Marc Mintel
5397309103
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{"version":3,"file":"utils.js","sources":["../../../../src/tracing/langgraph/utils.ts"],"sourcesContent":["import type { Span } from '../../types-hoist/span';\nimport {\n GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_TEXT_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} from '../ai/gen-ai-attributes';\nimport type { LangChainMessage } from '../langchain/types';\nimport { normalizeLangChainMessages } from '../langchain/utils';\nimport type { CompiledGraph, LangGraphTool } from './types';\n\n/**\n * Extract tool calls from messages\n */\nexport function extractToolCalls(messages: Array<Record<string, unknown>> | null): unknown[] | null {\n if (!messages || messages.length === 0) {\n return null;\n }\n\n const toolCalls: unknown[] = [];\n\n for (const message of messages) {\n if (message && typeof message === 'object') {\n const msgToolCalls = message.tool_calls;\n if (msgToolCalls && Array.isArray(msgToolCalls)) {\n toolCalls.push(...msgToolCalls);\n }\n }\n }\n\n return toolCalls.length > 0 ? toolCalls : null;\n}\n\n/**\n * Extract token usage from a message's usage_metadata or response_metadata\n * Returns token counts without setting span attributes\n */\nexport function extractTokenUsageFromMessage(message: LangChainMessage): {\n inputTokens: number;\n outputTokens: number;\n totalTokens: number;\n} {\n const msg = message as Record<string, unknown>;\n let inputTokens = 0;\n let outputTokens = 0;\n let totalTokens = 0;\n\n // Extract from usage_metadata (newer format)\n if (msg.usage_metadata && typeof msg.usage_metadata === 'object') {\n const usage = msg.usage_metadata as Record<string, unknown>;\n if (typeof usage.input_tokens === 'number') {\n inputTokens = usage.input_tokens;\n }\n if (typeof usage.output_tokens === 'number') {\n outputTokens = usage.output_tokens;\n }\n if (typeof usage.total_tokens === 'number') {\n totalTokens = usage.total_tokens;\n }\n return { inputTokens, outputTokens, totalTokens };\n }\n\n // Fallback: Extract from response_metadata.tokenUsage\n if (msg.response_metadata && typeof msg.response_metadata === 'object') {\n const metadata = msg.response_metadata as Record<string, unknown>;\n if (metadata.tokenUsage && typeof metadata.tokenUsage === 'object') {\n const tokenUsage = metadata.tokenUsage as Record<string, unknown>;\n if (typeof tokenUsage.promptTokens === 'number') {\n inputTokens = tokenUsage.promptTokens;\n }\n if (typeof tokenUsage.completionTokens === 'number') {\n outputTokens = tokenUsage.completionTokens;\n }\n if (typeof tokenUsage.totalTokens === 'number') {\n totalTokens = tokenUsage.totalTokens;\n }\n }\n }\n\n return { inputTokens, outputTokens, totalTokens };\n}\n\n/**\n * Extract model and finish reason from a message's response_metadata\n */\nexport function extractModelMetadata(span: Span, message: LangChainMessage): void {\n const msg = message as Record<string, unknown>;\n\n if (msg.response_metadata && typeof msg.response_metadata === 'object') {\n const metadata = msg.response_metadata as Record<string, unknown>;\n\n if (metadata.model_name && typeof metadata.model_name === 'string') {\n span.setAttribute(GEN_AI_RESPONSE_MODEL_ATTRIBUTE, metadata.model_name);\n }\n\n if (metadata.finish_reason && typeof metadata.finish_reason === 'string') {\n span.setAttribute(GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE, [metadata.finish_reason]);\n }\n }\n}\n\n/**\n * Extract tools from compiled graph structure\n *\n * Tools are stored in: compiledGraph.builder.nodes.tools.runnable.tools\n */\nexport function extractToolsFromCompiledGraph(compiledGraph: CompiledGraph): unknown[] | null {\n if (!compiledGraph.builder?.nodes?.tools?.runnable?.tools) {\n return null;\n }\n\n const tools = compiledGraph.builder?.nodes?.tools?.runnable?.tools;\n\n if (!tools || !Array.isArray(tools) || tools.length === 0) {\n return null;\n }\n\n // Extract name, description, and schema from each tool's lc_kwargs\n return tools.map((tool: LangGraphTool) => ({\n name: tool.lc_kwargs?.name,\n description: tool.lc_kwargs?.description,\n schema: tool.lc_kwargs?.schema,\n }));\n}\n\n/**\n * Set response attributes on the span\n */\nexport function setResponseAttributes(span: Span, inputMessages: LangChainMessage[] | null, result: unknown): void {\n // Extract messages from result\n const resultObj = result as { messages?: LangChainMessage[] } | undefined;\n const outputMessages = resultObj?.messages;\n\n if (!outputMessages || !Array.isArray(outputMessages)) {\n return;\n }\n\n // Get new messages (delta between input and output)\n const inputCount = inputMessages?.length ?? 0;\n const newMessages = outputMessages.length > inputCount ? outputMessages.slice(inputCount) : [];\n\n if (newMessages.length === 0) {\n return;\n }\n\n // Extract and set tool calls from new messages BEFORE normalization\n // (normalization strips tool_calls, so we need to extract them first)\n const toolCalls = extractToolCalls(newMessages as Array<Record<string, unknown>>);\n if (toolCalls) {\n span.setAttribute(GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE, JSON.stringify(toolCalls));\n }\n\n // Normalize the new messages\n const normalizedNewMessages = normalizeLangChainMessages(newMessages);\n span.setAttribute(GEN_AI_RESPONSE_TEXT_ATTRIBUTE, JSON.stringify(normalizedNewMessages));\n\n // Accumulate token usage across all messages\n let totalInputTokens = 0;\n let totalOutputTokens = 0;\n let totalTokens = 0;\n\n // Extract metadata from messages\n for (const message of newMessages) {\n // Accumulate token usage\n const tokens = extractTokenUsageFromMessage(message);\n totalInputTokens += tokens.inputTokens;\n totalOutputTokens += tokens.outputTokens;\n totalTokens += tokens.totalTokens;\n\n // Extract model metadata (last message's metadata wins for model/finish_reason)\n extractModelMetadata(span, message);\n }\n\n // Set accumulated token usage on span\n if (totalInputTokens > 0) {\n span.setAttribute(GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE, totalInputTokens);\n }\n if (totalOutputTokens > 0) {\n span.setAttribute(GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE, totalOutputTokens);\n }\n if (totalTokens > 0) {\n span.setAttribute(GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE, totalTokens);\n 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