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feat(node): Port OpenAI chat completions to orchestrion
Add a diagnostics-channel (orchestrion) instrumentation path for `chat.completions.create` (non-streaming) alongside the existing proxy/OTel path. Reuses the core attribute helpers and `bindTracingChannelToSpan`; the browser/edge keep the proxy `instrumentOpenAiClient`. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Lines changed: 529 additions & 5 deletions

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‎dev-packages/node-integration-tests/suites/tracing/openai/orchestrion/instrument-orchestrion-with-pii.mjs‎

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import * as Sentry from '@sentry/node';
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import { loggingTransport } from '@sentry-internal/node-integration-tests';
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Sentry.experimentalUseDiagnosticsChannelInjection();
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Sentry.init({
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dsn: 'https://public@dsn.ingest.sentry.io/1337',
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release: '1.0',
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tracesSampleRate: 1.0,
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dataCollection: { genAI: { inputs: true, outputs: true } },
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transport: loggingTransport,
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beforeSendTransaction: event => {
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if (event.transaction.includes('/openai/')) {
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return null;
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}
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return event;
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},
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streamGenAiSpans: true,
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});

‎dev-packages/node-integration-tests/suites/tracing/openai/orchestrion/instrument-orchestrion.mjs‎

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// Opting in via `experimentalUseDiagnosticsChannelInjection()` (before `init`)
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// swaps the OTel `OpenAI` instrumentation for the diagnostics-channel one and
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// synchronously installs the module hooks that inject the channels — so `openai`,
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// imported by the scenario afterwards, publishes to `orchestrion:openai:chat`.
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import * as Sentry from '@sentry/node';
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import { loggingTransport } from '@sentry-internal/node-integration-tests';
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Sentry.experimentalUseDiagnosticsChannelInjection();
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Sentry.init({
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dsn: 'https://public@dsn.ingest.sentry.io/1337',
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release: '1.0',
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tracesSampleRate: 1.0,
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dataCollection: { genAI: { inputs: false, outputs: false } },
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transport: loggingTransport,
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beforeSendTransaction: event => {
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if (event.transaction.includes('/openai/')) {
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return null;
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}
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return event;
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},
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streamGenAiSpans: true,
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});

‎dev-packages/node-integration-tests/suites/tracing/openai/orchestrion/scenario-orchestrion-chat.mjs‎

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import * as Sentry from '@sentry/node';
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import express from 'express';
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import OpenAI from 'openai';
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function startMockServer() {
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const app = express();
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app.use(express.json());
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app.post('/openai/chat/completions', (req, res) => {
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const { model } = req.body;
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if (model === 'error-model') {
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res.status(500).set('x-request-id', 'mock-request-error').end('Internal server error');
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return;
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}
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res.send({
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id: 'chatcmpl-mock123',
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object: 'chat.completion',
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created: 1677652288,
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model,
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choices: [
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{
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index: 0,
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message: { role: 'assistant', content: 'Hello from OpenAI mock!' },
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finish_reason: 'stop',
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},
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],
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usage: { prompt_tokens: 10, completion_tokens: 15, total_tokens: 25 },
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});
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});
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return new Promise(resolve => {
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const server = app.listen(0, () => resolve(server));
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});
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}
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async function run() {
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const server = await startMockServer();
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await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
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const client = new OpenAI({
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baseURL: `http://localhost:${server.address().port}/openai`,
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apiKey: 'mock-api-key',
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});
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await client.chat.completions.create({
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model: 'gpt-3.5-turbo',
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messages: [
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{ role: 'system', content: 'You are a helpful assistant.' },
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{ role: 'user', content: 'What is the capital of France?' },
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],
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temperature: 0.7,
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max_tokens: 100,
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});
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try {
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await client.chat.completions.create({
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model: 'error-model',
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messages: [{ role: 'user', content: 'This will fail' }],
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});
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} catch {
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// expected
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}
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});
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server.close();
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}
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run();

‎dev-packages/node-integration-tests/suites/tracing/openai/orchestrion/test.ts‎

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import { SEMANTIC_ATTRIBUTE_SENTRY_OP, SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '@sentry/core';
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import { afterAll, describe, expect } from 'vitest';
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import {
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GEN_AI_INPUT_MESSAGES_ATTRIBUTE,
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GEN_AI_OPERATION_NAME_ATTRIBUTE,
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GEN_AI_REQUEST_MODEL_ATTRIBUTE,
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GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE,
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GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE,
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GEN_AI_RESPONSE_ID_ATTRIBUTE,
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GEN_AI_RESPONSE_MODEL_ATTRIBUTE,
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GEN_AI_RESPONSE_TEXT_ATTRIBUTE,
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GEN_AI_SYSTEM_ATTRIBUTE,
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GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,
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GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,
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GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
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} from '../../../../../../packages/core/src/tracing/ai/gen-ai-attributes';
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import { cleanupChildProcesses, createEsmAndCjsTests } from '../../../../utils/runner';
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// The origin distinguishes the orchestrion (diagnostics-channel) path from the
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// OTel/proxy one (`auto.ai.openai`).
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const ORCHESTRION_ORIGIN = 'auto.ai.orchestrion.openai';
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describe('OpenAI integration (orchestrion)', () => {
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afterAll(() => {
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cleanupChildProcesses();
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});
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createEsmAndCjsTests(
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__dirname,
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'scenario-orchestrion-chat.mjs',
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'instrument-orchestrion.mjs',
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(createRunner, test) => {
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test('creates openai chat spans via the diagnostics-channel path', async () => {
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await createRunner()
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.ignore('event')
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.expect({ transaction: { transaction: 'main' } })
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.expect({
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span: container => {
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const chatSpan = container.items.find(
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span => span.attributes[GEN_AI_RESPONSE_ID_ATTRIBUTE]?.value === 'chatcmpl-mock123',
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);
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expect(chatSpan).toBeDefined();
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expect(chatSpan!.name).toBe('chat gpt-3.5-turbo');
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expect(chatSpan!.status).toBe('ok');
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expect(chatSpan!.attributes[SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]).toEqual({
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type: 'string',
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value: ORCHESTRION_ORIGIN,
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});
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expect(chatSpan!.attributes[SEMANTIC_ATTRIBUTE_SENTRY_OP]).toEqual({
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type: 'string',
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value: 'gen_ai.chat',
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});
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expect(chatSpan!.attributes[GEN_AI_OPERATION_NAME_ATTRIBUTE]).toEqual({ type: 'string', value: 'chat' });
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expect(chatSpan!.attributes[GEN_AI_SYSTEM_ATTRIBUTE]).toEqual({ type: 'string', value: 'openai' });
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expect(chatSpan!.attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE]).toEqual({
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type: 'string',
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value: 'gpt-3.5-turbo',
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});
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expect(chatSpan!.attributes[GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE]).toEqual({
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type: 'double',
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value: 0.7,
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});
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expect(chatSpan!.attributes[GEN_AI_RESPONSE_MODEL_ATTRIBUTE]).toEqual({
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type: 'string',
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value: 'gpt-3.5-turbo',
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});
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expect(chatSpan!.attributes[GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]).toEqual({
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type: 'string',
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value: '["stop"]',
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});
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expect(chatSpan!.attributes[GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE]).toEqual({ type: 'integer', value: 10 });
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expect(chatSpan!.attributes[GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE]).toEqual({
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type: 'integer',
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value: 15,
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});
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expect(chatSpan!.attributes[GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]).toEqual({ type: 'integer', value: 25 });
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// Recording disabled: no inputs/outputs captured.
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expect(chatSpan!.attributes[GEN_AI_INPUT_MESSAGES_ATTRIBUTE]).toBeUndefined();
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expect(chatSpan!.attributes[GEN_AI_RESPONSE_TEXT_ATTRIBUTE]).toBeUndefined();
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const errorSpan = container.items.find(span => span.name === 'chat error-model');
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expect(errorSpan).toBeDefined();
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// `bindTracingChannelToSpan` sets the error status message to the OpenAI error's message,
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// so the serialized status is that message rather than the literal 'error' — just assert
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// the span was marked as errored.
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expect(errorSpan!.status).toBeDefined();
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expect(errorSpan!.status).not.toBe('ok');
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expect(errorSpan!.attributes[SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]).toEqual({
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type: 'string',
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value: ORCHESTRION_ORIGIN,
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});
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},
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})
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.start()
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.completed();
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});
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},
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);
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createEsmAndCjsTests(
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__dirname,
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'scenario-orchestrion-chat.mjs',
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'instrument-orchestrion-with-pii.mjs',
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(createRunner, test) => {
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test('records inputs and outputs when PII is enabled', async () => {
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await createRunner()
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.ignore('event')
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.expect({ transaction: { transaction: 'main' } })
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.expect({
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span: container => {
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const chatSpan = container.items.find(
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span => span.attributes[GEN_AI_RESPONSE_ID_ATTRIBUTE]?.value === 'chatcmpl-mock123',
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);
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expect(chatSpan).toBeDefined();
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expect(chatSpan!.attributes[SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]).toEqual({
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type: 'string',
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value: ORCHESTRION_ORIGIN,
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});
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expect(chatSpan!.attributes[GEN_AI_INPUT_MESSAGES_ATTRIBUTE]?.value).toContain(
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'What is the capital of France?',
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);
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expect(chatSpan!.attributes[GEN_AI_RESPONSE_TEXT_ATTRIBUTE]?.value).toContain('Hello from OpenAI mock!');
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},
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})
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.start()
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.completed();
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});
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},
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);
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});

‎packages/core/src/shared-exports.ts‎

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@@ -177,14 +177,15 @@ export * as metrics from './metrics/public-api';
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export type { MetricOptions } from './metrics/public-api';
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export { createConsolaReporter } from './integrations/consola';
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export { addVercelAiProcessors, getProviderMetadataAttributes } from './tracing/vercel-ai';
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export { getTruncatedJsonString, shouldEnableTruncation } from './tracing/ai/utils';
180+
export { getTruncatedJsonString, shouldEnableTruncation, resolveAIRecordingOptions } from './tracing/ai/utils';
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export {
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GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE,
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GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE,
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} from './tracing/ai/gen-ai-attributes';
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export { _INTERNAL_getSpanContextForToolCallId, _INTERNAL_cleanupToolCallSpanContext } from './tracing/vercel-ai/utils';
186186
export { toolCallSpanContextMap as _INTERNAL_toolCallSpanContextMap } from './tracing/vercel-ai/constants';
187-
export { instrumentOpenAiClient } from './tracing/openai';
187+
export { instrumentOpenAiClient, extractRequestAttributes, addRequestAttributes } from './tracing/openai';
188+
export { addResponseAttributes, extractRequestParameters } from './tracing/openai/utils';
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export { OPENAI_INTEGRATION_NAME } from './tracing/openai/constants';
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export { instrumentAnthropicAiClient } from './tracing/anthropic-ai';
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export { ANTHROPIC_AI_INTEGRATION_NAME } from './tracing/anthropic-ai/constants';

‎packages/core/src/tracing/openai/index.ts‎

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/**
5757
* Extract request attributes from method arguments
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*/
59-
function extractRequestAttributes(args: unknown[], operationName: string): Record<string, unknown> {
59+
export function extractRequestAttributes(args: unknown[], operationName: string): Record<string, unknown> {
6060
const attributes: Record<string, unknown> = {
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[GEN_AI_SYSTEM_ATTRIBUTE]: 'openai',
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[GEN_AI_OPERATION_NAME_ATTRIBUTE]: operationName,
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8080
}
8181

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// Extract and record AI request inputs, if present. This is intentionally separate from response attributes.
83-
function addRequestAttributes(
83+
export function addRequestAttributes(
8484
span: Span,
8585
params: Record<string, unknown>,
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operationName: string,

‎packages/node/src/sdk/experimentalUseDiagnosticsChannelInjection.ts‎

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11
import {
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mysqlChannelIntegration,
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lruMemoizerChannelIntegration,
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openaiChannelIntegration,
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detectOrchestrionSetup,
56
} from '@sentry/server-utils/orchestrion';
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import { registerDiagnosticsChannelInjection } from '@sentry/server-utils/orchestrion/register';
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4142
*/
4243
export function experimentalUseDiagnosticsChannelInjection(): void {
4344
setDiagnosticsChannelInjectionLoader((): DiagnosticsChannelInjection => {
44-
const integrations = [mysqlChannelIntegration(), lruMemoizerChannelIntegration()] as const;
45+
const integrations = [
46+
mysqlChannelIntegration(),
47+
lruMemoizerChannelIntegration(),
48+
openaiChannelIntegration(),
49+
] as const;
4550
const replacedOtelIntegrationNames = integrations.map(i => i.name);
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return {

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