Marc Mintel
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{"version":3,"file":"utils.js","sources":["../../../../src/tracing/vercel-ai/utils.ts"],"sourcesContent":["import type { TraceContext } from '../../types-hoist/context';\nimport type { Span, SpanAttributes, SpanJSON } from '../../types-hoist/span';\nimport {\n GEN_AI_EMBED_DO_EMBED_OPERATION_ATTRIBUTE,\n GEN_AI_EMBED_MANY_DO_EMBED_OPERATION_ATTRIBUTE,\n GEN_AI_EXECUTE_TOOL_OPERATION_ATTRIBUTE,\n GEN_AI_GENERATE_OBJECT_DO_GENERATE_OPERATION_ATTRIBUTE,\n GEN_AI_GENERATE_TEXT_DO_GENERATE_OPERATION_ATTRIBUTE,\n GEN_AI_INPUT_MESSAGES_ATTRIBUTE,\n GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE,\n GEN_AI_INVOKE_AGENT_OPERATION_ATTRIBUTE,\n GEN_AI_RERANK_DO_RERANK_OPERATION_ATTRIBUTE,\n GEN_AI_STREAM_OBJECT_DO_STREAM_OPERATION_ATTRIBUTE,\n GEN_AI_STREAM_TEXT_DO_STREAM_OPERATION_ATTRIBUTE,\n GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE,\n GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { extractSystemInstructions, getTruncatedJsonString } from '../ai/utils';\nimport { toolCallSpanMap } from './constants';\nimport type { TokenSummary } from './types';\nimport { AI_PROMPT_ATTRIBUTE, AI_PROMPT_MESSAGES_ATTRIBUTE } from './vercel-ai-attributes';\n\n/**\n * Accumulates token data from a span to its parent in the token accumulator map.\n * This function extracts token usage from the current span and adds it to the\n * accumulated totals for its parent span.\n */\nexport function accumulateTokensForParent(span: SpanJSON, tokenAccumulator: Map<string, TokenSummary>): void {\n const parentSpanId = span.parent_span_id;\n if (!parentSpanId) {\n return;\n }\n\n const inputTokens = span.data[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE];\n const outputTokens = span.data[GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE];\n\n if (typeof inputTokens === 'number' || typeof outputTokens === 'number') {\n const existing = tokenAccumulator.get(parentSpanId) || { inputTokens: 0, outputTokens: 0 };\n\n if (typeof inputTokens === 'number') {\n existing.inputTokens += inputTokens;\n }\n if (typeof outputTokens === 'number') {\n existing.outputTokens += outputTokens;\n }\n\n tokenAccumulator.set(parentSpanId, existing);\n }\n}\n\n/**\n * Applies accumulated token data to the `gen_ai.invoke_agent` span.\n * Only immediate children of the `gen_ai.invoke_agent` span are considered,\n * since aggregation will automatically occur for each parent span.\n */\nexport function applyAccumulatedTokens(\n spanOrTrace: SpanJSON | TraceContext,\n tokenAccumulator: Map<string, TokenSummary>,\n): void {\n const accumulated = tokenAccumulator.get(spanOrTrace.span_id);\n if (!accumulated || !spanOrTrace.data) {\n return;\n }\n\n if (accumulated.inputTokens > 0) {\n spanOrTrace.data[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE] = accumulated.inputTokens;\n }\n if (accumulated.outputTokens > 0) {\n spanOrTrace.data[GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE] = accumulated.outputTokens;\n }\n if (accumulated.inputTokens > 0 || accumulated.outputTokens > 0) {\n spanOrTrace.data['gen_ai.usage.total_tokens'] = accumulated.inputTokens + accumulated.outputTokens;\n }\n}\n\n/**\n * Get the span associated with a tool call ID\n */\nexport function _INTERNAL_getSpanForToolCallId(toolCallId: string): Span | undefined {\n return toolCallSpanMap.get(toolCallId);\n}\n\n/**\n * Clean up the span mapping for a tool call ID\n */\nexport function _INTERNAL_cleanupToolCallSpan(toolCallId: string): void {\n toolCallSpanMap.delete(toolCallId);\n}\n\n/**\n * Convert an array of tool strings to a JSON string\n */\nexport function convertAvailableToolsToJsonString(tools: unknown[]): string {\n const toolObjects = tools.map(tool => {\n if (typeof tool === 'string') {\n try {\n return JSON.parse(tool);\n } catch {\n return tool;\n }\n }\n return tool;\n });\n return JSON.stringify(toolObjects);\n}\n\n/**\n * Filter out invalid entries in messages array\n * @param input - The input array to filter\n * @returns The filtered array\n */\nfunction filterMessagesArray(input: unknown[]): { role: string; content: string }[] {\n return input.filter(\n (m: unknown): m is { role: string; content: string } =>\n !!m && typeof m === 'object' && 'role' in m && 'content' in m,\n );\n}\n\n/**\n * Normalize the user input (stringified object with prompt, system, messages) to messages array\n */\nexport function convertUserInputToMessagesFormat(userInput: string): { role: string; content: string }[] {\n try {\n const p = JSON.parse(userInput);\n if (!!p && typeof p === 'object') {\n let { messages } = p;\n const { prompt, system } = p;\n const result: { role: string; content: string }[] = [];\n\n // prepend top-level system instruction if present\n if (typeof system === 'string') {\n result.push({ role: 'system', content: system });\n }\n\n // stringified messages array\n if (typeof messages === 'string') {\n try {\n messages = JSON.parse(messages);\n } catch {\n // ignore parse errors\n }\n }\n\n // messages array format: { messages: [...] }\n if (Array.isArray(messages)) {\n result.push(...filterMessagesArray(messages));\n return result;\n }\n\n // prompt array format: { prompt: [...] }\n if (Array.isArray(prompt)) {\n result.push(...filterMessagesArray(prompt));\n return result;\n }\n\n // prompt string format: { prompt: \"...\" }\n if (typeof prompt === 'string') {\n result.push({ role: 'user', content: prompt });\n }\n\n if (result.length > 0) {\n return result;\n }\n }\n // eslint-disable-next-line no-empty\n } catch {}\n return [];\n}\n\n/**\n * Generate a request.messages JSON array from the prompt field in the\n * invoke_agent op\n */\nexport function requestMessagesFromPrompt(span: Span, attributes: SpanAttributes): void {\n if (\n typeof attributes[AI_PROMPT_ATTRIBUTE] === 'string' &&\n !attributes[GEN_AI_INPUT_MESSAGES_ATTRIBUTE] &&\n !attributes[AI_PROMPT_MESSAGES_ATTRIBUTE]\n ) {\n // No messages array is present, so we need to convert the prompt to the proper messages format\n // This is the case for ai.generateText spans\n // The ai.prompt attribute is a stringified object with prompt, system, messages attributes\n // The format of these is described in the vercel docs, for instance: https://ai-sdk.dev/docs/reference/ai-sdk-core/stream-object#parameters\n const userInput = attributes[AI_PROMPT_ATTRIBUTE];\n const messages = convertUserInputToMessagesFormat(userInput);\n if (messages.length) {\n const { systemInstructions, filteredMessages } = extractSystemInstructions(messages);\n\n if (systemInstructions) {\n span.setAttribute(GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE, systemInstructions);\n }\n\n const filteredLength = Array.isArray(filteredMessages) ? filteredMessages.length : 0;\n const truncatedMessages = getTruncatedJsonString(filteredMessages);\n\n span.setAttributes({\n [AI_PROMPT_ATTRIBUTE]: truncatedMessages,\n [GEN_AI_INPUT_MESSAGES_ATTRIBUTE]: truncatedMessages,\n [GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE]: filteredLength,\n });\n }\n } else if (typeof attributes[AI_PROMPT_MESSAGES_ATTRIBUTE] === 'string') {\n // In this case we already get a properly formatted messages array, this is the preferred way to get the messages\n // This is the case for ai.generateText.doGenerate spans\n try {\n const messages = JSON.parse(attributes[AI_PROMPT_MESSAGES_ATTRIBUTE]);\n if (Array.isArray(messages)) {\n const { systemInstructions, filteredMessages } = extractSystemInstructions(messages);\n\n if (systemInstructions) {\n span.setAttribute(GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE, systemInstructions);\n }\n\n const filteredLength = Array.isArray(filteredMessages) ? filteredMessages.length : 0;\n const truncatedMessages = getTruncatedJsonString(filteredMessages);\n\n span.setAttributes({\n [AI_PROMPT_MESSAGES_ATTRIBUTE]: truncatedMessages,\n [GEN_AI_INPUT_MESSAGES_ATTRIBUTE]: truncatedMessages,\n [GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE]: filteredLength,\n });\n }\n // eslint-disable-next-line no-empty\n } catch {}\n }\n}\n\n/**\n * Maps a Vercel AI span name to the corresponding Sentry op.\n */\nexport function getSpanOpFromName(name: string): string | undefined {\n switch (name) {\n case 'ai.generateText':\n case 'ai.streamText':\n case 'ai.generateObject':\n case 'ai.streamObject':\n case 'ai.embed':\n case 'ai.embedMany':\n case 'ai.rerank':\n return GEN_AI_INVOKE_AGENT_OPERATION_ATTRIBUTE;\n case 'ai.generateText.doGenerate':\n return GEN_AI_GENERATE_TEXT_DO_GENERATE_OPERATION_ATTRIBUTE;\n case 'ai.streamText.doStream':\n return GEN_AI_STREAM_TEXT_DO_STREAM_OPERATION_ATTRIBUTE;\n case 'ai.generateObject.doGenerate':\n return GEN_AI_GENERATE_OBJECT_DO_GENERATE_OPERATION_ATTRIBUTE;\n case 'ai.streamObject.doStream':\n return GEN_AI_STREAM_OBJECT_DO_STREAM_OPERATION_ATTRIBUTE;\n case 'ai.embed.doEmbed':\n return GEN_AI_EMBED_DO_EMBED_OPERATION_ATTRIBUTE;\n case 'ai.embedMany.doEmbed':\n return GEN_AI_EMBED_MANY_DO_EMBED_OPERATION_ATTRIBUTE;\n case 'ai.rerank.doRerank':\n return 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