({\n role: 'assistant',\n content: text,\n })\n }\n }\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\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?: {\n inputTokens?: number\n outputTokens?: number\n reasoningTokens?: any\n cacheReadInputTokens?: any\n cacheCreationInputTokens?: any\n }\n params: (ChatCompletionCreateParamsBase | MessageCreateParams | ResponseCreateParams) & MonitoringParams\n isError?: boolean\n error?: string\n tools?: any\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 => {\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","import OpenAIOrignal, { ClientOptions } from 'openai'\nimport { PostHog } from 'posthog-node'\nimport { v4 as uuidv4 } from 'uuid'\nimport { formatResponseOpenAI, MonitoringParams, sendEventToPosthog } from '../utils'\nimport type { APIPromise } from 'openai'\nimport type { Stream } from 'openai/streaming'\nimport type { ParsedResponse } from 'openai/resources/responses/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\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 OpenAIOrignal.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 OpenAIOrignal.Chat.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\n\n // --- Overload #2: Streaming\n public create(\n body: ChatCompletionCreateParamsStreaming & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Overload #3: Generic base\n public create(\n body: ChatCompletionCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Implementation Signature\n public create(\n body: ChatCompletionCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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 await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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 captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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>\n } else {\n const wrappedPromise = parentPromise.then(\n async (result) => {\n if ('choices' in result) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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 captureImmediate: posthogCaptureImmediate,\n })\n }\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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\n\n return wrappedPromise\n }\n }\n}\n\nexport class WrappedResponses extends OpenAIOrignal.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\n\n // --- Overload #2: Streaming\n public create(\n body: ResponsesCreateParamsStreaming & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Overload #3: Generic base\n public create(\n body: ResponsesCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Implementation Signature\n public create(\n body: ResponsesCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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 await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: openAIParams.input,\n output: finalContent,\n latency,\n baseURL: (this as any).baseURL ?? '',\n params: body,\n httpStatus: 200,\n usage,\n captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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>\n } else {\n const wrappedPromise = parentPromise.then(\n async (result) => {\n if ('output' in result) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: openAIParams.input,\n 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 captureImmediate: posthogCaptureImmediate,\n })\n }\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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: {\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\n\n return wrappedPromise\n }\n }\n\n public parse(\n body: Params & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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 // Create a temporary instance that bypasses our wrapped create method\n const originalCreate = super.create.bind(this)\n const originalSelf = this as any\n const tempCreate = originalSelf.create\n originalSelf.create = originalCreate\n\n try {\n const parentPromise = super.parse(openAIParams, options)\n\n const wrappedPromise = parentPromise.then(\n async (result) => {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: openAIParams.input,\n 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 captureImmediate: posthogCaptureImmediate,\n })\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'openai',\n input: 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: {\n inputTokens: 0,\n outputTokens: 0,\n },\n isError: true,\n error: JSON.stringify(error),\n captureImmediate: posthogCaptureImmediate,\n })\n throw error\n }\n )\n\n return wrappedPromise as APIPromise>\n } finally {\n // Restore our wrapped create method\n originalSelf.create = tempCreate\n }\n }\n}\n\nexport default PostHogOpenAI\n\nexport { PostHogOpenAI as OpenAI }\n","import OpenAIOrignal, { AzureOpenAI } from 'openai'\nimport { PostHog } from 'posthog-node'\nimport { v4 as uuidv4 } from 'uuid'\nimport { formatResponseOpenAI, MonitoringParams, sendEventToPosthog } from '../utils'\nimport type { APIPromise } from 'openai'\nimport type { Stream } from 'openai/streaming'\nimport type { ParsedResponse } from 'openai/resources/responses/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 {\n apiKey: string\n posthog: PostHog\n baseURL?: string\n}\n\ntype RequestOptions = Record\n\nexport class PostHogAzureOpenAI extends AzureOpenAI {\n private readonly phClient: PostHog\n public chat: WrappedChat\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 }\n}\n\nexport class WrappedChat extends AzureOpenAI.Chat {\n constructor(parentClient: PostHogAzureOpenAI, phClient: PostHog) {\n super(parentClient)\n this.completions = new WrappedCompletions(parentClient, phClient)\n }\n\n public completions: WrappedCompletions\n}\n\nexport class WrappedCompletions extends AzureOpenAI.Chat.Completions {\n private readonly phClient: PostHog\n\n constructor(client: AzureOpenAI, 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\n\n // --- Overload #2: Streaming\n public create(\n body: ChatCompletionCreateParamsStreaming & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Overload #3: Generic base\n public create(\n body: ChatCompletionCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Implementation Signature\n public create(\n body: ChatCompletionCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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 await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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 captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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>\n } else {\n const wrappedPromise = parentPromise.then(\n async (result) => {\n if ('choices' in result) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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 captureImmediate: posthogCaptureImmediate,\n })\n }\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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\n\n return wrappedPromise\n }\n }\n}\n\nexport class WrappedResponses extends AzureOpenAI.Responses {\n private readonly phClient: PostHog\n\n constructor(client: AzureOpenAI, 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\n\n // --- Overload #2: Streaming\n public create(\n body: ResponsesCreateParamsStreaming & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Overload #3: Generic base\n public create(\n body: ResponsesCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n\n // --- Implementation Signature\n public create(\n body: ResponsesCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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 ('usage' in chunk && chunk.usage) {\n usage = {\n inputTokens: chunk.usage.input_tokens ?? 0,\n outputTokens: chunk.usage.output_tokens ?? 0,\n reasoningTokens: chunk.usage.output_tokens_details?.reasoning_tokens ?? 0,\n cacheReadInputTokens: chunk.usage.input_tokens_details?.cached_tokens ?? 0,\n }\n }\n }\n\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: openAIParams.input,\n output: finalContent,\n latency,\n baseURL: (this as any).baseURL ?? '',\n params: body,\n httpStatus: 200,\n usage,\n captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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>\n } else {\n const wrappedPromise = parentPromise.then(\n async (result) => {\n if ('output' in result) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: openAIParams.input,\n 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 captureImmediate: posthogCaptureImmediate,\n })\n }\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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: {\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\n\n return wrappedPromise\n }\n }\n\n public parse(\n body: Params & MonitoringParams,\n options?: RequestOptions\n ): APIPromise> {\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.parse(openAIParams, options)\n\n const wrappedPromise = parentPromise.then(\n async (result) => {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: openAIParams.input,\n 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 captureImmediate: posthogCaptureImmediate,\n })\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: openAIParams.model,\n provider: 'azure',\n input: 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: {\n inputTokens: 0,\n outputTokens: 0,\n },\n isError: true,\n error: JSON.stringify(error),\n captureImmediate: posthogCaptureImmediate,\n })\n throw error\n }\n )\n\n return wrappedPromise as APIPromise>\n }\n}\n\nexport default PostHogAzureOpenAI\n\nexport { PostHogAzureOpenAI as OpenAI }\n","import { experimental_wrapLanguageModel as wrapLanguageModel } from 'ai'\nimport type { LanguageModelV1, LanguageModelV1Middleware, LanguageModelV1Prompt, LanguageModelV1StreamPart } from 'ai'\nimport { v4 as uuidv4 } from 'uuid'\nimport { PostHog } from 'posthog-node'\nimport { CostOverride, sendEventToPosthog, truncate, MAX_OUTPUT_SIZE } from '../utils'\nimport { Buffer } from 'buffer'\n\ninterface ClientOptions {\n posthogDistinctId?: string\n posthogTraceId?: string\n posthogProperties?: Record\n posthogPrivacyMode?: boolean\n posthogGroups?: Record\n posthogModelOverride?: string\n posthogProviderOverride?: string\n posthogCostOverride?: CostOverride\n posthogCaptureImmediate?: boolean\n}\n\ninterface CreateInstrumentationMiddlewareOptions {\n posthogDistinctId: string\n posthogTraceId: string\n posthogProperties?: Record\n posthogPrivacyMode?: boolean\n posthogGroups?: Record\n posthogModelOverride?: string\n posthogProviderOverride?: string\n posthogCostOverride?: CostOverride\n posthogCaptureImmediate?: boolean\n}\n\ninterface PostHogInput {\n role: string\n type?: string\n content?:\n | string\n | {\n [key: string]: any\n }\n}\n\nconst mapVercelParams = (params: any): Record => {\n return {\n temperature: params.temperature,\n max_tokens: params.maxTokens,\n top_p: params.topP,\n frequency_penalty: params.frequencyPenalty,\n presence_penalty: params.presencePenalty,\n stop: params.stopSequences,\n stream: params.stream,\n }\n}\n\nconst mapVercelPrompt = (prompt: LanguageModelV1Prompt): PostHogInput[] => {\n // normalize single inputs into an array of messages\n let promptsArray: any[]\n if (typeof prompt === 'string') {\n promptsArray = [{ role: 'user', content: prompt }]\n } else if (!Array.isArray(prompt)) {\n promptsArray = [prompt]\n } else {\n promptsArray = prompt\n }\n\n // Map and truncate individual content\n const inputs: PostHogInput[] = promptsArray.map((p) => {\n let content = {}\n if (Array.isArray(p.content)) {\n content = p.content.map((c: any) => {\n if (c.type === 'text') {\n return {\n type: 'text',\n content: truncate(c.text),\n }\n } else if (c.type === 'image') {\n return {\n type: 'image',\n content: {\n // if image is a url use it, or use \"none supported\"\n image: c.image instanceof URL ? c.image.toString() : 'raw images not supported',\n mimeType: c.mimeType,\n },\n }\n } else if (c.type === 'file') {\n return {\n type: 'file',\n content: {\n file: c.data instanceof URL ? c.data.toString() : 'raw files not supported',\n mimeType: c.mimeType,\n },\n }\n } else if (c.type === 'tool-call') {\n return {\n type: 'tool-call',\n content: {\n toolCallId: c.toolCallId,\n toolName: c.toolName,\n args: c.args,\n },\n }\n } else if (c.type === 'tool-result') {\n return {\n type: 'tool-result',\n content: {\n toolCallId: c.toolCallId,\n toolName: c.toolName,\n result: c.result,\n isError: c.isError,\n },\n }\n }\n return {\n content: '',\n }\n })\n } else {\n content = {\n type: 'text',\n text: truncate(p.content),\n }\n }\n return {\n role: p.role,\n content,\n }\n })\n try {\n // Trim the inputs array until its JSON size fits within MAX_OUTPUT_SIZE\n let serialized = JSON.stringify(inputs)\n let removedCount = 0\n // We need to keep track of the initial size of the inputs array because we're going to be mutating it\n const initialSize = inputs.length\n for (let i = 0; i < initialSize && Buffer.byteLength(serialized, 'utf8') > MAX_OUTPUT_SIZE; i++) {\n inputs.shift()\n removedCount++\n serialized = JSON.stringify(inputs)\n }\n if (removedCount > 0) {\n // Add one placeholder to indicate how many were removed\n inputs.unshift({\n role: 'posthog',\n content: `[${removedCount} message${removedCount === 1 ? '' : 's'} removed due to size limit]`,\n })\n }\n } catch (error) {\n console.error('Error stringifying inputs', error)\n return [{ role: 'posthog', content: 'An error occurred while processing your request. Please try again.' }]\n }\n return inputs\n}\n\nconst mapVercelOutput = (result: any): PostHogInput[] => {\n // normalize string results to object\n const normalizedResult = typeof result === 'string' ? { text: result } : result\n const output = {\n ...(normalizedResult.text ? { text: normalizedResult.text } : {}),\n ...(normalizedResult.object ? { object: normalizedResult.object } : {}),\n ...(normalizedResult.reasoning ? { reasoning: normalizedResult.reasoning } : {}),\n ...(normalizedResult.response ? { response: normalizedResult.response } : {}),\n ...(normalizedResult.finishReason ? { finishReason: normalizedResult.finishReason } : {}),\n ...(normalizedResult.usage ? { usage: normalizedResult.usage } : {}),\n ...(normalizedResult.warnings ? { warnings: normalizedResult.warnings } : {}),\n ...(normalizedResult.providerMetadata ? { toolCalls: normalizedResult.providerMetadata } : {}),\n ...(normalizedResult.files\n ? {\n files: normalizedResult.files.map((file: any) => ({\n name: file.name,\n size: file.size,\n type: file.type,\n })),\n }\n : {}),\n }\n if (output.text && !output.object && !output.reasoning) {\n return [{ content: truncate(output.text as string), role: 'assistant' }]\n }\n // otherwise stringify and truncate\n try {\n const jsonOutput = JSON.stringify(output)\n return [{ content: truncate(jsonOutput), role: 'assistant' }]\n } catch (error) {\n console.error('Error stringifying output')\n return []\n }\n}\n\nconst extractProvider = (model: LanguageModelV1): string => {\n const provider = model.provider.toLowerCase()\n const providerName = provider.split('.')[0]\n return providerName\n}\n\nexport const createInstrumentationMiddleware = (\n phClient: PostHog,\n model: LanguageModelV1,\n options: CreateInstrumentationMiddlewareOptions\n): LanguageModelV1Middleware => {\n const middleware: LanguageModelV1Middleware = {\n wrapGenerate: async ({ doGenerate, params }) => {\n const startTime = Date.now()\n const mergedParams = {\n ...options,\n ...mapVercelParams(params),\n }\n try {\n const result = await doGenerate()\n const latency = (Date.now() - startTime) / 1000\n const modelId =\n options.posthogModelOverride ?? (result.response?.modelId ? result.response.modelId : model.modelId)\n const provider = options.posthogProviderOverride ?? extractProvider(model)\n const baseURL = '' // cannot currently get baseURL from vercel\n const content = mapVercelOutput(result)\n // let tools = result.toolCalls\n const providerMetadata = result.providerMetadata\n const additionalTokenValues = {\n ...(providerMetadata?.openai?.reasoningTokens\n ? { reasoningTokens: providerMetadata.openai.reasoningTokens }\n : {}),\n ...(providerMetadata?.openai?.cachedPromptTokens\n ? { cacheReadInputTokens: providerMetadata.openai.cachedPromptTokens }\n : {}),\n ...(providerMetadata?.anthropic\n ? {\n cacheReadInputTokens: providerMetadata.anthropic.cacheReadInputTokens,\n cacheCreationInputTokens: providerMetadata.anthropic.cacheCreationInputTokens,\n }\n : {}),\n }\n await sendEventToPosthog({\n client: phClient,\n distinctId: options.posthogDistinctId,\n traceId: options.posthogTraceId,\n model: modelId,\n provider: provider,\n input: options.posthogPrivacyMode ? '' : mapVercelPrompt(params.prompt),\n output: [{ content, role: 'assistant' }],\n latency,\n baseURL,\n params: mergedParams as any,\n httpStatus: 200,\n usage: {\n inputTokens: result.usage.promptTokens,\n outputTokens: result.usage.completionTokens,\n ...additionalTokenValues,\n },\n captureImmediate: options.posthogCaptureImmediate,\n })\n\n return result\n } catch (error: any) {\n const modelId = model.modelId\n await sendEventToPosthog({\n client: phClient,\n distinctId: options.posthogDistinctId,\n traceId: options.posthogTraceId,\n model: modelId,\n provider: model.provider,\n input: options.posthogPrivacyMode ? '' : mapVercelPrompt(params.prompt),\n output: [],\n latency: 0,\n baseURL: '',\n params: mergedParams as any,\n httpStatus: error?.status ? error.status : 500,\n usage: {\n inputTokens: 0,\n outputTokens: 0,\n },\n isError: true,\n error: truncate(JSON.stringify(error)),\n captureImmediate: options.posthogCaptureImmediate,\n })\n throw error\n }\n },\n\n wrapStream: async ({ doStream, params }) => {\n const startTime = Date.now()\n let generatedText = ''\n let usage: {\n inputTokens?: number\n outputTokens?: number\n reasoningTokens?: any\n cacheReadInputTokens?: any\n cacheCreationInputTokens?: any\n } = {}\n const mergedParams = {\n ...options,\n ...mapVercelParams(params),\n }\n\n const modelId = options.posthogModelOverride ?? model.modelId\n const provider = options.posthogProviderOverride ?? extractProvider(model)\n const baseURL = '' // cannot currently get baseURL from vercel\n try {\n const { stream, ...rest } = await doStream()\n const transformStream = new TransformStream({\n transform(chunk, controller) {\n if (chunk.type === 'text-delta') {\n generatedText += chunk.textDelta\n }\n if (chunk.type === 'finish') {\n usage = {\n inputTokens: chunk.usage?.promptTokens,\n outputTokens: chunk.usage?.completionTokens,\n }\n if (chunk.providerMetadata?.openai?.reasoningTokens) {\n usage.reasoningTokens = chunk.providerMetadata.openai.reasoningTokens\n }\n if (chunk.providerMetadata?.openai?.cachedPromptTokens) {\n usage.cacheReadInputTokens = chunk.providerMetadata.openai.cachedPromptTokens\n }\n if (chunk.providerMetadata?.anthropic?.cacheReadInputTokens) {\n usage.cacheReadInputTokens = chunk.providerMetadata.anthropic.cacheReadInputTokens\n }\n if (chunk.providerMetadata?.anthropic?.cacheCreationInputTokens) {\n usage.cacheCreationInputTokens = chunk.providerMetadata.anthropic.cacheCreationInputTokens\n }\n }\n controller.enqueue(chunk)\n },\n\n flush: async () => {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: phClient,\n distinctId: options.posthogDistinctId,\n traceId: options.posthogTraceId,\n model: modelId,\n provider: provider,\n input: options.posthogPrivacyMode ? '' : mapVercelPrompt(params.prompt),\n output: [{ content: generatedText, role: 'assistant' }],\n latency,\n baseURL,\n params: mergedParams as any,\n httpStatus: 200,\n usage,\n captureImmediate: options.posthogCaptureImmediate,\n })\n },\n })\n\n return {\n stream: stream.pipeThrough(transformStream),\n ...rest,\n }\n } catch (error: any) {\n await sendEventToPosthog({\n client: phClient,\n distinctId: options.posthogDistinctId,\n traceId: options.posthogTraceId,\n model: modelId,\n provider: provider,\n input: options.posthogPrivacyMode ? '' : mapVercelPrompt(params.prompt),\n output: [],\n latency: 0,\n baseURL: '',\n params: mergedParams as any,\n httpStatus: error?.status ? error.status : 500,\n usage: {\n inputTokens: 0,\n outputTokens: 0,\n },\n isError: true,\n error: truncate(JSON.stringify(error)),\n captureImmediate: options.posthogCaptureImmediate,\n })\n throw error\n }\n },\n }\n\n return middleware\n}\n\nexport const wrapVercelLanguageModel = (\n model: LanguageModelV1,\n phClient: PostHog,\n options: ClientOptions\n): LanguageModelV1 => {\n const traceId = options.posthogTraceId ?? uuidv4()\n const middleware = createInstrumentationMiddleware(phClient, model, {\n ...options,\n posthogTraceId: traceId,\n posthogDistinctId: options.posthogDistinctId ?? traceId,\n })\n\n const wrappedModel = wrapLanguageModel({\n model,\n middleware,\n })\n\n return wrappedModel\n}\n","import AnthropicOriginal from '@anthropic-ai/sdk'\nimport { PostHog } from 'posthog-node'\nimport { v4 as uuidv4 } from 'uuid'\nimport { formatResponseAnthropic, mergeSystemPrompt, MonitoringParams, sendEventToPosthog } from '../utils'\n\ntype MessageCreateParamsNonStreaming = AnthropicOriginal.Messages.MessageCreateParamsNonStreaming\ntype MessageCreateParamsStreaming = AnthropicOriginal.Messages.MessageCreateParamsStreaming\ntype MessageCreateParams = AnthropicOriginal.Messages.MessageCreateParams\ntype Message = AnthropicOriginal.Messages.Message\ntype RawMessageStreamEvent = AnthropicOriginal.Messages.RawMessageStreamEvent\ntype MessageCreateParamsBase = AnthropicOriginal.Messages.MessageCreateParams\n\nimport type { APIPromise, RequestOptions } from '@anthropic-ai/sdk/core'\nimport type { Stream } from '@anthropic-ai/sdk/streaming'\n\ninterface MonitoringAnthropicConfig {\n apiKey: string\n posthog: PostHog\n baseURL?: string\n}\n\nexport class PostHogAnthropic extends AnthropicOriginal {\n private readonly phClient: PostHog\n public messages: WrappedMessages\n\n constructor(config: MonitoringAnthropicConfig) {\n const { posthog, ...anthropicConfig } = config\n super(anthropicConfig)\n this.phClient = posthog\n this.messages = new WrappedMessages(this, this.phClient)\n }\n}\n\nexport class WrappedMessages extends AnthropicOriginal.Messages {\n private readonly phClient: PostHog\n\n constructor(parentClient: PostHogAnthropic, phClient: PostHog) {\n super(parentClient)\n this.phClient = phClient\n }\n\n public create(body: MessageCreateParamsNonStreaming, options?: RequestOptions): APIPromise\n public create(\n body: MessageCreateParamsStreaming & MonitoringParams,\n options?: RequestOptions\n ): APIPromise>\n public create(\n body: MessageCreateParamsBase & MonitoringParams,\n options?: RequestOptions\n ): APIPromise | Message>\n public create(\n body: MessageCreateParams & MonitoringParams,\n options?: RequestOptions\n ): APIPromise | APIPromise> {\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 ...anthropicParams\n } = body\n\n const traceId = posthogTraceId ?? uuidv4()\n const startTime = Date.now()\n\n const parentPromise = super.create(anthropicParams, options)\n\n if (anthropicParams.stream) {\n return parentPromise.then((value) => {\n let accumulatedContent = ''\n const usage: {\n inputTokens: number\n outputTokens: number\n cacheCreationInputTokens?: number\n cacheReadInputTokens?: number\n } = {\n inputTokens: 0,\n outputTokens: 0,\n cacheCreationInputTokens: 0,\n cacheReadInputTokens: 0,\n }\n if ('tee' in value) {\n const [stream1, stream2] = value.tee()\n ;(async () => {\n try {\n for await (const chunk of stream1) {\n if ('delta' in chunk) {\n if ('text' in chunk.delta) {\n const delta = chunk?.delta?.text ?? ''\n accumulatedContent += delta\n }\n }\n if (chunk.type == 'message_start') {\n usage.inputTokens = chunk.message.usage.input_tokens ?? 0\n usage.cacheCreationInputTokens = chunk.message.usage.cache_creation_input_tokens ?? 0\n usage.cacheReadInputTokens = chunk.message.usage.cache_read_input_tokens ?? 0\n }\n if ('usage' in chunk) {\n usage.outputTokens = chunk.usage.output_tokens ?? 0\n }\n }\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: anthropicParams.model,\n provider: 'anthropic',\n input: mergeSystemPrompt(anthropicParams, 'anthropic'),\n output: [{ content: accumulatedContent, role: 'assistant' }],\n latency,\n baseURL: (this as any).baseURL ?? '',\n params: body,\n httpStatus: 200,\n usage,\n captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n // error handling\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: anthropicParams.model,\n provider: 'anthropic',\n input: mergeSystemPrompt(anthropicParams, 'anthropic'),\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 }\n })()\n\n // Return the other stream to the user\n return stream2\n }\n return value\n }) as APIPromise>\n } else {\n const wrappedPromise = parentPromise.then(\n async (result) => {\n if ('content' in result) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: anthropicParams.model,\n provider: 'anthropic',\n input: mergeSystemPrompt(anthropicParams, 'anthropic'),\n output: formatResponseAnthropic(result),\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 cacheCreationInputTokens: result.usage.cache_creation_input_tokens ?? 0,\n cacheReadInputTokens: result.usage.cache_read_input_tokens ?? 0,\n },\n captureImmediate: posthogCaptureImmediate,\n })\n }\n return result\n },\n async (error: any) => {\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: anthropicParams.model,\n provider: 'anthropic',\n input: mergeSystemPrompt(anthropicParams, 'anthropic'),\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\n\n return wrappedPromise\n }\n }\n}\n\nexport default PostHogAnthropic\n\nexport { PostHogAnthropic as Anthropic }\n","import { GoogleGenAI } from '@google/genai'\nimport { PostHog } from 'posthog-node'\nimport { v4 as uuidv4 } from 'uuid'\nimport { MonitoringParams, sendEventToPosthog } from '../utils'\n\n// Types from @google/genai\ntype GenerateContentRequest = {\n model: string\n contents: any\n config?: any\n [key: string]: any\n}\n\ntype GenerateContentResponse = {\n text?: string\n candidates?: any[]\n usageMetadata?: {\n promptTokenCount?: number\n candidatesTokenCount?: number\n totalTokenCount?: number\n }\n [key: string]: any\n}\n\ninterface MonitoringGeminiConfig {\n apiKey?: string\n vertexai?: boolean\n project?: string\n location?: string\n apiVersion?: string\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: GenerateContentRequest & MonitoringParams): Promise {\n const {\n posthogDistinctId,\n posthogTraceId,\n posthogProperties,\n posthogGroups,\n posthogCaptureImmediate,\n ...geminiParams\n } = params\n\n const traceId = posthogTraceId ?? uuidv4()\n const startTime = Date.now()\n\n try {\n const response = await this.client.models.generateContent(geminiParams)\n const latency = (Date.now() - startTime) / 1000\n\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: geminiParams.model,\n provider: 'gemini',\n input: this.formatInput(geminiParams.contents),\n output: this.formatOutput(response),\n latency,\n baseURL: 'https://generativelanguage.googleapis.com',\n params: params as any,\n httpStatus: 200,\n usage: {\n inputTokens: response.usageMetadata?.promptTokenCount ?? 0,\n outputTokens: response.usageMetadata?.candidatesTokenCount ?? 0,\n },\n captureImmediate: posthogCaptureImmediate,\n })\n\n return response\n } catch (error: any) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: geminiParams.model,\n provider: 'gemini',\n input: this.formatInput(geminiParams.contents),\n output: [],\n latency,\n baseURL: 'https://generativelanguage.googleapis.com',\n params: params as any,\n httpStatus: 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 }\n\n public async *generateContentStream(\n params: GenerateContentRequest & MonitoringParams\n ): AsyncGenerator {\n const {\n posthogDistinctId,\n posthogTraceId,\n posthogProperties,\n posthogGroups,\n posthogCaptureImmediate,\n ...geminiParams\n } = params\n\n const traceId = posthogTraceId ?? uuidv4()\n const startTime = Date.now()\n let accumulatedContent = ''\n let usage = {\n inputTokens: 0,\n outputTokens: 0,\n }\n\n try {\n const stream = await this.client.models.generateContentStream(geminiParams)\n\n for await (const chunk of stream) {\n if (chunk.text) {\n accumulatedContent += chunk.text\n }\n if (chunk.usageMetadata) {\n usage = {\n inputTokens: chunk.usageMetadata.promptTokenCount ?? 0,\n outputTokens: chunk.usageMetadata.candidatesTokenCount ?? 0,\n }\n }\n yield chunk\n }\n\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: geminiParams.model,\n provider: 'gemini',\n input: this.formatInput(geminiParams.contents),\n output: [{ content: accumulatedContent, role: 'assistant' }],\n latency,\n baseURL: 'https://generativelanguage.googleapis.com',\n params: params as any,\n httpStatus: 200,\n usage,\n captureImmediate: posthogCaptureImmediate,\n })\n } catch (error: any) {\n const latency = (Date.now() - startTime) / 1000\n await sendEventToPosthog({\n client: this.phClient,\n distinctId: posthogDistinctId ?? traceId,\n traceId,\n model: geminiParams.model,\n provider: 'gemini',\n input: this.formatInput(geminiParams.contents),\n output: [],\n latency,\n baseURL: 'https://generativelanguage.googleapis.com',\n params: params as any,\n httpStatus: 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 }\n\n private formatInput(contents: any): Array<{ role: string; content: string }> {\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 if (item && typeof item === 'object') {\n if (item.text) {\n return { role: item.role || 'user', content: item.text }\n }\n if (item.content) {\n return { role: item.role || 'user', content: item.content }\n }\n }\n return { role: 'user', content: String(item) }\n })\n }\n\n if (contents && typeof contents === 'object') {\n if (contents.text) {\n return [{ role: 'user', content: contents.text }]\n }\n if (contents.content) {\n return [{ role: 'user', content: contents.content }]\n }\n }\n\n return [{ role: 'user', content: String(contents) }]\n }\n\n private formatOutput(response: GenerateContentResponse): Array<{ role: string; content: string }> {\n if (response.text) {\n return [{ role: 'assistant', content: response.text }]\n }\n\n if (response.candidates && Array.isArray(response.candidates)) {\n return response.candidates.map((candidate) => {\n if (candidate.content && candidate.content.parts) {\n const text = candidate.content.parts\n .filter((part: any) => part.text)\n .map((part: any) => part.text)\n .join('')\n return { role: 'assistant', content: text }\n }\n return { role: 'assistant', content: String(candidate) }\n })\n }\n\n return []\n }\n}\n\nexport default PostHogGoogleGenAI\nexport { PostHogGoogleGenAI as Gemini }\n","'use strict';\nmodule.exports = function (str, sep) {\n\tif (typeof str !== 'string') {\n\t\tthrow new TypeError('Expected a string');\n\t}\n\n\tsep = typeof sep === 'undefined' ? '_' : sep;\n\n\treturn str\n\t\t.replace(/([a-z\\d])([A-Z])/g, '$1' + sep + '$2')\n\t\t.replace(/([A-Z]+)([A-Z][a-z\\d]+)/g, '$1' + sep + '$2')\n\t\t.toLowerCase();\n};\n","'use strict';\n\nconst UPPERCASE = /[\\p{Lu}]/u;\nconst LOWERCASE = /[\\p{Ll}]/u;\nconst LEADING_CAPITAL = /^[\\p{Lu}](?![\\p{Lu}])/gu;\nconst IDENTIFIER = /([\\p{Alpha}\\p{N}_]|$)/u;\nconst SEPARATORS = /[_.\\- ]+/;\n\nconst LEADING_SEPARATORS = new RegExp('^' + SEPARATORS.source);\nconst SEPARATORS_AND_IDENTIFIER = new RegExp(SEPARATORS.source + IDENTIFIER.source, 'gu');\nconst NUMBERS_AND_IDENTIFIER = new RegExp('\\\\d+' + IDENTIFIER.source, 'gu');\n\nconst preserveCamelCase = (string, toLowerCase, toUpperCase) => {\n\tlet isLastCharLower = false;\n\tlet isLastCharUpper = false;\n\tlet isLastLastCharUpper = false;\n\n\tfor (let i = 0; i < string.length; i++) {\n\t\tconst character = string[i];\n\n\t\tif (isLastCharLower && UPPERCASE.test(character)) {\n\t\t\tstring = string.slice(0, i) + '-' + string.slice(i);\n\t\t\tisLastCharLower = false;\n\t\t\tisLastLastCharUpper = isLastCharUpper;\n\t\t\tisLastCharUpper = true;\n\t\t\ti++;\n\t\t} else if (isLastCharUpper && isLastLastCharUpper && LOWERCASE.test(character)) {\n\t\t\tstring = string.slice(0, i - 1) + '-' + string.slice(i - 1);\n\t\t\tisLastLastCharUpper = isLastCharUpper;\n\t\t\tisLastCharUpper = false;\n\t\t\tisLastCharLower = true;\n\t\t} else {\n\t\t\tisLastCharLower = toLowerCase(character) === character && toUpperCase(character) !== character;\n\t\t\tisLastLastCharUpper = isLastCharUpper;\n\t\t\tisLastCharUpper = toUpperCase(character) === character && toLowerCase(character) !== character;\n\t\t}\n\t}\n\n\treturn string;\n};\n\nconst preserveConsecutiveUppercase = (input, toLowerCase) => {\n\tLEADING_CAPITAL.lastIndex = 0;\n\n\treturn input.replace(LEADING_CAPITAL, m1 => toLowerCase(m1));\n};\n\nconst postProcess = (input, toUpperCase) => {\n\tSEPARATORS_AND_IDENTIFIER.lastIndex = 0;\n\tNUMBERS_AND_IDENTIFIER.lastIndex = 0;\n\n\treturn input.replace(SEPARATORS_AND_IDENTIFIER, (_, identifier) => toUpperCase(identifier))\n\t\t.replace(NUMBERS_AND_IDENTIFIER, m => toUpperCase(m));\n};\n\nconst camelCase = (input, options) => {\n\tif (!(typeof input === 'string' || Array.isArray(input))) {\n\t\tthrow new TypeError('Expected the input to be `string | string[]`');\n\t}\n\n\toptions = {\n\t\tpascalCase: false,\n\t\tpreserveConsecutiveUppercase: false,\n\t\t...options\n\t};\n\n\tif (Array.isArray(input)) {\n\t\tinput = input.map(x => x.trim())\n\t\t\t.filter(x => x.length)\n\t\t\t.join('-');\n\t} else {\n\t\tinput = input.trim();\n\t}\n\n\tif (input.length === 0) {\n\t\treturn '';\n\t}\n\n\tconst toLowerCase = options.locale === false ?\n\t\tstring => string.toLowerCase() :\n\t\tstring => string.toLocaleLowerCase(options.locale);\n\tconst toUpperCase = options.locale === false ?\n\t\tstring => string.toUpperCase() :\n\t\tstring => string.toLocaleUpperCase(options.locale);\n\n\tif (input.length === 1) {\n\t\treturn options.pascalCase ? toUpperCase(input) : toLowerCase(input);\n\t}\n\n\tconst hasUpperCase = input !== toLowerCase(input);\n\n\tif (hasUpperCase) {\n\t\tinput = preserveCamelCase(input, toLowerCase, toUpperCase);\n\t}\n\n\tinput = input.replace(LEADING_SEPARATORS, '');\n\n\tif (options.preserveConsecutiveUppercase) {\n\t\tinput = preserveConsecutiveUppercase(input, toLowerCase);\n\t} else {\n\t\tinput = toLowerCase(input);\n\t}\n\n\tif (options.pascalCase) {\n\t\tinput = toUpperCase(input.charAt(0)) + input.slice(1);\n\t}\n\n\treturn postProcess(input, toUpperCase);\n};\n\nmodule.exports = camelCase;\n// TODO: Remove this for the next major release\nmodule.exports.default = camelCase;\n","import snakeCase from \"decamelize\";\nimport camelCase from \"camelcase\";\nexport function keyToJson(key, map) {\n return map?.[key] || snakeCase(key);\n}\nexport function keyFromJson(key, map) {\n return map?.[key] || camelCase(key);\n}\nexport function mapKeys(fields, mapper, map) {\n const mapped = {};\n for (const key in fields) {\n if (Object.hasOwn(fields, key)) {\n mapped[mapper(key, map)] = fields[key];\n }\n }\n return mapped;\n}\n","import { keyToJson, mapKeys } from \"./map_keys.js\";\nfunction shallowCopy(obj) {\n return Array.isArray(obj) ? [...obj] : { ...obj };\n}\nfunction replaceSecrets(root, secretsMap) {\n const result = shallowCopy(root);\n for (const [path, secretId] of Object.entries(secretsMap)) {\n const [last, ...partsReverse] = path.split(\".\").reverse();\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let current = result;\n for (const part of partsReverse.reverse()) {\n if (current[part] === undefined) {\n break;\n }\n current[part] = shallowCopy(current[part]);\n current = current[part];\n }\n if (current[last] !== undefined) {\n current[last] = {\n lc: 1,\n type: \"secret\",\n id: [secretId],\n };\n }\n }\n return result;\n}\n/**\n * Get a unique name for the module, rather than parent class implementations.\n * Should not be subclassed, subclass lc_name above instead.\n */\nexport function get_lc_unique_name(\n// eslint-disable-next-line @typescript-eslint/no-use-before-define\nserializableClass) {\n // \"super\" here would refer to the parent class of Serializable,\n // when we want the parent class of the module actually calling this method.\n const parentClass = Object.getPrototypeOf(serializableClass);\n const lcNameIsSubclassed = typeof serializableClass.lc_name === \"function\" &&\n (typeof parentClass.lc_name !== \"function\" ||\n serializableClass.lc_name() !== parentClass.lc_name());\n if (lcNameIsSubclassed) {\n return serializableClass.lc_name();\n }\n else {\n return serializableClass.name;\n }\n}\nexport class Serializable {\n /**\n * The name of the serializable. Override to provide an alias or\n * to preserve the serialized module name in minified environments.\n *\n * Implemented as a static method to support loading logic.\n */\n static lc_name() {\n return this.name;\n }\n /**\n * The final serialized identifier for the module.\n */\n get lc_id() {\n return [\n ...this.lc_namespace,\n get_lc_unique_name(this.constructor),\n ];\n }\n /**\n * A map of secrets, which will be omitted from serialization.\n * Keys are paths to the secret in constructor args, e.g. \"foo.bar.baz\".\n * Values are the secret ids, which will be used when deserializing.\n */\n get lc_secrets() {\n return undefined;\n }\n /**\n * A map of additional attributes to merge with constructor args.\n * Keys are the attribute names, e.g. \"foo\".\n * Values are the attribute values, which will be serialized.\n * These attributes need to be accepted by the constructor as arguments.\n */\n get lc_attributes() {\n return undefined;\n }\n /**\n * A map of aliases for constructor args.\n * Keys are the attribute names, e.g. \"foo\".\n * Values are the alias that will replace the key in serialization.\n * This is used to eg. make argument names match Python.\n */\n get lc_aliases() {\n return undefined;\n }\n constructor(kwargs, ..._args) {\n Object.defineProperty(this, \"lc_serializable\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"lc_kwargs\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: void 0\n });\n this.lc_kwargs = kwargs || {};\n }\n toJSON() {\n if (!this.lc_serializable) {\n return this.toJSONNotImplemented();\n }\n if (\n // eslint-disable-next-line no-instanceof/no-instanceof\n this.lc_kwargs instanceof Serializable ||\n typeof this.lc_kwargs !== \"object\" ||\n Array.isArray(this.lc_kwargs)) {\n // We do not support serialization of classes with arg not a POJO\n // I'm aware the check above isn't as strict as it could be\n return this.toJSONNotImplemented();\n }\n const aliases = {};\n const secrets = {};\n const kwargs = Object.keys(this.lc_kwargs).reduce((acc, key) => {\n acc[key] = key in this ? this[key] : this.lc_kwargs[key];\n return acc;\n }, {});\n // get secrets, attributes and aliases from all superclasses\n for (\n // eslint-disable-next-line @typescript-eslint/no-this-alias\n let current = Object.getPrototypeOf(this); current; current = Object.getPrototypeOf(current)) {\n Object.assign(aliases, Reflect.get(current, \"lc_aliases\", this));\n Object.assign(secrets, Reflect.get(current, \"lc_secrets\", this));\n Object.assign(kwargs, Reflect.get(current, \"lc_attributes\", this));\n }\n // include all secrets used, even if not in kwargs,\n // will be replaced with sentinel value in replaceSecrets\n Object.keys(secrets).forEach((keyPath) => {\n // eslint-disable-next-line @typescript-eslint/no-this-alias, @typescript-eslint/no-explicit-any\n let read = this;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let write = kwargs;\n const [last, ...partsReverse] = keyPath.split(\".\").reverse();\n for (const key of partsReverse.reverse()) {\n if (!(key in read) || read[key] === undefined)\n return;\n if (!(key in write) || write[key] === undefined) {\n if (typeof read[key] === \"object\" && read[key] != null) {\n write[key] = {};\n }\n else if (Array.isArray(read[key])) {\n write[key] = [];\n }\n }\n read = read[key];\n write = write[key];\n }\n if (last in read && read[last] !== undefined) {\n write[last] = write[last] || read[last];\n }\n });\n return {\n lc: 1,\n type: \"constructor\",\n id: this.lc_id,\n kwargs: mapKeys(Object.keys(secrets).length ? replaceSecrets(kwargs, secrets) : kwargs, keyToJson, aliases),\n };\n }\n toJSONNotImplemented() {\n return {\n lc: 1,\n type: \"not_implemented\",\n id: this.lc_id,\n };\n }\n}\n","export const isBrowser = () => typeof window !== \"undefined\" && typeof window.document !== \"undefined\";\nexport const isWebWorker = () => typeof globalThis === \"object\" &&\n globalThis.constructor &&\n globalThis.constructor.name === \"DedicatedWorkerGlobalScope\";\nexport const isJsDom = () => (typeof window !== \"undefined\" && window.name === \"nodejs\") ||\n (typeof navigator !== \"undefined\" &&\n (navigator.userAgent.includes(\"Node.js\") ||\n navigator.userAgent.includes(\"jsdom\")));\n// Supabase Edge Function provides a `Deno` global object\n// without `version` property\nexport const isDeno = () => typeof Deno !== \"undefined\";\n// Mark not-as-node if in Supabase Edge Function\nexport const isNode = () => typeof process !== \"undefined\" &&\n typeof process.versions !== \"undefined\" &&\n typeof process.versions.node !== \"undefined\" &&\n !isDeno();\nexport const getEnv = () => {\n let env;\n if (isBrowser()) {\n env = \"browser\";\n }\n else if (isNode()) {\n env = \"node\";\n }\n else if (isWebWorker()) {\n env = \"webworker\";\n }\n else if (isJsDom()) {\n env = \"jsdom\";\n }\n else if (isDeno()) {\n env = \"deno\";\n }\n else {\n env = \"other\";\n }\n return env;\n};\nlet runtimeEnvironment;\nexport async function getRuntimeEnvironment() {\n if (runtimeEnvironment === undefined) {\n const env = getEnv();\n runtimeEnvironment = {\n library: \"langchain-js\",\n runtime: env,\n };\n }\n return runtimeEnvironment;\n}\nexport function getEnvironmentVariable(name) {\n // Certain Deno setups will throw an error if you try to access environment variables\n // https://github.com/langchain-ai/langchainjs/issues/1412\n try {\n if (typeof process !== \"undefined\") {\n // eslint-disable-next-line no-process-env\n return process.env?.[name];\n }\n else if (isDeno()) {\n return Deno?.env.get(name);\n }\n else {\n return undefined;\n }\n }\n catch (e) {\n return undefined;\n }\n}\n","import * as uuid from \"uuid\";\nimport { Serializable, get_lc_unique_name, } from \"../load/serializable.js\";\nimport { getEnvironmentVariable } from \"../utils/env.js\";\n/**\n * Abstract class that provides a set of optional methods that can be\n * overridden in derived classes to handle various events during the\n * execution of a LangChain application.\n */\nclass BaseCallbackHandlerMethodsClass {\n}\nexport function callbackHandlerPrefersStreaming(x) {\n return \"lc_prefer_streaming\" in x && x.lc_prefer_streaming;\n}\n/**\n * Abstract base class for creating callback handlers in the LangChain\n * framework. It provides a set of optional methods that can be overridden\n * in derived classes to handle various events during the execution of a\n * LangChain application.\n */\nexport class BaseCallbackHandler extends BaseCallbackHandlerMethodsClass {\n get lc_namespace() {\n return [\"langchain_core\", \"callbacks\", this.name];\n }\n get lc_secrets() {\n return undefined;\n }\n get lc_attributes() {\n return undefined;\n }\n get lc_aliases() {\n return undefined;\n }\n /**\n * The name of the serializable. Override to provide an alias or\n * to preserve the serialized module name in minified environments.\n *\n * Implemented as a static method to support loading logic.\n */\n static lc_name() {\n return this.name;\n }\n /**\n * The final serialized identifier for the module.\n */\n get lc_id() {\n return [\n ...this.lc_namespace,\n get_lc_unique_name(this.constructor),\n ];\n }\n constructor(input) {\n super();\n Object.defineProperty(this, \"lc_serializable\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"lc_kwargs\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: void 0\n });\n Object.defineProperty(this, \"ignoreLLM\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"ignoreChain\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"ignoreAgent\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"ignoreRetriever\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"ignoreCustomEvent\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"raiseError\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: false\n });\n Object.defineProperty(this, \"awaitHandlers\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: getEnvironmentVariable(\"LANGCHAIN_CALLBACKS_BACKGROUND\") === \"false\"\n });\n this.lc_kwargs = input || {};\n if (input) {\n this.ignoreLLM = input.ignoreLLM ?? this.ignoreLLM;\n this.ignoreChain = input.ignoreChain ?? this.ignoreChain;\n this.ignoreAgent = input.ignoreAgent ?? this.ignoreAgent;\n this.ignoreRetriever = input.ignoreRetriever ?? this.ignoreRetriever;\n this.ignoreCustomEvent =\n input.ignoreCustomEvent ?? this.ignoreCustomEvent;\n this.raiseError = input.raiseError ?? this.raiseError;\n this.awaitHandlers =\n this.raiseError || (input._awaitHandler ?? this.awaitHandlers);\n }\n }\n copy() {\n return new this.constructor(this);\n }\n toJSON() {\n return Serializable.prototype.toJSON.call(this);\n }\n toJSONNotImplemented() {\n return Serializable.prototype.toJSONNotImplemented.call(this);\n }\n static fromMethods(methods) {\n class Handler extends BaseCallbackHandler {\n constructor() {\n super();\n Object.defineProperty(this, \"name\", {\n enumerable: true,\n configurable: true,\n writable: true,\n value: uuid.v4()\n });\n Object.assign(this, methods);\n }\n }\n return new Handler();\n }\n}\nexport const isBaseCallbackHandler = (x) => {\n const callbackHandler = x;\n return (callbackHandler !== undefined &&\n typeof callbackHandler.copy === \"function\" &&\n typeof callbackHandler.name === \"string\" &&\n typeof callbackHandler.awaitHandlers === \"boolean\");\n};\n","import { PostHog } from 'posthog-node'\nimport { withPrivacyMode, getModelParams } from '../utils'\nimport { BaseCallbackHandler } from '@langchain/core/callbacks/base'\nimport type { Serialized } from '@langchain/core/load/serializable'\nimport type { ChainValues } from '@langchain/core/utils/types'\nimport type { BaseMessage } from '@langchain/core/messages'\nimport type { LLMResult } from '@langchain/core/outputs'\nimport type { AgentAction, AgentFinish } from '@langchain/core/agents'\nimport type { DocumentInterface } from '@langchain/core/documents'\n\ninterface SpanMetadata {\n /** Name of the trace/span (e.g. chain name) */\n name: string\n /** Timestamp (in ms) when the run started */\n startTime: number\n /** Timestamp (in ms) when the run ended (if already finished) */\n endTime?: number\n /** The input state */\n input?: any\n}\n\ninterface GenerationMetadata extends SpanMetadata {\n /** Provider used (e.g. openai, anthropic) */\n provider?: string\n /** Model name used in the generation */\n model?: string\n /** The model parameters (temperature, max_tokens, etc.) */\n modelParams?: Record\n /** The base URL—for example, the API base used */\n baseUrl?: string\n /** The tools used in the generation */\n tools?: Record\n}\n\n/** A run may either be a Span or a Generation */\ntype RunMetadata = SpanMetadata | GenerationMetadata\n\n/** Storage for run metadata */\ntype RunMetadataStorage = { [runId: string]: RunMetadata }\n\nexport class LangChainCallbackHandler extends BaseCallbackHandler {\n public name = 'PosthogCallbackHandler'\n private client: PostHog\n private distinctId?: string | number\n private traceId?: string | number\n private properties: Record\n private privacyMode: boolean\n private groups: Record\n private debug: boolean\n\n private runs: RunMetadataStorage = {}\n private parentTree: { [runId: string]: string } = {}\n\n constructor(options: {\n client: PostHog\n distinctId?: string | number\n traceId?: string | number\n properties?: Record\n privacyMode?: boolean\n groups?: Record\n debug?: boolean\n }) {\n if (!options.client) {\n throw new Error('PostHog client is required')\n }\n super()\n this.client = options.client\n this.distinctId = options.distinctId\n this.traceId = options.traceId\n this.properties = options.properties || {}\n this.privacyMode = options.privacyMode || false\n this.groups = options.groups || {}\n this.debug = options.debug || false\n }\n\n // ===== CALLBACK METHODS =====\n\n public handleChainStart(\n chain: Serialized,\n inputs: ChainValues,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n metadata?: Record,\n runType?: string,\n runName?: string\n ): void {\n this._logDebugEvent('on_chain_start', runId, parentRunId, { inputs, tags })\n this._setParentOfRun(runId, parentRunId)\n this._setTraceOrSpanMetadata(chain, inputs, runId, parentRunId, metadata, tags, runName)\n }\n\n public handleChainEnd(\n outputs: ChainValues,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n kwargs?: { inputs?: Record }\n ): void {\n this._logDebugEvent('on_chain_end', runId, parentRunId, { outputs, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, outputs)\n }\n\n public handleChainError(\n error: Error,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n kwargs?: { inputs?: Record }\n ): void {\n this._logDebugEvent('on_chain_error', runId, parentRunId, { error, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, error)\n }\n\n public handleChatModelStart(\n serialized: Serialized,\n messages: BaseMessage[][],\n runId: string,\n parentRunId?: string,\n extraParams?: Record,\n tags?: string[],\n metadata?: Record,\n runName?: string\n ): void {\n this._logDebugEvent('on_chat_model_start', runId, parentRunId, { messages, tags })\n this._setParentOfRun(runId, parentRunId)\n // Flatten the two-dimensional messages and convert each message to a plain object\n const input = messages.flat().map((m) => this._convertMessageToDict(m))\n this._setLLMMetadata(serialized, runId, input, metadata, extraParams, runName)\n }\n\n public handleLLMStart(\n serialized: Serialized,\n prompts: string[],\n runId: string,\n parentRunId?: string,\n extraParams?: Record,\n tags?: string[],\n metadata?: Record,\n runName?: string\n ): void {\n this._logDebugEvent('on_llm_start', runId, parentRunId, { prompts, tags })\n this._setParentOfRun(runId, parentRunId)\n this._setLLMMetadata(serialized, runId, prompts, metadata, extraParams, runName)\n }\n\n public handleLLMEnd(\n output: LLMResult,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n extraParams?: Record\n ): void {\n this._logDebugEvent('on_llm_end', runId, parentRunId, { output, tags })\n this._popRunAndCaptureGeneration(runId, parentRunId, output)\n }\n\n public handleLLMError(\n err: Error,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n extraParams?: Record\n ): void {\n this._logDebugEvent('on_llm_error', runId, parentRunId, { err, tags })\n this._popRunAndCaptureGeneration(runId, parentRunId, err)\n }\n\n public handleToolStart(\n tool: Serialized,\n input: string,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n metadata?: Record,\n runName?: string\n ): void {\n this._logDebugEvent('on_tool_start', runId, parentRunId, { input, tags })\n this._setParentOfRun(runId, parentRunId)\n this._setTraceOrSpanMetadata(tool, input, runId, parentRunId, metadata, tags, runName)\n }\n\n public handleToolEnd(output: any, runId: string, parentRunId?: string, tags?: string[]): void {\n this._logDebugEvent('on_tool_end', runId, parentRunId, { output, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, output)\n }\n\n public handleToolError(err: Error, runId: string, parentRunId?: string, tags?: string[]): void {\n this._logDebugEvent('on_tool_error', runId, parentRunId, { err, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, err)\n }\n\n public handleRetrieverStart(\n retriever: Serialized,\n query: string,\n runId: string,\n parentRunId?: string,\n tags?: string[],\n metadata?: Record,\n name?: string\n ): void {\n this._logDebugEvent('on_retriever_start', runId, parentRunId, { query, tags })\n this._setParentOfRun(runId, parentRunId)\n this._setTraceOrSpanMetadata(retriever, query, runId, parentRunId, metadata, tags, name)\n }\n\n public handleRetrieverEnd(\n documents: DocumentInterface[],\n runId: string,\n parentRunId?: string,\n tags?: string[]\n ): void {\n this._logDebugEvent('on_retriever_end', runId, parentRunId, { documents, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, documents)\n }\n\n public handleRetrieverError(err: Error, runId: string, parentRunId?: string, tags?: string[]): void {\n this._logDebugEvent('on_retriever_error', runId, parentRunId, { err, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, err)\n }\n\n public handleAgentAction(action: AgentAction, runId: string, parentRunId?: string, tags?: string[]): void {\n this._logDebugEvent('on_agent_action', runId, parentRunId, { action, tags })\n this._setParentOfRun(runId, parentRunId)\n this._setTraceOrSpanMetadata(null, action, runId, parentRunId)\n }\n\n public handleAgentEnd(action: AgentFinish, runId: string, parentRunId?: string, tags?: string[]): void {\n this._logDebugEvent('on_agent_finish', runId, parentRunId, { action, tags })\n this._popRunAndCaptureTraceOrSpan(runId, parentRunId, action)\n }\n\n // ===== PRIVATE HELPERS =====\n\n private _setParentOfRun(runId: string, parentRunId?: string): void {\n if (parentRunId) {\n this.parentTree[runId] = parentRunId\n }\n }\n\n private _popParentOfRun(runId: string): void {\n delete this.parentTree[runId]\n }\n\n private _findRootRun(runId: string): string {\n let id = runId\n while (this.parentTree[id]) {\n id = this.parentTree[id]\n }\n return id\n }\n\n private _setTraceOrSpanMetadata(\n serialized: any,\n input: any,\n runId: string,\n parentRunId?: string,\n ...args: any[]\n ): void {\n // Use default names if not provided: if this is a top-level run, we mark it as a trace, otherwise as a span.\n const defaultName = parentRunId ? 'span' : 'trace'\n const runName = this._getLangchainRunName(serialized, ...args) || defaultName\n this.runs[runId] = {\n name: runName,\n input,\n startTime: Date.now(),\n } as SpanMetadata\n }\n\n private _setLLMMetadata(\n serialized: Serialized | null,\n runId: string,\n messages: any,\n metadata?: any,\n extraParams?: any,\n runName?: string\n ): void {\n const runNameFound = this._getLangchainRunName(serialized, { extraParams, runName }) || 'generation'\n const generation: GenerationMetadata = {\n name: runNameFound,\n input: messages,\n startTime: Date.now(),\n }\n if (extraParams) {\n generation.modelParams = getModelParams(extraParams.invocation_params)\n }\n if (metadata) {\n if (metadata.ls_model_name) {\n generation.model = metadata.ls_model_name\n }\n if (metadata.ls_provider) {\n generation.provider = metadata.ls_provider\n }\n }\n if (serialized && 'kwargs' in serialized && serialized.kwargs.openai_api_base) {\n generation.baseUrl = serialized.kwargs.openai_api_base\n }\n this.runs[runId] = generation\n }\n\n private _popRunMetadata(runId: string): RunMetadata | undefined {\n const endTime = Date.now()\n const run = this.runs[runId]\n if (!run) {\n console.warn(`No run metadata found for run ${runId}`)\n return undefined\n }\n run.endTime = endTime\n delete this.runs[runId]\n return run\n }\n\n private _getTraceId(runId: string): string {\n return this.traceId ? String(this.traceId) : this._findRootRun(runId)\n }\n\n private _getParentRunId(traceId: string, runId: string, parentRunId?: string): string | undefined {\n // Replace the parent-run if not found in our stored parent tree.\n if (parentRunId && !this.parentTree[parentRunId]) {\n return traceId\n }\n return parentRunId\n }\n\n private _popRunAndCaptureTraceOrSpan(\n runId: string,\n parentRunId: string | undefined,\n outputs: ChainValues | DocumentInterface[] | AgentFinish | Error | any\n ): void {\n const traceId = this._getTraceId(runId)\n this._popParentOfRun(runId)\n const run = this._popRunMetadata(runId)\n if (!run) {\n return\n }\n if ('modelParams' in run) {\n console.warn(`Run ${runId} is a generation, but attempted to be captured as a trace/span.`)\n return\n }\n const actualParentRunId = this._getParentRunId(traceId, runId, parentRunId)\n this._captureTraceOrSpan(traceId, runId, run as SpanMetadata, outputs, actualParentRunId)\n }\n\n private _captureTraceOrSpan(\n traceId: string,\n runId: string,\n run: SpanMetadata,\n outputs: ChainValues | DocumentInterface[] | AgentFinish | Error | any,\n parentRunId?: string\n ): void {\n const eventName = parentRunId ? '$ai_span' : '$ai_trace'\n const latency = run.endTime ? (run.endTime - run.startTime) / 1000 : 0\n const eventProperties: Record = {\n $ai_trace_id: traceId,\n $ai_input_state: withPrivacyMode(this.client, this.privacyMode, run.input),\n $ai_latency: latency,\n $ai_span_name: run.name,\n $ai_span_id: runId,\n }\n if (parentRunId) {\n eventProperties['$ai_parent_id'] = parentRunId\n }\n\n Object.assign(eventProperties, this.properties)\n if (!this.distinctId) {\n eventProperties['$process_person_profile'] = false\n }\n if (outputs instanceof Error) {\n eventProperties['$ai_error'] = outputs.toString()\n eventProperties['$ai_is_error'] = true\n } else if (outputs !== undefined) {\n eventProperties['$ai_output_state'] = withPrivacyMode(this.client, this.privacyMode, outputs)\n }\n this.client.capture({\n distinctId: this.distinctId ? this.distinctId.toString() : runId,\n event: eventName,\n properties: eventProperties,\n groups: this.groups,\n })\n }\n\n private _popRunAndCaptureGeneration(\n runId: string,\n parentRunId: string | undefined,\n response: LLMResult | Error\n ): void {\n const traceId = this._getTraceId(runId)\n this._popParentOfRun(runId)\n const run = this._popRunMetadata(runId)\n if (!run || typeof run !== 'object' || !('modelParams' in run)) {\n console.warn(`Run ${runId} is not a generation, but attempted to be captured as such.`)\n return\n }\n const actualParentRunId = this._getParentRunId(traceId, runId, parentRunId)\n this._captureGeneration(traceId, runId, run as GenerationMetadata, response, actualParentRunId)\n }\n\n private _captureGeneration(\n traceId: string,\n runId: string,\n run: GenerationMetadata,\n output: LLMResult | Error,\n parentRunId?: string\n ): void {\n const latency = run.endTime ? (run.endTime - run.startTime) / 1000 : 0\n const eventProperties: Record = {\n $ai_trace_id: traceId,\n $ai_span_id: runId,\n $ai_span_name: run.name,\n $ai_parent_id: parentRunId,\n $ai_provider: run.provider,\n $ai_model: run.model,\n $ai_model_parameters: run.modelParams,\n $ai_input: withPrivacyMode(this.client, this.privacyMode, run.input),\n $ai_http_status: 200,\n $ai_latency: latency,\n $ai_base_url: run.baseUrl,\n }\n\n if (run.tools) {\n eventProperties['$ai_tools'] = withPrivacyMode(this.client, this.privacyMode, run.tools)\n }\n\n if (output instanceof Error) {\n eventProperties['$ai_http_status'] = (output as any).status || 500\n eventProperties['$ai_error'] = output.toString()\n eventProperties['$ai_is_error'] = true\n } else {\n // Handle token usage\n const [inputTokens, outputTokens, additionalTokenData] = this.parseUsage(output)\n eventProperties['$ai_input_tokens'] = inputTokens\n eventProperties['$ai_output_tokens'] = outputTokens\n\n // Add additional token data to properties\n if (additionalTokenData.cacheReadInputTokens) {\n eventProperties['$ai_cache_read_tokens'] = additionalTokenData.cacheReadInputTokens\n }\n if (additionalTokenData.reasoningTokens) {\n eventProperties['$ai_reasoning_tokens'] = additionalTokenData.reasoningTokens\n }\n\n // Handle generations/completions\n let completions\n if (output.generations && Array.isArray(output.generations)) {\n const lastGeneration = output.generations[output.generations.length - 1]\n if (Array.isArray(lastGeneration)) {\n completions = lastGeneration.map((gen) => {\n return { role: 'assistant', content: gen.text }\n })\n }\n }\n\n if (completions) {\n eventProperties['$ai_output_choices'] = withPrivacyMode(this.client, this.privacyMode, completions)\n }\n }\n\n Object.assign(eventProperties, this.properties)\n if (!this.distinctId) {\n eventProperties['$process_person_profile'] = false\n }\n\n this.client.capture({\n distinctId: this.distinctId ? this.distinctId.toString() : traceId,\n event: '$ai_generation',\n properties: eventProperties,\n groups: this.groups,\n })\n }\n\n private _logDebugEvent(eventName: string, runId: string, parentRunId: string | undefined, extra: any): void {\n if (this.debug) {\n console.log(`Event: ${eventName}, runId: ${runId}, parentRunId: ${parentRunId}, extra:`, extra)\n }\n }\n\n private _getLangchainRunName(serialized: any, ...args: any): string | undefined {\n if (args && args.length > 0) {\n for (const arg of args) {\n if (arg && typeof arg === 'object' && 'name' in arg) {\n return arg.name\n } else if (arg && typeof arg === 'object' && 'runName' in arg) {\n return arg.runName\n }\n }\n }\n\n if (serialized && serialized.name) {\n return serialized.name\n }\n if (serialized && serialized.id) {\n return Array.isArray(serialized.id) ? serialized.id[serialized.id.length - 1] : serialized.id\n }\n return undefined\n }\n\n private _convertMessageToDict(message: any): Record {\n let messageDict: Record = {}\n\n // Check the _getType() method or type property instead of instanceof\n const messageType = message._getType?.() || message.type\n\n switch (messageType) {\n case 'human':\n messageDict = { role: 'user', content: message.content }\n break\n case 'ai':\n messageDict = { role: 'assistant', content: message.content }\n break\n case 'system':\n messageDict = { role: 'system', content: message.content }\n break\n case 'tool':\n messageDict = { role: 'tool', content: message.content }\n break\n case 'function':\n messageDict = { role: 'function', content: message.content }\n break\n default:\n messageDict = { role: messageType || 'unknown', content: String(message.content) }\n }\n\n if (message.additional_kwargs) {\n messageDict = { ...messageDict, ...message.additional_kwargs }\n }\n return messageDict\n }\n\n private _parseUsageModel(usage: any): [number, number, Record] {\n const conversionList: Array<[string, 'input' | 'output']> = [\n ['promptTokens', 'input'],\n ['completionTokens', 'output'],\n ['input_tokens', 'input'],\n ['output_tokens', 'output'],\n ['prompt_token_count', 'input'],\n ['candidates_token_count', 'output'],\n ['inputTokenCount', 'input'],\n ['outputTokenCount', 'output'],\n ['input_token_count', 'input'],\n ['generated_token_count', 'output'],\n ]\n\n const parsedUsage = conversionList.reduce(\n (acc: { input: number; output: number }, [modelKey, typeKey]) => {\n const value = usage[modelKey]\n if (value != null) {\n const finalCount = Array.isArray(value)\n ? value.reduce((sum: number, tokenCount: number) => sum + tokenCount, 0)\n : value\n acc[typeKey] = finalCount\n }\n return acc\n },\n { input: 0, output: 0 }\n )\n\n // Extract additional token details like cached tokens and reasoning tokens\n const additionalTokenData: Record = {}\n\n // Check for cached tokens in various formats\n if (usage.prompt_tokens_details?.cached_tokens != null) {\n additionalTokenData.cacheReadInputTokens = usage.prompt_tokens_details.cached_tokens\n } else if (usage.input_token_details?.cache_read != null) {\n additionalTokenData.cacheReadInputTokens = usage.input_token_details.cache_read\n } else if (usage.cachedPromptTokens != null) {\n additionalTokenData.cacheReadInputTokens = usage.cachedPromptTokens\n }\n\n // Check for reasoning tokens in various formats\n if (usage.completion_tokens_details?.reasoning_tokens != null) {\n additionalTokenData.reasoningTokens = usage.completion_tokens_details.reasoning_tokens\n } else if (usage.output_token_details?.reasoning != null) {\n additionalTokenData.reasoningTokens = usage.output_token_details.reasoning\n } else if (usage.reasoningTokens != null) {\n additionalTokenData.reasoningTokens = usage.reasoningTokens\n }\n\n return [parsedUsage.input, parsedUsage.output, additionalTokenData]\n }\n\n private parseUsage(response: LLMResult): [number, number, Record] {\n let llmUsage: [number, number, Record] = [0, 0, {}]\n const llmUsageKeys = ['token_usage', 'usage', 'tokenUsage']\n\n if 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