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* Copyright The OpenTelemetry Authors\n * SPDX-License-Identifier: Apache-2.0\n */\n\n// avoids a dependency on @opentelemetry/core for hrTime utilities\nimport { performance } from 'perf_hooks';\n\nimport { context, SpanKind, SpanStatusCode, trace } from '@opentelemetry/api';\nimport type { Attributes, Context, Histogram, Span } from '@opentelemetry/api';\nimport {\n InstrumentationBase,\n InstrumentationNodeModuleDefinition,\n} from '@opentelemetry/instrumentation';\nimport { SeverityNumber } from '@opentelemetry/api-logs';\nimport type {\n ChatCompletion,\n ChatCompletionMessageToolCall,\n ChatCompletionContentPart,\n ChatCompletionContentPartRefusal,\n ChatCompletionContentPartText,\n ChatCompletionCreateParams,\n ChatCompletionMessageParam,\n ChatCompletionChunk,\n Completions as ChatCompletions,\n} from 'openai/resources/chat/completions';\nimport type {\n CreateEmbeddingResponse,\n Embeddings,\n EmbeddingCreateParams,\n} from 'openai/resources/embeddings';\nimport type { Stream } from 'openai/streaming';\n\nimport {\n ATTR_EVENT_NAME,\n ATTR_GEN_AI_OPERATION_NAME,\n ATTR_GEN_AI_REQUEST_ENCODING_FORMATS,\n ATTR_GEN_AI_REQUEST_FREQUENCY_PENALTY,\n ATTR_GEN_AI_REQUEST_MAX_TOKENS,\n ATTR_GEN_AI_REQUEST_MODEL,\n ATTR_GEN_AI_REQUEST_PRESENCE_PENALTY,\n ATTR_GEN_AI_REQUEST_STOP_SEQUENCES,\n ATTR_GEN_AI_REQUEST_TEMPERATURE,\n ATTR_GEN_AI_REQUEST_TOP_P,\n ATTR_GEN_AI_RESPONSE_FINISH_REASONS,\n ATTR_GEN_AI_RESPONSE_ID,\n ATTR_GEN_AI_RESPONSE_MODEL,\n ATTR_GEN_AI_SYSTEM,\n ATTR_GEN_AI_TOKEN_TYPE,\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n METRIC_GEN_AI_CLIENT_OPERATION_DURATION,\n METRIC_GEN_AI_CLIENT_TOKEN_USAGE,\n} from './semconv';\n/** @knipignore */\nimport { PACKAGE_NAME, PACKAGE_VERSION } from './version';\nimport { getEnvBool, getAttrsFromBaseURL } from './utils';\nimport { OpenAIInstrumentationConfig } from './types';\nimport {\n APIPromise,\n GenAIMessage,\n GenAIChoiceEventBody,\n GenAISystemMessageEventBody,\n GenAIUserMessageEventBody,\n GenAIAssistantMessageEventBody,\n GenAIToolMessageEventBody,\n GenAIToolCall,\n} from './internal-types';\n\n// The JS semconv package doesn't yet emit constants for event names.\n// TODO: otel-js issue for semconv pkg not including event names\nconst EVENT_GEN_AI_SYSTEM_MESSAGE = 'gen_ai.system.message';\nconst EVENT_GEN_AI_USER_MESSAGE = 'gen_ai.user.message';\nconst EVENT_GEN_AI_ASSISTANT_MESSAGE = 'gen_ai.assistant.message';\nconst EVENT_GEN_AI_TOOL_MESSAGE = 'gen_ai.tool.message';\nconst EVENT_GEN_AI_CHOICE = 'gen_ai.choice';\n\nexport class OpenAIInstrumentation extends InstrumentationBase {\n private _genaiClientOperationDuration!: Histogram;\n private _genaiClientTokenUsage!: Histogram;\n\n constructor(config: OpenAIInstrumentationConfig = {}) {\n super(PACKAGE_NAME, PACKAGE_VERSION, config);\n\n // Possible environment variable overrides for config.\n const cfg = this.getConfig();\n const envCC = getEnvBool(\n 'OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT',\n this._diag\n );\n if (envCC !== undefined) {\n cfg.captureMessageContent = envCC;\n }\n }\n\n // Override InstrumentationAbtract.setConfig so we can normalize config.\n override setConfig(config: OpenAIInstrumentationConfig = {}) {\n const { captureMessageContent, ...validConfig } = config;\n (validConfig as OpenAIInstrumentationConfig).captureMessageContent =\n !!captureMessageContent;\n super.setConfig(validConfig);\n }\n\n protected init() {\n return [\n new InstrumentationNodeModuleDefinition(\n 'openai',\n ['>=4.19.0 <7'],\n modExports => {\n this._wrap(\n modExports.OpenAI.Chat.Completions.prototype,\n 'create',\n this._getPatchedChatCompletionsCreate()\n );\n this._wrap(\n modExports.OpenAI.Embeddings.prototype,\n 'create',\n this._getPatchedEmbeddingsCreate()\n );\n\n return modExports;\n },\n modExports => {\n this._unwrap(modExports.OpenAI.Chat.Completions.prototype, 'create');\n this._unwrap(modExports.OpenAI.Embeddings.prototype, 'create');\n }\n ),\n ];\n }\n\n // This is a 'protected' method on class `InstrumentationAbstract`.\n override _updateMetricInstruments() {\n this._genaiClientOperationDuration = this.meter.createHistogram(\n METRIC_GEN_AI_CLIENT_OPERATION_DURATION,\n {\n description: 'GenAI operation duration',\n unit: 's',\n advice: {\n explicitBucketBoundaries: [\n 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24,\n 20.48, 40.96, 81.92,\n ],\n },\n }\n );\n this._genaiClientTokenUsage = this.meter.createHistogram(\n METRIC_GEN_AI_CLIENT_TOKEN_USAGE,\n {\n description: 'Measures number of input and output tokens used',\n unit: '{token}',\n advice: {\n explicitBucketBoundaries: [\n 1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576,\n 4194304, 16777216, 67108864,\n ],\n },\n }\n );\n }\n\n private _getPatchedChatCompletionsCreate(): any {\n const self = this;\n return (original: ChatCompletions['create']) => {\n // https://platform.openai.com/docs/api-reference/chat/create\n return function patchedCreate(\n this: ChatCompletions,\n ...args: Parameters\n ) {\n if (!self.isEnabled) {\n return original.apply(this, args);\n }\n\n self._diag.debug('OpenAI.Chat.Completions.create args: %O', args);\n const params = args[0];\n const config = self.getConfig();\n const startNow = performance.now();\n\n let startInfo;\n try {\n startInfo = self._startChatCompletionsSpan(\n params,\n config,\n this?._client?.baseURL\n );\n } catch (err) {\n self._diag.error('unexpected error starting span:', err);\n return original.apply(this, args);\n }\n const { span, ctx, commonAttrs } = startInfo;\n\n const apiPromise = context.with(ctx, () => original.apply(this, args));\n\n // Streaming.\n if (isStreamPromise(params, apiPromise)) {\n // When streaming, `apiPromise` resolves to `Stream`,\n // an async iterable (i.e. has a `Symbol.asyncIterator` method). We\n // want to wrap that iteration to gather telemetry. Instead of wrapping\n // `Symbol.asyncIterator`, which would be nice, we wrap the `iterator`\n // method because it is used internally by `Stream#tee()`.\n apiPromise\n .then(stream => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n self._wrap(stream as any, 'iterator', origIterator => {\n return () => {\n return self._onChatCompletionsStreamIterator(\n origIterator(),\n span,\n startNow,\n config,\n commonAttrs,\n ctx\n );\n };\n });\n return stream;\n })\n .catch(\n self._createAPIPromiseRejectionHandler(\n startNow,\n span,\n commonAttrs\n )\n );\n // We have to return the original `APIPromise` instance, because it is\n // a customized subclass of `Promise`.\n return apiPromise;\n }\n\n // Non-streaming.\n apiPromise\n .then(result => {\n self._onChatCompletionsCreateResult(\n span,\n startNow,\n commonAttrs,\n result as ChatCompletion,\n config,\n ctx\n );\n })\n .catch(\n self._createAPIPromiseRejectionHandler(startNow, span, commonAttrs)\n );\n\n return apiPromise;\n };\n };\n }\n\n /**\n * Start a span for this chat-completion API call. This also emits log events\n * as appropriate for the request params.\n */\n private _startChatCompletionsSpan(\n params: ChatCompletionCreateParams,\n config: OpenAIInstrumentationConfig,\n baseURL: string | undefined\n ) {\n // Attributes common to span, metrics, log events.\n const commonAttrs: Attributes = {\n [ATTR_GEN_AI_OPERATION_NAME]: 'chat',\n [ATTR_GEN_AI_REQUEST_MODEL]: params.model,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n };\n Object.assign(commonAttrs, getAttrsFromBaseURL(baseURL, this._diag));\n\n // Span attributes.\n const attrs: Attributes = {\n ...commonAttrs,\n };\n if (params.frequency_penalty != null) {\n attrs[ATTR_GEN_AI_REQUEST_FREQUENCY_PENALTY] = params.frequency_penalty;\n }\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n if ((params as any).max_completion_tokens != null) {\n attrs[ATTR_GEN_AI_REQUEST_MAX_TOKENS] =\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n (params as any).max_completion_tokens;\n } else if (params.max_tokens != null) {\n // `max_tokens` is deprecated in favour of `max_completion_tokens`.\n attrs[ATTR_GEN_AI_REQUEST_MAX_TOKENS] = params.max_tokens;\n }\n if (params.presence_penalty != null) {\n attrs[ATTR_GEN_AI_REQUEST_PRESENCE_PENALTY] = params.presence_penalty;\n }\n if (params.stop != null) {\n if (Array.isArray(params.stop)) {\n attrs[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] = params.stop;\n } else {\n attrs[ATTR_GEN_AI_REQUEST_STOP_SEQUENCES] = [params.stop];\n }\n }\n if (params.temperature != null) {\n attrs[ATTR_GEN_AI_REQUEST_TEMPERATURE] = params.temperature;\n }\n if (params.top_p != null) {\n attrs[ATTR_GEN_AI_REQUEST_TOP_P] = params.top_p;\n }\n\n const span: Span = this.tracer.startSpan(\n `${attrs[ATTR_GEN_AI_OPERATION_NAME]} ${attrs[ATTR_GEN_AI_REQUEST_MODEL]}`,\n {\n kind: SpanKind.CLIENT,\n attributes: attrs,\n }\n );\n const ctx: Context = trace.setSpan(context.active(), span);\n\n // Capture prompts as log events.\n const timestamp = Date.now();\n params.messages.forEach((msg: ChatCompletionMessageParam) => {\n switch (msg.role) {\n case 'system': {\n const body: GenAISystemMessageEventBody = {};\n if (config.captureMessageContent) {\n if (Array.isArray(msg.content)) {\n body.content = msg.content.map(p => p.text).join('');\n } else {\n body.content = msg.content;\n }\n }\n this.logger.emit({\n timestamp,\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_SYSTEM_MESSAGE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body,\n });\n break;\n }\n case 'user': {\n const body: GenAIUserMessageEventBody = {};\n if (config.captureMessageContent) {\n if (Array.isArray(msg.content)) {\n body.content = msg.content\n .filter(isTextContent)\n .map(p => p.text)\n .join('');\n } else {\n body.content = msg.content;\n }\n }\n this.logger.emit({\n timestamp,\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_USER_MESSAGE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body,\n });\n break;\n }\n case 'assistant': {\n const body: GenAIAssistantMessageEventBody = {};\n if (config.captureMessageContent) {\n if (msg.content) {\n if (Array.isArray(msg.content)) {\n body.content = msg.content\n .filter(isTextContent)\n .map(p => p.text)\n .join('');\n } else {\n body.content = msg.content;\n }\n }\n // As of openai@5.12.1, type ChatCompletionMessageToolCall can\n // have type=\"custom\" which has no `function` property. As well,\n // GenAI semconv has since changed how it captures tool calls.\n // For now we just cope: we could capture\n // `ChatCompletionMessageCustomToolCall.Custom` properties, but we\n // don't for now.\n body.tool_calls = msg.tool_calls?.map(tc => {\n const repr: GenAIToolCall = {\n id: tc.id,\n type: tc.type,\n };\n if (tc.type === 'function') {\n repr.function = {\n name: tc.function.name,\n arguments: tc.function.arguments,\n };\n }\n return repr;\n });\n } else {\n body.tool_calls = msg.tool_calls?.map(tc => {\n const repr: GenAIToolCall = {\n id: tc.id,\n type: tc.type,\n };\n if (tc.type === 'function') {\n repr.function = { name: tc.function.name };\n }\n return repr;\n });\n }\n this.logger.emit({\n timestamp,\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_ASSISTANT_MESSAGE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body,\n });\n break;\n }\n case 'tool': {\n const body: GenAIToolMessageEventBody = {\n id: msg.tool_call_id,\n };\n if (config.captureMessageContent) {\n if (Array.isArray(msg.content)) {\n body.content = msg.content.map(p => p.text).join('');\n } else {\n body.content = msg.content;\n }\n }\n this.logger.emit({\n timestamp,\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_TOOL_MESSAGE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body,\n });\n break;\n }\n default:\n this._diag.debug(\n `unknown message role in OpenAI.Chat.Completions.create: ${msg.role}`\n );\n }\n });\n\n return { span, ctx, commonAttrs };\n }\n\n /**\n * This wraps an instance of a `openai/streaming.Stream.iterator()`, an\n * async iterator. It should yield the chunks unchanged, and gather telemetry\n * data from those chunks, then end the span.\n */\n private async *_onChatCompletionsStreamIterator(\n streamIter: AsyncIterator,\n span: Span,\n startNow: number,\n config: OpenAIInstrumentationConfig,\n commonAttrs: Attributes,\n ctx: Context\n ) {\n let id;\n let model;\n const finishReasons: string[] = [];\n const choices = [];\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n for await (const chunk of streamIter as any) {\n yield chunk;\n\n // Gather telemetry from this chunk.\n this._diag.debug(\n 'OpenAI.Chat.Completions.create stream chunk: %O',\n chunk\n );\n const idx = chunk.choices[0]?.index ?? 0;\n if (!choices[idx]) {\n choices[idx] = {} as {\n content: string;\n toolCalls: ChatCompletionMessageToolCall[];\n };\n }\n if (config.captureMessageContent) {\n const contentPart = chunk.choices[0]?.delta?.content;\n if (contentPart) {\n if (!choices[idx].content) {\n choices[idx].content = '';\n }\n choices[idx].content += contentPart;\n }\n }\n // Assume delta.tool_calls, if exists, is an array of length 1.\n const toolCallPart = chunk.choices[0]?.delta?.tool_calls?.[0];\n if (toolCallPart) {\n if (!choices[idx].toolCalls) {\n choices[idx].toolCalls = [];\n }\n const toolCalls: GenAIToolCall[] = choices[idx].toolCalls;\n if (toolCallPart.id) {\n // First chunk in a tool call.\n const repr: GenAIToolCall = {\n id: toolCallPart.id,\n type: toolCallPart.type,\n };\n if (toolCallPart.type === 'function') {\n repr.function = {\n name: toolCallPart.function?.name,\n arguments: toolCallPart.function?.arguments ?? '',\n };\n }\n toolCalls.push(repr);\n } else if (toolCalls.length > 0) {\n // A tool call chunk with more of the `function.arguments`.\n const lastPart = toolCalls[toolCalls.length - 1];\n if (lastPart.function !== undefined) {\n lastPart.function.arguments +=\n toolCallPart.function?.arguments ?? '';\n }\n }\n }\n if (!id && chunk.id) {\n id = chunk.id;\n span.setAttribute(ATTR_GEN_AI_RESPONSE_ID, id);\n }\n if (!model && chunk.model) {\n model = chunk.model;\n span.setAttribute(ATTR_GEN_AI_RESPONSE_MODEL, model);\n }\n if (!finishReasons[idx]) {\n const finishReason = chunk.choices[0]?.finish_reason;\n if (finishReason) {\n finishReasons[idx] = finishReason;\n }\n }\n if (chunk.usage) {\n // A final usage chunk if `stream_options.include_usage: true`.\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n chunk.usage.prompt_tokens\n );\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n chunk.usage.completion_tokens\n );\n this._genaiClientTokenUsage.record(chunk.usage.prompt_tokens, {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: model,\n [ATTR_GEN_AI_TOKEN_TYPE]: 'input',\n });\n this._genaiClientTokenUsage.record(chunk.usage.completion_tokens, {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: model,\n [ATTR_GEN_AI_TOKEN_TYPE]: 'output',\n });\n }\n }\n span.setAttribute(\n ATTR_GEN_AI_RESPONSE_FINISH_REASONS,\n finishReasons.filter(f => f !== undefined)\n );\n\n // Capture choices as log events.\n for (const [idx, choice] of choices.entries()) {\n if (!choice) {\n continue;\n }\n const message: Partial = {};\n if (\n config.captureMessageContent &&\n choice.content &&\n choice.content.length > 0\n ) {\n message.content = choice.content;\n }\n if (choice.toolCalls && choice.toolCalls.length > 0) {\n message.tool_calls = choice.toolCalls;\n if (!config.captureMessageContent) {\n message.tool_calls.forEach(tc => {\n delete tc.function?.arguments;\n });\n }\n }\n this.logger.emit({\n timestamp: Date.now(),\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_CHOICE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body: {\n finish_reason: finishReasons[idx],\n index: idx,\n message,\n } as GenAIChoiceEventBody,\n });\n }\n\n this._genaiClientOperationDuration.record(\n (performance.now() - startNow) / 1000,\n {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: model,\n }\n );\n\n span.end();\n }\n\n private _onChatCompletionsCreateResult(\n span: Span,\n startNow: number,\n commonAttrs: Attributes,\n result: ChatCompletion,\n config: OpenAIInstrumentationConfig,\n ctx: Context\n ) {\n this._diag.debug('OpenAI.Chat.Completions.create result: %O', result);\n try {\n span.setAttribute(\n ATTR_GEN_AI_RESPONSE_FINISH_REASONS,\n result.choices.map(c => c.finish_reason)\n );\n span.setAttribute(ATTR_GEN_AI_RESPONSE_ID, result.id);\n span.setAttribute(ATTR_GEN_AI_RESPONSE_MODEL, result.model);\n if (result.usage) {\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n result.usage.prompt_tokens\n );\n span.setAttribute(\n ATTR_GEN_AI_USAGE_OUTPUT_TOKENS,\n result.usage.completion_tokens\n );\n }\n\n // Capture choices as log events.\n result.choices.forEach(choice => {\n const message: Partial = {};\n if (config.captureMessageContent) {\n // TODO: telemetry diff with streaming case: content=null, no 'role: assistant', 'tool calls (enableCaptureContent=true)' test case\n if (choice.message.content) {\n message.content = choice.message.content;\n }\n if (choice.message.tool_calls) {\n message.tool_calls = choice.message.tool_calls;\n }\n } else {\n if (choice.message.tool_calls) {\n message.tool_calls = choice.message.tool_calls.map(tc => {\n const repr: GenAIToolCall = {\n id: tc.id,\n type: tc.type,\n };\n if (tc.type === 'function') {\n repr.function = { name: tc.function.name };\n }\n return repr;\n });\n }\n }\n this.logger.emit({\n timestamp: Date.now(),\n context: ctx,\n severityNumber: SeverityNumber.INFO,\n attributes: {\n [ATTR_EVENT_NAME]: EVENT_GEN_AI_CHOICE,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n },\n body: {\n finish_reason: choice.finish_reason,\n index: choice.index,\n message,\n } as GenAIChoiceEventBody,\n });\n });\n\n this._genaiClientOperationDuration.record(\n (performance.now() - startNow) / 1000,\n {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: result.model,\n }\n );\n\n if (result.usage) {\n this._genaiClientTokenUsage.record(result.usage.prompt_tokens, {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: result.model,\n [ATTR_GEN_AI_TOKEN_TYPE]: 'input',\n });\n\n this._genaiClientTokenUsage.record(result.usage.completion_tokens, {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: result.model,\n [ATTR_GEN_AI_TOKEN_TYPE]: 'output',\n });\n }\n } catch (err) {\n this._diag.error(\n 'unexpected error getting telemetry from chat result:',\n err\n );\n }\n span.end();\n }\n\n private _createAPIPromiseRejectionHandler(\n startNow: number,\n span: Span,\n commonAttrs: Attributes\n ) {\n return (err: Error) => {\n this._diag.debug('OpenAI APIPromise rejection: %O', err);\n\n // https://github.com/openai/openai-node/blob/master/src/error.ts\n // The most reliable low cardinality string for errors seems to be\n // the class name. See also:\n // https://platform.openai.com/docs/guides/error-codes\n const errorType = err?.constructor?.name;\n\n this._genaiClientOperationDuration.record(\n (performance.now() - startNow) / 1000,\n {\n ...commonAttrs,\n 'error.type': errorType,\n }\n );\n\n span.setStatus({\n code: SpanStatusCode.ERROR,\n message: err.message,\n });\n\n span.setAttribute('error.type', errorType);\n span.end();\n };\n }\n\n private _getPatchedEmbeddingsCreate(): any {\n const self = this;\n return (original: Embeddings['create']) => {\n // https://platform.openai.com/docs/api-reference/embeddings/create\n return function patchedCreate(\n this: Embeddings,\n ...args: Parameters\n ) {\n if (!self.isEnabled) {\n return original.apply(this, args);\n }\n\n self._diag.debug('OpenAI.Chat.Embeddings.create args: %O', args);\n const params = args[0];\n const startNow = performance.now();\n\n let startInfo;\n try {\n startInfo = self._startEmbeddingsSpan(params, this?._client?.baseURL);\n } catch (err) {\n self._diag.error('unexpected error starting span:', err);\n return original.apply(this, args);\n }\n const { span, ctx, commonAttrs } = startInfo;\n\n const apiPromise = context.with(ctx, () => original.apply(this, args));\n\n apiPromise\n .then(result => {\n self._onEmbeddingsCreateResult(span, startNow, commonAttrs, result);\n })\n .catch(\n self._createAPIPromiseRejectionHandler(startNow, span, commonAttrs)\n );\n\n return apiPromise;\n };\n };\n }\n\n /**\n * Start a span for this chat-completion API call. This also emits log events\n * as appropriate for the request params.\n */\n private _startEmbeddingsSpan(\n params: EmbeddingCreateParams,\n baseURL: string | undefined\n ) {\n // Attributes common to span, metrics, log events.\n const commonAttrs: Attributes = {\n [ATTR_GEN_AI_OPERATION_NAME]: 'embeddings',\n [ATTR_GEN_AI_REQUEST_MODEL]: params.model,\n [ATTR_GEN_AI_SYSTEM]: 'openai',\n };\n Object.assign(commonAttrs, getAttrsFromBaseURL(baseURL, this._diag));\n\n // Span attributes.\n const attrs: Attributes = {\n ...commonAttrs,\n };\n if (params.encoding_format != null) {\n attrs[ATTR_GEN_AI_REQUEST_ENCODING_FORMATS] = [params.encoding_format];\n }\n\n const span = this.tracer.startSpan(\n `${attrs[ATTR_GEN_AI_OPERATION_NAME]} ${attrs[ATTR_GEN_AI_REQUEST_MODEL]}`,\n {\n kind: SpanKind.CLIENT,\n attributes: attrs,\n }\n );\n const ctx = trace.setSpan(context.active(), span);\n\n return { span, ctx, commonAttrs };\n }\n\n private _onEmbeddingsCreateResult(\n span: Span,\n startNow: number,\n commonAttrs: Attributes,\n result: CreateEmbeddingResponse\n ) {\n this._diag.debug('OpenAI.Embeddings.create result: %O', result);\n try {\n span.setAttribute(ATTR_GEN_AI_RESPONSE_MODEL, result.model);\n\n this._genaiClientOperationDuration.record(\n (performance.now() - startNow) / 1000,\n {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: result.model,\n }\n );\n\n span.setAttribute(\n ATTR_GEN_AI_USAGE_INPUT_TOKENS,\n result.usage.prompt_tokens\n );\n this._genaiClientTokenUsage.record(result.usage.prompt_tokens, {\n ...commonAttrs,\n [ATTR_GEN_AI_RESPONSE_MODEL]: result.model,\n [ATTR_GEN_AI_TOKEN_TYPE]: 'input',\n });\n } catch (err) {\n this._diag.error(\n 'unexpected error getting telemetry from embeddings result:',\n err\n );\n }\n span.end();\n }\n}\n\nfunction isTextContent(\n value: ChatCompletionContentPart | ChatCompletionContentPartRefusal\n): value is ChatCompletionContentPartText {\n return value.type === 'text';\n}\n\nfunction isStreamPromise(\n params: ChatCompletionCreateParams | undefined,\n value: APIPromise | ChatCompletion>\n): value is APIPromise> {\n if (params && params.stream) {\n return true;\n }\n return false;\n}\n"]}