Marc Mintel
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{"version":3,"file":"streaming.js","sources":["../../../../src/tracing/anthropic-ai/streaming.ts"],"sourcesContent":["import { captureException } from '../../exports';\nimport { SPAN_STATUS_ERROR } from '../../tracing';\nimport type { Span } from '../../types-hoist/span';\nimport {\n GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE,\n GEN_AI_RESPONSE_ID_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_STREAMING_ATTRIBUTE,\n GEN_AI_RESPONSE_TEXT_ATTRIBUTE,\n GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { setTokenUsageAttributes } from '../ai/utils';\nimport type { AnthropicAiStreamingEvent } from './types';\n\n/**\n * State object used to accumulate information from a stream of Anthropic AI events.\n */\ninterface StreamingState {\n /** Collected response text fragments (for output recording). */\n responseTexts: string[];\n /** Reasons for finishing the response, as reported by the API. */\n finishReasons: string[];\n /** The response ID. */\n responseId: string;\n /** The model name. */\n responseModel: string;\n /** Number of prompt/input tokens used. */\n promptTokens: number | undefined;\n /** Number of completion/output tokens used. */\n completionTokens: number | undefined;\n /** Number of cache creation input tokens used. */\n cacheCreationInputTokens: number | undefined;\n /** Number of cache read input tokens used. */\n cacheReadInputTokens: number | undefined;\n /** Accumulated tool calls (finalized) */\n toolCalls: Array<Record<string, unknown>>;\n /** In-progress tool call blocks keyed by index */\n activeToolBlocks: Record<\n number,\n {\n id?: string;\n name?: string;\n inputJsonParts: string[];\n }\n >;\n}\n\n/**\n * Checks if an event is an error event\n * @param event - The event to process\n * @param state - The state of the streaming process\n * @param recordOutputs - Whether to record outputs\n * @param span - The span to update\n * @returns Whether an error occurred\n */\n\nfunction isErrorEvent(event: AnthropicAiStreamingEvent, span: Span): boolean {\n if ('type' in event && typeof event.type === 'string') {\n // If the event is an error, set the span status and capture the error\n // These error events are not rejected by the API by default, but are sent as metadata of the response\n if (event.type === 'error') {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: event.error?.type ?? 'internal_error' });\n captureException(event.error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.anthropic.anthropic_error',\n },\n });\n return true;\n }\n }\n return false;\n}\n\n/**\n * Processes the message metadata of an event\n * @param event - The event to process\n * @param state - The state of the streaming process\n */\n\nfunction handleMessageMetadata(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n // The token counts shown in the usage field of the message_delta event are cumulative.\n // @see https://docs.anthropic.com/en/docs/build-with-claude/streaming#event-types\n if (event.type === 'message_delta' && event.usage) {\n if ('output_tokens' in event.usage && typeof event.usage.output_tokens === 'number') {\n state.completionTokens = event.usage.output_tokens;\n }\n }\n\n if (event.message) {\n const message = event.message;\n\n if (message.id) state.responseId = message.id;\n if (message.model) state.responseModel = message.model;\n if (message.stop_reason) state.finishReasons.push(message.stop_reason);\n\n if (message.usage) {\n if (typeof message.usage.input_tokens === 'number') state.promptTokens = message.usage.input_tokens;\n if (typeof message.usage.cache_creation_input_tokens === 'number')\n state.cacheCreationInputTokens = message.usage.cache_creation_input_tokens;\n if (typeof message.usage.cache_read_input_tokens === 'number')\n state.cacheReadInputTokens = message.usage.cache_read_input_tokens;\n }\n }\n}\n\n/**\n * Handle start of a content block (e.g., tool_use)\n */\nfunction handleContentBlockStart(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n if (event.type !== 'content_block_start' || typeof event.index !== 'number' || !event.content_block) return;\n if (event.content_block.type === 'tool_use' || event.content_block.type === 'server_tool_use') {\n state.activeToolBlocks[event.index] = {\n id: event.content_block.id,\n name: event.content_block.name,\n inputJsonParts: [],\n };\n }\n}\n\n/**\n * Handle deltas of a content block, including input_json_delta for tool_use\n */\nfunction handleContentBlockDelta(\n event: AnthropicAiStreamingEvent,\n state: StreamingState,\n recordOutputs: boolean,\n): void {\n if (event.type !== 'content_block_delta' || !event.delta) return;\n\n // Accumulate tool_use input JSON deltas only when we have an index and an active tool block\n if (\n typeof event.index === 'number' &&\n 'partial_json' in event.delta &&\n typeof event.delta.partial_json === 'string'\n ) {\n const active = state.activeToolBlocks[event.index];\n if (active) {\n active.inputJsonParts.push(event.delta.partial_json);\n }\n }\n\n // Accumulate streamed response text regardless of index\n if (recordOutputs && typeof event.delta.text === 'string') {\n state.responseTexts.push(event.delta.text);\n }\n}\n\n/**\n * Handle stop of a content block; finalize tool_use entries\n */\nfunction handleContentBlockStop(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n if (event.type !== 'content_block_stop' || typeof event.index !== 'number') return;\n\n const active = state.activeToolBlocks[event.index];\n if (!active) return;\n\n const raw = active.inputJsonParts.join('');\n let parsedInput: unknown;\n\n try {\n parsedInput = raw ? JSON.parse(raw) : {};\n } catch {\n parsedInput = { __unparsed: raw };\n }\n\n state.toolCalls.push({\n type: 'tool_use',\n id: active.id,\n name: active.name,\n input: parsedInput,\n });\n\n // eslint-disable-next-line @typescript-eslint/no-dynamic-delete\n delete state.activeToolBlocks[event.index];\n}\n\n/**\n * Processes an event\n * @param event - The event to process\n * @param state - The state of the streaming process\n * @param recordOutputs - Whether to record outputs\n * @param span - The span to update\n */\nfunction processEvent(\n event: AnthropicAiStreamingEvent,\n state: StreamingState,\n recordOutputs: boolean,\n span: Span,\n): void {\n if (!(event && typeof event === 'object')) {\n return;\n }\n\n const isError = isErrorEvent(event, span);\n if (isError) return;\n\n handleMessageMetadata(event, state);\n\n // Tool call events are sent via 3 separate events:\n // - content_block_start (start of the tool call)\n // - content_block_delta (delta aka input of the tool call)\n // - content_block_stop (end of the tool call)\n // We need to handle them all to capture the full tool call.\n handleContentBlockStart(event, state);\n handleContentBlockDelta(event, state, recordOutputs);\n handleContentBlockStop(event, state);\n}\n\n/**\n * Finalizes span attributes when stream processing completes\n */\nfunction finalizeStreamSpan(state: StreamingState, span: Span, recordOutputs: boolean): void {\n if (!span.isRecording()) {\n return;\n }\n\n // Set common response attributes if available\n if (state.responseId) {\n span.setAttributes({\n [GEN_AI_RESPONSE_ID_ATTRIBUTE]: state.responseId,\n });\n }\n if (state.responseModel) {\n span.setAttributes({\n [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: state.responseModel,\n });\n }\n\n setTokenUsageAttributes(\n span,\n state.promptTokens,\n state.completionTokens,\n state.cacheCreationInputTokens,\n state.cacheReadInputTokens,\n );\n\n span.setAttributes({\n [GEN_AI_RESPONSE_STREAMING_ATTRIBUTE]: true,\n });\n\n if (state.finishReasons.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify(state.finishReasons),\n });\n }\n\n if (recordOutputs && state.responseTexts.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: state.responseTexts.join(''),\n });\n }\n\n // Set tool calls if any were captured\n if (recordOutputs && state.toolCalls.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(state.toolCalls),\n });\n }\n\n span.end();\n}\n\n/**\n * Instruments an async iterable stream of Anthropic events, updates the span with\n * streaming attributes and (optionally) the aggregated output text, and yields\n * each event from the input stream unchanged.\n */\nexport async function* instrumentAsyncIterableStream(\n stream: AsyncIterable<AnthropicAiStreamingEvent>,\n span: Span,\n recordOutputs: boolean,\n): AsyncGenerator<AnthropicAiStreamingEvent, void, unknown> {\n const state: StreamingState = {\n responseTexts: [],\n finishReasons: [],\n responseId: '',\n responseModel: '',\n promptTokens: undefined,\n completionTokens: undefined,\n cacheCreationInputTokens: undefined,\n cacheReadInputTokens: undefined,\n toolCalls: [],\n activeToolBlocks: {},\n };\n\n try {\n for await (const event of stream) {\n processEvent(event, state, recordOutputs, span);\n yield event;\n }\n } finally {\n // Set common response attributes if available\n if (state.responseId) {\n span.setAttributes({\n [GEN_AI_RESPONSE_ID_ATTRIBUTE]: state.responseId,\n });\n }\n if (state.responseModel) {\n span.setAttributes({\n [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: state.responseModel,\n });\n }\n\n setTokenUsageAttributes(\n span,\n state.promptTokens,\n state.completionTokens,\n state.cacheCreationInputTokens,\n state.cacheReadInputTokens,\n );\n\n span.setAttributes({\n [GEN_AI_RESPONSE_STREAMING_ATTRIBUTE]: true,\n });\n\n if (state.finishReasons.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify(state.finishReasons),\n });\n }\n\n if (recordOutputs && state.responseTexts.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: state.responseTexts.join(''),\n });\n }\n\n // Set tool calls if any were captured\n if (recordOutputs && state.toolCalls.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(state.toolCalls),\n });\n }\n\n span.end();\n }\n}\n\n/**\n * Instruments a MessageStream by registering event handlers and preserving the original stream API.\n */\nexport function instrumentMessageStream<R extends { on: (...args: unknown[]) => void }>(\n stream: R,\n span: Span,\n recordOutputs: boolean,\n): R {\n const state: StreamingState = {\n responseTexts: [],\n finishReasons: [],\n responseId: '',\n responseModel: '',\n promptTokens: undefined,\n completionTokens: undefined,\n cacheCreationInputTokens: undefined,\n cacheReadInputTokens: undefined,\n toolCalls: [],\n activeToolBlocks: {},\n };\n\n stream.on('streamEvent', (event: unknown) => {\n processEvent(event as AnthropicAiStreamingEvent, state, recordOutputs, span);\n });\n\n // The event fired when a message is done being streamed by the API. Corresponds to the message_stop SSE event.\n // @see https://github.com/anthropics/anthropic-sdk-typescript/blob/d3be31f5a4e6ebb4c0a2f65dbb8f381ae73a9166/helpers.md?plain=1#L42-L44\n stream.on('message', () => {\n finalizeStreamSpan(state, span, recordOutputs);\n });\n\n stream.on('error', (error: unknown) => {\n captureException(error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.anthropic.stream_error',\n },\n });\n\n if (span.isRecording()) {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: 'stream_error' });\n span.end();\n }\n });\n\n return 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