{"version":3,"file":"utils.js","sources":["../../../../src/tracing/langchain/utils.ts"],"sourcesContent":["import { SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '../../semanticAttributes';\nimport type { SpanAttributeValue } from '../../types-hoist/span';\nimport {\n  GEN_AI_INPUT_MESSAGES_ATTRIBUTE,\n  GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE,\n  GEN_AI_OPERATION_NAME_ATTRIBUTE,\n  GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE,\n  GEN_AI_REQUEST_MAX_TOKENS_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_STOP_REASON_ATTRIBUTE,\n  GEN_AI_RESPONSE_TEXT_ATTRIBUTE,\n  GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n  GEN_AI_SYSTEM_ATTRIBUTE,\n  GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE,\n  GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n  GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { truncateGenAiMessages } from '../ai/messageTruncation';\nimport { extractSystemInstructions } from '../ai/utils';\nimport { LANGCHAIN_ORIGIN, ROLE_MAP } from './constants';\nimport type { LangChainLLMResult, LangChainMessage, LangChainSerialized } from './types';\n\n/**\n * Assigns an attribute only when the value is neither `undefined` nor `null`.\n *\n * We keep this tiny helper because call sites are repetitive and easy to miswrite.\n * It also preserves falsy-but-valid values like `0` and `\"\"`.\n */\nconst setIfDefined = (target: Record<string, SpanAttributeValue>, key: string, value: unknown): void => {\n  if (value != null) target[key] = value as SpanAttributeValue;\n};\n\n/**\n * Like `setIfDefined`, but converts the value with `Number()` and skips only when the\n * result is `NaN`. This ensures numeric 0 makes it through (unlike truthy checks).\n */\nconst setNumberIfDefined = (target: Record<string, SpanAttributeValue>, key: string, value: unknown): void => {\n  const n = Number(value);\n  if (!Number.isNaN(n)) target[key] = n;\n};\n\n/**\n * Converts a value to a string. Avoids double-quoted JSON strings where a plain\n * string is desired, but still handles objects/arrays safely.\n */\nfunction asString(v: unknown): string {\n  if (typeof v === 'string') return v;\n  try {\n    return JSON.stringify(v);\n  } catch {\n    return String(v);\n  }\n}\n\n/**\n * Normalizes a single role token to our canonical set.\n *\n * @param role Incoming role value (free-form, any casing)\n * @returns Canonical role: 'user' | 'assistant' | 'system' | 'function' | 'tool' | <passthrough>\n */\nfunction normalizeMessageRole(role: string): string {\n  const normalized = role.toLowerCase();\n  return ROLE_MAP[normalized] ?? normalized;\n}\n\n/**\n * Infers a role from a LangChain message constructor name.\n *\n * Checks for substrings like \"System\", \"Human\", \"AI\", etc.\n */\nfunction normalizeRoleNameFromCtor(name: string): string {\n  if (name.includes('System')) return 'system';\n  if (name.includes('Human')) return 'user';\n  if (name.includes('AI') || name.includes('Assistant')) return 'assistant';\n  if (name.includes('Function')) return 'function';\n  if (name.includes('Tool')) return 'tool';\n  return 'user';\n}\n\n/**\n * Returns invocation params from a LangChain `tags` object.\n *\n * LangChain often passes runtime parameters (model, temperature, etc.) via the\n * `tags.invocation_params` bag. If `tags` is an array (LangChain sometimes uses\n * string tags), we return `undefined`.\n *\n * @param tags LangChain tags (string[] or record)\n * @returns The `invocation_params` object, if present\n */\nexport function getInvocationParams(tags?: string[] | Record<string, unknown>): Record<string, unknown> | undefined {\n  if (!tags || Array.isArray(tags)) return undefined;\n  return tags.invocation_params as Record<string, unknown> | undefined;\n}\n\n/**\n * Normalizes a heterogeneous set of LangChain messages to `{ role, content }`.\n *\n * Why so many branches? LangChain messages can arrive in several shapes:\n *  - Message classes with `_getType()` (most reliable)\n *  - Classes with meaningful constructor names (e.g. `SystemMessage`)\n *  - Plain objects with `type`, or `{ role, content }`\n *  - Serialized format with `{ lc: 1, id: [...], kwargs: { content } }`\n * We preserve the prioritization to minimize behavioral drift.\n *\n * @param messages Mixed LangChain messages\n * @returns Array of normalized `{ role, content }`\n */\nexport function normalizeLangChainMessages(messages: LangChainMessage[]): Array<{ role: string; content: string }> {\n  return messages.map(message => {\n    // 1) Prefer _getType() when present\n    const maybeGetType = (message as { _getType?: () => string })._getType;\n    if (typeof maybeGetType === 'function') {\n      const messageType = maybeGetType.call(message);\n      return {\n        role: normalizeMessageRole(messageType),\n        content: asString(message.content),\n      };\n    }\n\n    // 2) Serialized LangChain format (lc: 1) - check before constructor name\n    // This is more reliable than constructor.name which can be lost during serialization\n    if (message.lc === 1 && message.kwargs) {\n      const id = message.id;\n      const messageType = Array.isArray(id) && id.length > 0 ? id[id.length - 1] : '';\n      const role = typeof messageType === 'string' ? normalizeRoleNameFromCtor(messageType) : 'user';\n\n      return {\n        role: normalizeMessageRole(role),\n        content: asString(message.kwargs?.content),\n      };\n    }\n\n    // 3) Then objects with `type`\n    if (message.type) {\n      const role = String(message.type).toLowerCase();\n      return {\n        role: normalizeMessageRole(role),\n        content: asString(message.content),\n      };\n    }\n\n    // 4) Then objects with `{ role, content }` - check before constructor name\n    // Plain objects have constructor.name=\"Object\" which would incorrectly default to \"user\"\n    if (message.role) {\n      return {\n        role: normalizeMessageRole(String(message.role)),\n        content: asString(message.content),\n      };\n    }\n\n    // 5) Then try constructor name (SystemMessage / HumanMessage / ...)\n    // Only use this if we haven't matched a more specific case\n    const ctor = (message as { constructor?: { name?: string } }).constructor?.name;\n    if (ctor && ctor !== 'Object') {\n      return {\n        role: normalizeMessageRole(normalizeRoleNameFromCtor(ctor)),\n        content: asString(message.content),\n      };\n    }\n\n    // 6) Fallback: treat as user text\n    return {\n      role: 'user',\n      content: asString(message.content),\n    };\n  });\n}\n\n/**\n * Extracts request attributes common to both LLM and ChatModel invocations.\n *\n * Source precedence:\n * 1) `invocationParams` (highest)\n * 2) `langSmithMetadata`\n *\n * Numeric values are set even when 0 (e.g. `temperature: 0`), but skipped if `NaN`.\n */\nfunction extractCommonRequestAttributes(\n  serialized: LangChainSerialized,\n  invocationParams?: Record<string, unknown>,\n  langSmithMetadata?: Record<string, unknown>,\n): Record<string, SpanAttributeValue> {\n  const attrs: Record<string, SpanAttributeValue> = {};\n\n  // Get kwargs if available (from constructor type)\n  const kwargs = 'kwargs' in serialized ? serialized.kwargs : undefined;\n\n  const temperature = invocationParams?.temperature ?? langSmithMetadata?.ls_temperature ?? kwargs?.temperature;\n  setNumberIfDefined(attrs, GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE, temperature);\n\n  const maxTokens = invocationParams?.max_tokens ?? langSmithMetadata?.ls_max_tokens ?? kwargs?.max_tokens;\n  setNumberIfDefined(attrs, GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE, maxTokens);\n\n  const topP = invocationParams?.top_p ?? kwargs?.top_p;\n  setNumberIfDefined(attrs, GEN_AI_REQUEST_TOP_P_ATTRIBUTE, topP);\n\n  const frequencyPenalty = invocationParams?.frequency_penalty;\n  setNumberIfDefined(attrs, GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE, frequencyPenalty);\n\n  const presencePenalty = invocationParams?.presence_penalty;\n  setNumberIfDefined(attrs, GEN_AI_REQUEST_PRESENCE_PENALTY_ATTRIBUTE, presencePenalty);\n\n  // LangChain uses `stream`. We only set the attribute if the key actually exists\n  // (some callbacks report `false` even on streamed requests, this stems from LangChain's callback handler).\n  if (invocationParams && 'stream' in invocationParams) {\n    setIfDefined(attrs, GEN_AI_REQUEST_STREAM_ATTRIBUTE, Boolean(invocationParams.stream));\n  }\n\n  return attrs;\n}\n\n/**\n * Small helper to assemble boilerplate attributes shared by both request extractors.\n * Always uses 'chat' as the operation type for all LLM and chat model operations.\n */\nfunction baseRequestAttributes(\n  system: unknown,\n  modelName: unknown,\n  serialized: LangChainSerialized,\n  invocationParams?: Record<string, unknown>,\n  langSmithMetadata?: Record<string, unknown>,\n): Record<string, SpanAttributeValue> {\n  return {\n    [GEN_AI_SYSTEM_ATTRIBUTE]: asString(system ?? 'langchain'),\n    [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'chat',\n    [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: asString(modelName),\n    [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: LANGCHAIN_ORIGIN,\n    ...extractCommonRequestAttributes(serialized, invocationParams, langSmithMetadata),\n  };\n}\n\n/**\n * Extracts attributes for plain LLM invocations (string prompts).\n *\n * - Operation is tagged as `chat` following OpenTelemetry semantic conventions.\n *   LangChain LLM operations are treated as chat operations.\n * - When `recordInputs` is true, string prompts are wrapped into `{role:\"user\"}`\n *   messages to align with the chat schema used elsewhere.\n */\nexport function extractLLMRequestAttributes(\n  llm: LangChainSerialized,\n  prompts: string[],\n  recordInputs: boolean,\n  invocationParams?: Record<string, unknown>,\n  langSmithMetadata?: Record<string, unknown>,\n): Record<string, SpanAttributeValue> {\n  const system = langSmithMetadata?.ls_provider;\n  const modelName = invocationParams?.model ?? langSmithMetadata?.ls_model_name ?? 'unknown';\n\n  const attrs = baseRequestAttributes(system, modelName, llm, invocationParams, langSmithMetadata);\n\n  if (recordInputs && Array.isArray(prompts) && prompts.length > 0) {\n    setIfDefined(attrs, GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE, prompts.length);\n    const messages = prompts.map(p => ({ role: 'user', content: p }));\n    setIfDefined(attrs, GEN_AI_INPUT_MESSAGES_ATTRIBUTE, asString(messages));\n  }\n\n  return attrs;\n}\n\n/**\n * Extracts attributes for ChatModel invocations (array-of-arrays of messages).\n *\n * - Operation is tagged as `chat` following OpenTelemetry semantic conventions.\n *   LangChain chat model operations are chat operations.\n * - We flatten LangChain's `LangChainMessage[][]` and normalize shapes into a\n *   consistent `{ role, content }` array when `recordInputs` is true.\n * - Provider system value falls back to `serialized.id?.[2]`.\n */\nexport function extractChatModelRequestAttributes(\n  llm: LangChainSerialized,\n  langChainMessages: LangChainMessage[][],\n  recordInputs: boolean,\n  invocationParams?: Record<string, unknown>,\n  langSmithMetadata?: Record<string, unknown>,\n): Record<string, SpanAttributeValue> {\n  const system = langSmithMetadata?.ls_provider ?? llm.id?.[2];\n  const modelName = invocationParams?.model ?? langSmithMetadata?.ls_model_name ?? 'unknown';\n\n  const attrs = baseRequestAttributes(system, modelName, llm, invocationParams, langSmithMetadata);\n\n  if (recordInputs && Array.isArray(langChainMessages) && langChainMessages.length > 0) {\n    const normalized = normalizeLangChainMessages(langChainMessages.flat());\n\n    const { systemInstructions, filteredMessages } = extractSystemInstructions(normalized);\n\n    if (systemInstructions) {\n      setIfDefined(attrs, GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE, systemInstructions);\n    }\n\n    const filteredLength = Array.isArray(filteredMessages) ? filteredMessages.length : 0;\n    setIfDefined(attrs, GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE, filteredLength);\n\n    const truncated = truncateGenAiMessages(filteredMessages as unknown[]);\n    setIfDefined(attrs, GEN_AI_INPUT_MESSAGES_ATTRIBUTE, asString(truncated));\n  }\n\n  return attrs;\n}\n\n/**\n * Scans generations for Anthropic-style `tool_use` items and records them.\n *\n * LangChain represents some provider messages (e.g., Anthropic) with a `message.content`\n * array that may include objects `{ type: 'tool_use', ... }`. We collect and attach\n * them as a JSON array on `gen_ai.response.tool_calls` for downstream consumers.\n */\nfunction addToolCallsAttributes(generations: LangChainMessage[][], attrs: Record<string, SpanAttributeValue>): void {\n  const toolCalls: unknown[] = [];\n  const flatGenerations = generations.flat();\n\n  for (const gen of flatGenerations) {\n    const content = gen.message?.content;\n    if (Array.isArray(content)) {\n      for (const item of content) {\n        const t = item as { type: string };\n        if (t.type === 'tool_use') toolCalls.push(t);\n      }\n    }\n  }\n\n  if (toolCalls.length > 0) {\n    setIfDefined(attrs, GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE, asString(toolCalls));\n  }\n}\n\n/**\n * Adds token usage attributes, supporting both OpenAI (`tokenUsage`) and Anthropic (`usage`) formats.\n * - Preserve zero values (0 tokens) by avoiding truthy checks.\n * - Compute a total for Anthropic when not explicitly provided.\n * - Include cache token metrics when present.\n */\nfunction addTokenUsageAttributes(\n  llmOutput: LangChainLLMResult['llmOutput'],\n  attrs: Record<string, SpanAttributeValue>,\n): void {\n  if (!llmOutput) return;\n\n  const tokenUsage = llmOutput.tokenUsage as\n    | { promptTokens?: number; completionTokens?: number; totalTokens?: number }\n    | undefined;\n  const anthropicUsage = llmOutput.usage as\n    | {\n        input_tokens?: number;\n        output_tokens?: number;\n        cache_creation_input_tokens?: number;\n        cache_read_input_tokens?: number;\n      }\n    | undefined;\n\n  if (tokenUsage) {\n    setNumberIfDefined(attrs, GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE, tokenUsage.promptTokens);\n    setNumberIfDefined(attrs, GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE, tokenUsage.completionTokens);\n    setNumberIfDefined(attrs, GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE, tokenUsage.totalTokens);\n  } else if (anthropicUsage) {\n    setNumberIfDefined(attrs, GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE, anthropicUsage.input_tokens);\n    setNumberIfDefined(attrs, GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE, anthropicUsage.output_tokens);\n\n    // Compute total when not provided by the provider.\n    const input = Number(anthropicUsage.input_tokens);\n    const output = Number(anthropicUsage.output_tokens);\n    const total = (Number.isNaN(input) ? 0 : input) + (Number.isNaN(output) ? 0 : output);\n    if (total > 0) setNumberIfDefined(attrs, GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE, total);\n\n    // Extra Anthropic cache metrics (present only when caching is enabled)\n    if (anthropicUsage.cache_creation_input_tokens !== undefined)\n      setNumberIfDefined(\n        attrs,\n        GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS_ATTRIBUTE,\n        anthropicUsage.cache_creation_input_tokens,\n      );\n    if (anthropicUsage.cache_read_input_tokens !== undefined)\n      setNumberIfDefined(attrs, GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS_ATTRIBUTE, anthropicUsage.cache_read_input_tokens);\n  }\n}\n\n/**\n * Extracts response-related attributes based on a `LangChainLLMResult`.\n *\n * - Records finish reasons when present on generations (e.g., OpenAI)\n * - When `recordOutputs` is true, captures textual response content and any\n *   tool calls.\n * - Also propagates model name (`model_name` or `model`), response `id`, and\n *   `stop_reason` (for providers that use it).\n */\nexport function extractLlmResponseAttributes(\n  llmResult: LangChainLLMResult,\n  recordOutputs: boolean,\n): Record<string, SpanAttributeValue> | undefined {\n  if (!llmResult) return;\n\n  const attrs: Record<string, SpanAttributeValue> = {};\n\n  if (Array.isArray(llmResult.generations)) {\n    const finishReasons = llmResult.generations\n      .flat()\n      .map(g => {\n        // v1 uses generationInfo.finish_reason\n        if (g.generationInfo?.finish_reason) {\n          return g.generationInfo.finish_reason;\n        }\n        // v0.3+ uses generation_info.finish_reason\n        if (g.generation_info?.finish_reason) {\n          return g.generation_info.finish_reason;\n        }\n        return null;\n      })\n      .filter((r): r is string => typeof r === 'string');\n\n    if (finishReasons.length > 0) {\n      setIfDefined(attrs, GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE, asString(finishReasons));\n    }\n\n    // Tool calls metadata (names, IDs) are not PII, so capture them regardless of recordOutputs\n    addToolCallsAttributes(llmResult.generations as LangChainMessage[][], attrs);\n\n    if (recordOutputs) {\n      const texts = llmResult.generations\n        .flat()\n        .map(gen => gen.text ?? gen.message?.content)\n        .filter(t => typeof t === 'string');\n\n      if (texts.length > 0) {\n        setIfDefined(attrs, GEN_AI_RESPONSE_TEXT_ATTRIBUTE, asString(texts));\n      }\n    }\n  }\n\n  addTokenUsageAttributes(llmResult.llmOutput, attrs);\n\n  const llmOutput = llmResult.llmOutput;\n\n  // Extract from v1 generations structure if available\n  const firstGeneration = llmResult.generations?.[0]?.[0];\n  const v1Message = firstGeneration?.message;\n\n  // Provider model identifier: `model_name` (OpenAI-style) or `model` (others)\n  // v1 stores this in message.response_metadata.model_name\n  const modelName = llmOutput?.model_name ?? llmOutput?.model ?? v1Message?.response_metadata?.model_name;\n  if (modelName) setIfDefined(attrs, GEN_AI_RESPONSE_MODEL_ATTRIBUTE, modelName);\n\n  // Response ID: v1 stores this in message.id\n  const responseId = llmOutput?.id ?? v1Message?.id;\n  if (responseId) {\n    setIfDefined(attrs, GEN_AI_RESPONSE_ID_ATTRIBUTE, responseId);\n  }\n\n  // Stop reason: v1 stores this in message.response_metadata.finish_reason\n  const stopReason = llmOutput?.stop_reason ?? v1Message?.response_metadata?.finish_reason;\n  if (stopReason) {\n    setIfDefined(attrs, GEN_AI_RESPONSE_STOP_REASON_ATTRIBUTE, asString(stopReason));\n  }\n\n  return 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