Marc Mintel
5397309103
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{"version":3,"file":"index.js","sources":["../../../../src/tracing/vercel-ai/index.ts"],"sourcesContent":["import type { Client } from '../../client';\nimport { SEMANTIC_ATTRIBUTE_SENTRY_OP, SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '../../semanticAttributes';\nimport type { Event } from '../../types-hoist/event';\nimport type { Span, SpanAttributes, SpanAttributeValue, SpanJSON, SpanOrigin } from '../../types-hoist/span';\nimport { spanToJSON } from '../../utils/spanUtils';\nimport {\n GEN_AI_INPUT_MESSAGES_ATTRIBUTE,\n GEN_AI_OPERATION_NAME_ATTRIBUTE,\n GEN_AI_REQUEST_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_TOOL_CALL_ID_ATTRIBUTE,\n GEN_AI_TOOL_INPUT_ATTRIBUTE,\n GEN_AI_TOOL_NAME_ATTRIBUTE,\n GEN_AI_TOOL_OUTPUT_ATTRIBUTE,\n GEN_AI_TOOL_TYPE_ATTRIBUTE,\n GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_INPUT_TOKENS_CACHE_WRITE_ATTRIBUTE,\n GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE,\n GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { EMBEDDINGS_OPS, GENERATE_CONTENT_OPS, INVOKE_AGENT_OPS, RERANK_OPS, toolCallSpanMap } from './constants';\nimport type { TokenSummary } from './types';\nimport {\n accumulateTokensForParent,\n applyAccumulatedTokens,\n convertAvailableToolsToJsonString,\n getSpanOpFromName,\n requestMessagesFromPrompt,\n} from './utils';\nimport type { OpenAiProviderMetadata, ProviderMetadata } from './vercel-ai-attributes';\nimport {\n AI_MODEL_ID_ATTRIBUTE,\n AI_OPERATION_ID_ATTRIBUTE,\n AI_PROMPT_MESSAGES_ATTRIBUTE,\n AI_PROMPT_TOOLS_ATTRIBUTE,\n AI_RESPONSE_OBJECT_ATTRIBUTE,\n AI_RESPONSE_PROVIDER_METADATA_ATTRIBUTE,\n AI_RESPONSE_TEXT_ATTRIBUTE,\n AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n AI_SCHEMA_ATTRIBUTE,\n AI_TELEMETRY_FUNCTION_ID_ATTRIBUTE,\n AI_TOOL_CALL_ARGS_ATTRIBUTE,\n AI_TOOL_CALL_ID_ATTRIBUTE,\n AI_TOOL_CALL_NAME_ATTRIBUTE,\n AI_TOOL_CALL_RESULT_ATTRIBUTE,\n AI_USAGE_CACHED_INPUT_TOKENS_ATTRIBUTE,\n AI_USAGE_COMPLETION_TOKENS_ATTRIBUTE,\n AI_USAGE_PROMPT_TOKENS_ATTRIBUTE,\n OPERATION_NAME_ATTRIBUTE,\n} from './vercel-ai-attributes';\n\nfunction addOriginToSpan(span: Span, origin: SpanOrigin): void {\n span.setAttribute(SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN, origin);\n}\n\n/**\n * Maps Vercel AI SDK operation names to OpenTelemetry semantic convention values\n * @see https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#llm-request-spans\n */\nfunction mapVercelAiOperationName(operationName: string): string {\n // Top-level pipeline operations map to invoke_agent\n if (INVOKE_AGENT_OPS.has(operationName)) {\n return 'invoke_agent';\n }\n // .do* operations are the actual LLM calls\n if (GENERATE_CONTENT_OPS.has(operationName)) {\n return 'generate_content';\n }\n if (EMBEDDINGS_OPS.has(operationName)) {\n return 'embeddings';\n }\n if (RERANK_OPS.has(operationName)) {\n return 'rerank';\n }\n if (operationName === 'ai.toolCall') {\n return 'execute_tool';\n }\n // Return the original value for unknown operations\n return operationName;\n}\n\n/**\n * Post-process spans emitted by the Vercel AI SDK.\n * This is supposed to be used in `client.on('spanStart', ...)\n */\nfunction onVercelAiSpanStart(span: Span): void {\n const { data: attributes, description: name } = spanToJSON(span);\n\n if (!name) {\n return;\n }\n\n // Tool call spans\n // https://ai-sdk.dev/docs/ai-sdk-core/telemetry#tool-call-spans\n if (attributes[AI_TOOL_CALL_NAME_ATTRIBUTE] && attributes[AI_TOOL_CALL_ID_ATTRIBUTE] && name === 'ai.toolCall') {\n processToolCallSpan(span, attributes);\n return;\n }\n\n // V6+ Check if this is a Vercel AI span by checking if the operation ID attribute is present.\n // V5+ Check if this is a Vercel AI span by name pattern.\n if (!attributes[AI_OPERATION_ID_ATTRIBUTE] && !name.startsWith('ai.')) {\n return;\n }\n\n processGenerateSpan(span, name, attributes);\n}\n\nfunction vercelAiEventProcessor(event: Event): Event {\n if (event.type === 'transaction' && event.spans) {\n // Map to accumulate token data by parent span ID\n const tokenAccumulator: Map<string, TokenSummary> = new Map();\n\n // First pass: process all spans and accumulate token data\n for (const span of event.spans) {\n processEndedVercelAiSpan(span);\n\n // Accumulate token data for parent spans\n accumulateTokensForParent(span, tokenAccumulator);\n }\n\n // Second pass: apply accumulated token data to parent spans\n for (const span of event.spans) {\n if (span.op !== 'gen_ai.invoke_agent') {\n continue;\n }\n\n applyAccumulatedTokens(span, tokenAccumulator);\n }\n\n // Also apply to root when it is the invoke_agent pipeline\n const trace = event.contexts?.trace;\n if (trace && trace.op === 'gen_ai.invoke_agent') {\n applyAccumulatedTokens(trace, tokenAccumulator);\n }\n }\n\n return event;\n}\n/**\n * Post-process spans emitted by the Vercel AI SDK.\n */\nfunction processEndedVercelAiSpan(span: SpanJSON): void {\n const { data: attributes, origin } = span;\n\n if (origin !== 'auto.vercelai.otel') {\n return;\n }\n\n renameAttributeKey(attributes, AI_USAGE_COMPLETION_TOKENS_ATTRIBUTE, GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE);\n renameAttributeKey(attributes, AI_USAGE_PROMPT_TOKENS_ATTRIBUTE, GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE);\n renameAttributeKey(attributes, AI_USAGE_CACHED_INPUT_TOKENS_ATTRIBUTE, GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE);\n\n // Parent spans (ai.streamText, ai.streamObject, etc.) use inputTokens/outputTokens instead of promptTokens/completionTokens\n renameAttributeKey(attributes, 'ai.usage.inputTokens', GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE);\n renameAttributeKey(attributes, 'ai.usage.outputTokens', GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE);\n\n // AI SDK uses avgOutputTokensPerSecond, map to our expected attribute name\n renameAttributeKey(attributes, 'ai.response.avgOutputTokensPerSecond', 'ai.response.avgCompletionTokensPerSecond');\n\n // Input tokens is the sum of prompt tokens and cached input tokens\n if (\n typeof attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE] === 'number' &&\n typeof attributes[GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE] === 'number'\n ) {\n attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE] =\n attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE] + attributes[GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE];\n }\n\n if (\n typeof attributes[GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE] === 'number' &&\n typeof attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE] === 'number'\n ) {\n attributes[GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE] =\n attributes[GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE] + attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE];\n }\n\n // Convert the available tools array to a JSON string\n if (attributes[AI_PROMPT_TOOLS_ATTRIBUTE] && Array.isArray(attributes[AI_PROMPT_TOOLS_ATTRIBUTE])) {\n attributes[AI_PROMPT_TOOLS_ATTRIBUTE] = convertAvailableToolsToJsonString(\n attributes[AI_PROMPT_TOOLS_ATTRIBUTE] as unknown[],\n );\n }\n\n // Rename AI SDK attributes to standardized gen_ai attributes\n // Map operation.name to OpenTelemetry semantic convention values\n if (attributes[OPERATION_NAME_ATTRIBUTE]) {\n const operationName = mapVercelAiOperationName(attributes[OPERATION_NAME_ATTRIBUTE] as string);\n attributes[GEN_AI_OPERATION_NAME_ATTRIBUTE] = operationName;\n // eslint-disable-next-line @typescript-eslint/no-dynamic-delete\n delete attributes[OPERATION_NAME_ATTRIBUTE];\n }\n renameAttributeKey(attributes, AI_PROMPT_MESSAGES_ATTRIBUTE, GEN_AI_INPUT_MESSAGES_ATTRIBUTE);\n renameAttributeKey(attributes, AI_RESPONSE_TEXT_ATTRIBUTE, 'gen_ai.response.text');\n renameAttributeKey(attributes, AI_RESPONSE_TOOL_CALLS_ATTRIBUTE, 'gen_ai.response.tool_calls');\n renameAttributeKey(attributes, AI_RESPONSE_OBJECT_ATTRIBUTE, 'gen_ai.response.object');\n renameAttributeKey(attributes, AI_PROMPT_TOOLS_ATTRIBUTE, 'gen_ai.request.available_tools');\n\n renameAttributeKey(attributes, AI_TOOL_CALL_ARGS_ATTRIBUTE, GEN_AI_TOOL_INPUT_ATTRIBUTE);\n renameAttributeKey(attributes, AI_TOOL_CALL_RESULT_ATTRIBUTE, GEN_AI_TOOL_OUTPUT_ATTRIBUTE);\n\n renameAttributeKey(attributes, AI_SCHEMA_ATTRIBUTE, 'gen_ai.request.schema');\n renameAttributeKey(attributes, AI_MODEL_ID_ATTRIBUTE, GEN_AI_REQUEST_MODEL_ATTRIBUTE);\n\n addProviderMetadataToAttributes(attributes);\n\n // Change attributes namespaced with `ai.X` to `vercel.ai.X`\n for (const key of Object.keys(attributes)) {\n if (key.startsWith('ai.')) {\n renameAttributeKey(attributes, key, `vercel.${key}`);\n }\n }\n}\n\n/**\n * Renames an attribute key in the provided attributes object if the old key exists.\n * This function safely handles null and undefined values.\n */\nfunction renameAttributeKey(attributes: Record<string, unknown>, oldKey: string, newKey: string): void {\n if (attributes[oldKey] != null) {\n attributes[newKey] = attributes[oldKey];\n // eslint-disable-next-line @typescript-eslint/no-dynamic-delete\n delete attributes[oldKey];\n }\n}\n\nfunction processToolCallSpan(span: Span, attributes: SpanAttributes): void {\n addOriginToSpan(span, 'auto.vercelai.otel');\n span.setAttribute(SEMANTIC_ATTRIBUTE_SENTRY_OP, 'gen_ai.execute_tool');\n span.setAttribute(GEN_AI_OPERATION_NAME_ATTRIBUTE, 'execute_tool');\n renameAttributeKey(attributes, AI_TOOL_CALL_NAME_ATTRIBUTE, GEN_AI_TOOL_NAME_ATTRIBUTE);\n renameAttributeKey(attributes, AI_TOOL_CALL_ID_ATTRIBUTE, GEN_AI_TOOL_CALL_ID_ATTRIBUTE);\n\n // Store the span in our global map using the tool call ID\n // This allows us to capture tool errors and link them to the correct span\n const toolCallId = attributes[GEN_AI_TOOL_CALL_ID_ATTRIBUTE];\n\n if (typeof toolCallId === 'string') {\n toolCallSpanMap.set(toolCallId, span);\n }\n\n // https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/#gen-ai-tool-type\n if (!attributes[GEN_AI_TOOL_TYPE_ATTRIBUTE]) {\n span.setAttribute(GEN_AI_TOOL_TYPE_ATTRIBUTE, 'function');\n }\n const toolName = attributes[GEN_AI_TOOL_NAME_ATTRIBUTE];\n if (toolName) {\n span.updateName(`execute_tool ${toolName}`);\n }\n}\n\nfunction processGenerateSpan(span: Span, name: string, attributes: SpanAttributes): void {\n addOriginToSpan(span, 'auto.vercelai.otel');\n\n const nameWthoutAi = name.replace('ai.', '');\n span.setAttribute('ai.pipeline.name', nameWthoutAi);\n span.updateName(nameWthoutAi);\n\n // If a telemetry name is set and the span represents a pipeline, use it as the operation name.\n // This name can be set at the request level by adding `experimental_telemetry.functionId`.\n const functionId = attributes[AI_TELEMETRY_FUNCTION_ID_ATTRIBUTE];\n if (functionId && typeof functionId === 'string') {\n span.updateName(`${nameWthoutAi} ${functionId}`);\n span.setAttribute('gen_ai.function_id', functionId);\n }\n\n requestMessagesFromPrompt(span, attributes);\n\n if (attributes[AI_MODEL_ID_ATTRIBUTE] && !attributes[GEN_AI_RESPONSE_MODEL_ATTRIBUTE]) {\n span.setAttribute(GEN_AI_RESPONSE_MODEL_ATTRIBUTE, attributes[AI_MODEL_ID_ATTRIBUTE]);\n }\n span.setAttribute('ai.streaming', name.includes('stream'));\n\n // Set the op based on the span name\n const op = getSpanOpFromName(name);\n if (op) {\n span.setAttribute(SEMANTIC_ATTRIBUTE_SENTRY_OP, op);\n }\n\n // Update span names for .do* spans to include the model ID (only if model ID exists)\n const modelId = attributes[AI_MODEL_ID_ATTRIBUTE];\n if (modelId) {\n switch (name) {\n case 'ai.generateText.doGenerate':\n span.updateName(`generate_text ${modelId}`);\n break;\n case 'ai.streamText.doStream':\n span.updateName(`stream_text ${modelId}`);\n break;\n case 'ai.generateObject.doGenerate':\n span.updateName(`generate_object ${modelId}`);\n break;\n case 'ai.streamObject.doStream':\n span.updateName(`stream_object ${modelId}`);\n break;\n case 'ai.embed.doEmbed':\n span.updateName(`embed ${modelId}`);\n break;\n case 'ai.embedMany.doEmbed':\n span.updateName(`embed_many ${modelId}`);\n break;\n case 'ai.rerank.doRerank':\n span.updateName(`rerank ${modelId}`);\n break;\n }\n }\n}\n\n/**\n * Add event processors to the given client to process Vercel AI spans.\n */\nexport function addVercelAiProcessors(client: Client): void {\n client.on('spanStart', onVercelAiSpanStart);\n // Note: We cannot do this on `spanEnd`, because the span cannot be mutated anymore at this point\n client.addEventProcessor(Object.assign(vercelAiEventProcessor, { id: 'VercelAiEventProcessor' }));\n}\n\nfunction addProviderMetadataToAttributes(attributes: SpanAttributes): void {\n const providerMetadata = attributes[AI_RESPONSE_PROVIDER_METADATA_ATTRIBUTE] as string | undefined;\n if (providerMetadata) {\n try {\n const providerMetadataObject = JSON.parse(providerMetadata) as ProviderMetadata;\n\n // Handle OpenAI metadata (v5 uses 'openai', v6 Azure Responses API uses 'azure')\n const openaiMetadata: OpenAiProviderMetadata | undefined =\n providerMetadataObject.openai ?? providerMetadataObject.azure;\n if (openaiMetadata) {\n setAttributeIfDefined(\n attributes,\n GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE,\n openaiMetadata.cachedPromptTokens,\n );\n setAttributeIfDefined(attributes, 'gen_ai.usage.output_tokens.reasoning', openaiMetadata.reasoningTokens);\n setAttributeIfDefined(\n attributes,\n 'gen_ai.usage.output_tokens.prediction_accepted',\n openaiMetadata.acceptedPredictionTokens,\n );\n setAttributeIfDefined(\n attributes,\n 'gen_ai.usage.output_tokens.prediction_rejected',\n openaiMetadata.rejectedPredictionTokens,\n );\n setAttributeIfDefined(attributes, 'gen_ai.conversation.id', openaiMetadata.responseId);\n }\n\n if (providerMetadataObject.anthropic) {\n const cachedInputTokens =\n providerMetadataObject.anthropic.usage?.cache_read_input_tokens ??\n providerMetadataObject.anthropic.cacheReadInputTokens;\n setAttributeIfDefined(attributes, GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE, cachedInputTokens);\n\n const cacheWriteInputTokens =\n providerMetadataObject.anthropic.usage?.cache_creation_input_tokens ??\n providerMetadataObject.anthropic.cacheCreationInputTokens;\n setAttributeIfDefined(attributes, GEN_AI_USAGE_INPUT_TOKENS_CACHE_WRITE_ATTRIBUTE, cacheWriteInputTokens);\n }\n\n if (providerMetadataObject.bedrock?.usage) {\n setAttributeIfDefined(\n attributes,\n GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE,\n providerMetadataObject.bedrock.usage.cacheReadInputTokens,\n );\n setAttributeIfDefined(\n attributes,\n GEN_AI_USAGE_INPUT_TOKENS_CACHE_WRITE_ATTRIBUTE,\n providerMetadataObject.bedrock.usage.cacheWriteInputTokens,\n );\n }\n\n if (providerMetadataObject.deepseek) {\n setAttributeIfDefined(\n attributes,\n GEN_AI_USAGE_INPUT_TOKENS_CACHED_ATTRIBUTE,\n providerMetadataObject.deepseek.promptCacheHitTokens,\n );\n setAttributeIfDefined(\n attributes,\n 'gen_ai.usage.input_tokens.cache_miss',\n providerMetadataObject.deepseek.promptCacheMissTokens,\n );\n }\n } catch {\n // Ignore\n }\n }\n}\n\n/**\n * Sets an attribute only if the value is not null or undefined.\n */\nfunction setAttributeIfDefined(attributes: SpanAttributes, key: string, value: SpanAttributeValue | undefined): void {\n if (value != null) {\n attributes[key] = value;\n 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