{"version":3,"file":"utils.js","sources":["../../../../src/tracing/openai/utils.ts"],"sourcesContent":["import type { Span } from '../../types-hoist/span';\nimport {\n  GEN_AI_CONVERSATION_ID_ATTRIBUTE,\n  GEN_AI_REQUEST_DIMENSIONS_ATTRIBUTE,\n  GEN_AI_REQUEST_ENCODING_FORMAT_ATTRIBUTE,\n  GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE,\n  GEN_AI_REQUEST_MODEL_ATTRIBUTE,\n  GEN_AI_REQUEST_PRESENCE_PENALTY_ATTRIBUTE,\n  GEN_AI_REQUEST_STREAM_ATTRIBUTE,\n  GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE,\n  GEN_AI_REQUEST_TOP_P_ATTRIBUTE,\n  GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE,\n  GEN_AI_RESPONSE_ID_ATTRIBUTE,\n  GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n  GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n  GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,\n  OPENAI_OPERATIONS,\n  OPENAI_RESPONSE_ID_ATTRIBUTE,\n  OPENAI_RESPONSE_MODEL_ATTRIBUTE,\n  OPENAI_RESPONSE_TIMESTAMP_ATTRIBUTE,\n  OPENAI_USAGE_COMPLETION_TOKENS_ATTRIBUTE,\n  OPENAI_USAGE_PROMPT_TOKENS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { INSTRUMENTED_METHODS } from './constants';\nimport type {\n  ChatCompletionChunk,\n  InstrumentedMethod,\n  OpenAiChatCompletionObject,\n  OpenAIConversationObject,\n  OpenAICreateEmbeddingsObject,\n  OpenAIResponseObject,\n  ResponseStreamingEvent,\n} from './types';\n\n/**\n * Maps OpenAI method paths to OpenTelemetry semantic convention operation names\n * @see https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#llm-request-spans\n */\nexport function getOperationName(methodPath: string): string {\n  if (methodPath.includes('chat.completions')) {\n    return OPENAI_OPERATIONS.CHAT;\n  }\n  if (methodPath.includes('responses')) {\n    return OPENAI_OPERATIONS.CHAT;\n  }\n  if (methodPath.includes('embeddings')) {\n    return OPENAI_OPERATIONS.EMBEDDINGS;\n  }\n  if (methodPath.includes('conversations')) {\n    return OPENAI_OPERATIONS.CHAT;\n  }\n  return methodPath.split('.').pop() || 'unknown';\n}\n\n/**\n * Get the span operation for OpenAI methods\n * Following Sentry's convention: \"gen_ai.{operation_name}\"\n */\nexport function getSpanOperation(methodPath: string): string {\n  return `gen_ai.${getOperationName(methodPath)}`;\n}\n\n/**\n * Check if a method path should be instrumented\n */\nexport function shouldInstrument(methodPath: string): methodPath is InstrumentedMethod {\n  return INSTRUMENTED_METHODS.includes(methodPath as InstrumentedMethod);\n}\n\n/**\n * Build method path from current traversal\n */\nexport function buildMethodPath(currentPath: string, prop: string): string {\n  return currentPath ? `${currentPath}.${prop}` : prop;\n}\n\n/**\n * Check if response is a Chat Completion object\n */\nexport function isChatCompletionResponse(response: unknown): response is OpenAiChatCompletionObject {\n  return (\n    response !== null &&\n    typeof response === 'object' &&\n    'object' in response &&\n    (response as Record<string, unknown>).object === 'chat.completion'\n  );\n}\n\n/**\n * Check if response is a Responses API object\n */\nexport function isResponsesApiResponse(response: unknown): response is OpenAIResponseObject {\n  return (\n    response !== null &&\n    typeof response === 'object' &&\n    'object' in response &&\n    (response as Record<string, unknown>).object === 'response'\n  );\n}\n\n/**\n * Check if response is an Embeddings API object\n */\nexport function isEmbeddingsResponse(response: unknown): response is OpenAICreateEmbeddingsObject {\n  if (response === null || typeof response !== 'object' || !('object' in response)) {\n    return false;\n  }\n  const responseObject = response as Record<string, unknown>;\n  return (\n    responseObject.object === 'list' &&\n    typeof responseObject.model === 'string' &&\n    responseObject.model.toLowerCase().includes('embedding')\n  );\n}\n\n/**\n * Check if response is a Conversations API object\n * @see https://platform.openai.com/docs/api-reference/conversations\n */\nexport function isConversationResponse(response: unknown): response is OpenAIConversationObject {\n  return (\n    response !== null &&\n    typeof response === 'object' &&\n    'object' in response &&\n    (response as Record<string, unknown>).object === 'conversation'\n  );\n}\n\n/**\n * Check if streaming event is from the Responses API\n */\nexport function isResponsesApiStreamEvent(event: unknown): event is ResponseStreamingEvent {\n  return (\n    event !== null &&\n    typeof event === 'object' &&\n    'type' in event &&\n    typeof (event as Record<string, unknown>).type === 'string' &&\n    ((event as Record<string, unknown>).type as string).startsWith('response.')\n  );\n}\n\n/**\n * Check if streaming event is a chat completion chunk\n */\nexport function isChatCompletionChunk(event: unknown): event is ChatCompletionChunk {\n  return (\n    event !== null &&\n    typeof event === 'object' &&\n    'object' in event &&\n    (event as Record<string, unknown>).object === 'chat.completion.chunk'\n  );\n}\n\n/**\n * Add attributes for Chat Completion responses\n */\nexport function addChatCompletionAttributes(\n  span: Span,\n  response: OpenAiChatCompletionObject,\n  recordOutputs?: boolean,\n): void {\n  setCommonResponseAttributes(span, response.id, response.model, response.created);\n  if (response.usage) {\n    setTokenUsageAttributes(\n      span,\n      response.usage.prompt_tokens,\n      response.usage.completion_tokens,\n      response.usage.total_tokens,\n    );\n  }\n  if (Array.isArray(response.choices)) {\n    const finishReasons = response.choices\n      .map(choice => choice.finish_reason)\n      .filter((reason): reason is string => reason !== null);\n    if (finishReasons.length > 0) {\n      span.setAttributes({\n        [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify(finishReasons),\n      });\n    }\n\n    // Extract tool calls from all choices (only if recordOutputs is true)\n    if (recordOutputs) {\n      const toolCalls = response.choices\n        .map(choice => choice.message?.tool_calls)\n        .filter(calls => Array.isArray(calls) && calls.length > 0)\n        .flat();\n\n      if (toolCalls.length > 0) {\n        span.setAttributes({\n          [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(toolCalls),\n        });\n      }\n    }\n  }\n}\n\n/**\n * Add attributes for Responses API responses\n */\nexport function addResponsesApiAttributes(span: Span, response: OpenAIResponseObject, recordOutputs?: boolean): void {\n  setCommonResponseAttributes(span, response.id, response.model, response.created_at);\n  if (response.status) {\n    span.setAttributes({\n      [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify([response.status]),\n    });\n  }\n  if (response.usage) {\n    setTokenUsageAttributes(\n      span,\n      response.usage.input_tokens,\n      response.usage.output_tokens,\n      response.usage.total_tokens,\n    );\n  }\n\n  // Extract function calls from output (only if recordOutputs is true)\n  if (recordOutputs) {\n    const responseWithOutput = response as OpenAIResponseObject & { output?: unknown[] };\n    if (Array.isArray(responseWithOutput.output) && responseWithOutput.output.length > 0) {\n      // Filter for function_call type objects in the output array\n      const functionCalls = responseWithOutput.output.filter(\n        (item): unknown =>\n          typeof item === 'object' && item !== null && (item as Record<string, unknown>).type === 'function_call',\n      );\n\n      if (functionCalls.length > 0) {\n        span.setAttributes({\n          [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(functionCalls),\n        });\n      }\n    }\n  }\n}\n\n/**\n * Add attributes for Embeddings API responses\n */\nexport function addEmbeddingsAttributes(span: Span, response: OpenAICreateEmbeddingsObject): void {\n  span.setAttributes({\n    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* @param promptTokens - The number of prompt tokens\n * @param completionTokens - The number of completion tokens\n * @param totalTokens - The number of total tokens\n */\nexport function setTokenUsageAttributes(\n  span: Span,\n  promptTokens?: number,\n  completionTokens?: number,\n  totalTokens?: number,\n): void {\n  if (promptTokens !== undefined) {\n    span.setAttributes({\n      [OPENAI_USAGE_PROMPT_TOKENS_ATTRIBUTE]: promptTokens,\n      [GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE]: promptTokens,\n    });\n  }\n  if (completionTokens !== undefined) {\n    span.setAttributes({\n      [OPENAI_USAGE_COMPLETION_TOKENS_ATTRIBUTE]: completionTokens,\n      [GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE]: completionTokens,\n    });\n  }\n  if (totalTokens !== undefined) {\n    span.setAttributes({\n      [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: totalTokens,\n    });\n  }\n}\n\n/**\n * Set common response attributes\n * @param span - The span to add attributes to\n * @param id - The response id\n * @param model - The response model\n * @param timestamp - The response timestamp\n */\nexport function setCommonResponseAttributes(span: Span, id: string, model: string, timestamp: number): void {\n  span.setAttributes({\n    [OPENAI_RESPONSE_ID_ATTRIBUTE]: id,\n    [GEN_AI_RESPONSE_ID_ATTRIBUTE]: id,\n  });\n  span.setAttributes({\n    [OPENAI_RESPONSE_MODEL_ATTRIBUTE]: model,\n    [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: model,\n  });\n  span.setAttributes({\n    [OPENAI_RESPONSE_TIMESTAMP_ATTRIBUTE]: new Date(timestamp * 1000).toISOString(),\n  });\n}\n\n/**\n * Extract conversation ID from request parameters\n * Supports both Conversations API and previous_response_id chaining\n * @see https://platform.openai.com/docs/guides/conversation-state\n */\nfunction extractConversationId(params: Record<string, unknown>): string | undefined {\n  // Conversations API: conversation parameter (e.g., \"conv_...\")\n  if ('conversation' in params && typeof params.conversation === 'string') {\n    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