feat(core): Support embedding APIs in google-genai

dev-packages/browser-integration-tests/suites/tracing/ai-providers/google-genai/mocks.js

@@ -39,6 +39,24 @@ export class MockGoogleGenAI {
           },
         };
       },
+      embedContent: async (...args) => {
+        const params = args[0];
+        await new Promise(resolve => setTimeout(resolve, 10));
+
+        if (params.model === 'error-model') {
+          const error = new Error('Model not found');
+          error.status = 404;
+          throw error;
+        }
+
+        return {
+          embeddings: [
+            {
+              values: [0.1, 0.2, 0.3, 0.4, 0.5],
+            },
+          ],
+        };
+      },
       generateContentStream: async () => {
         // Return a promise that resolves to an async generator
         return (async function* () {

dev-packages/browser-integration-tests/suites/tracing/ai-providers/google-genai/subject.js

@@ -30,3 +30,11 @@ const response = await chat.sendMessage({
 });
 
 console.log('Received response', response);
+
+// Test embedContent
+const embedResponse = await client.models.embedContent({
+  model: 'text-embedding-004',
+  contents: 'Hello world',
+});
+
+console.log('Received embed response', embedResponse);

dev-packages/node-integration-tests/suites/tracing/google-genai/scenario-embeddings.mjs

@@ -0,0 +1,77 @@
+import { GoogleGenAI } from '@google/genai';
+import * as Sentry from '@sentry/node';
+import express from 'express';
+
+function startMockGoogleGenAIServer() {
+  const app = express();
+  app.use(express.json());
+
+  app.post('/v1beta/models/:model\\:batchEmbedContents', (req, res) => {
+    const model = req.params.model;
+
+    if (model === 'error-model') {
+      res.status(404).set('x-request-id', 'mock-request-123').end('Model not found');
+      return;
+    }
+
+    res.send({
+      embeddings: [
+        {
+          values: [0.1, 0.2, 0.3, 0.4, 0.5],
+        },
+      ],
+    });
+  });
+
+  return new Promise(resolve => {
+    const server = app.listen(0, () => {
+      resolve(server);
+    });
+  });
+}
+
+async function run() {
+  const server = await startMockGoogleGenAIServer();
+
+  await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
+    const client = new GoogleGenAI({
+      apiKey: 'mock-api-key',
+      httpOptions: { baseUrl: `http://localhost:${server.address().port}` },
+    });
+
+    // Test 1: Basic embedContent with string contents
+    await client.models.embedContent({
+      model: 'text-embedding-004',
+      contents: 'What is the capital of France?',
+    });
+
+    // Test 2: Error handling
+    try {
+      await client.models.embedContent({
+        model: 'error-model',
+        contents: 'This will fail',
+      });
+    } catch {
+      // Expected error
+    }
+
+    // Test 3: embedContent with array contents
+    await client.models.embedContent({
+      model: 'text-embedding-004',
+      contents: [
+        {
+          role: 'user',
+          parts: [{ text: 'First input text' }],
+        },
+        {
+          role: 'user',
+          parts: [{ text: 'Second input text' }],
+        },
+      ],
+    });
+  });
+
+  server.close();
+}
+
+run();

dev-packages/node-integration-tests/suites/tracing/google-genai/test.ts

@@ -1,6 +1,7 @@
 import { SEMANTIC_ATTRIBUTE_SENTRY_OP, SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '@sentry/core';
 import { afterAll, describe, expect } from 'vitest';
 import {
+  GEN_AI_EMBEDDINGS_INPUT_ATTRIBUTE,
   GEN_AI_INPUT_MESSAGES_ATTRIBUTE,
   GEN_AI_INPUT_MESSAGES_ORIGINAL_LENGTH_ATTRIBUTE,
   GEN_AI_OPERATION_NAME_ATTRIBUTE,
@@ -601,4 +602,124 @@ describe('Google GenAI integration', () => {
       });
     },
   );
+
+  const EXPECTED_TRANSACTION_DEFAULT_PII_FALSE_EMBEDDINGS = {
+    transaction: 'main',
+    spans: expect.arrayContaining([
+      // First span - embedContent with string contents
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'text-embedding-004',
+        },
+        description: 'embeddings text-embedding-004',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'ok',
+      }),
+      // Second span - embedContent error model
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'error-model',
+        },
+        description: 'embeddings error-model',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'internal_error',
+      }),
+      // Third span - embedContent with array contents
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'text-embedding-004',
+        },
+        description: 'embeddings text-embedding-004',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'ok',
+      }),
+    ]),
+  };
+
+  const EXPECTED_TRANSACTION_DEFAULT_PII_TRUE_EMBEDDINGS = {
+    transaction: 'main',
+    spans: expect.arrayContaining([
+      // First span - embedContent with PII
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'text-embedding-004',
+          [GEN_AI_EMBEDDINGS_INPUT_ATTRIBUTE]: 'What is the capital of France?',
+        },
+        description: 'embeddings text-embedding-004',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'ok',
+      }),
+      // Second span - embedContent error model with PII
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'error-model',
+          [GEN_AI_EMBEDDINGS_INPUT_ATTRIBUTE]: 'This will fail',
+        },
+        description: 'embeddings error-model',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'internal_error',
+      }),
+      // Third span - embedContent with array contents and PII
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.embeddings',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.google_genai',
+          [GEN_AI_SYSTEM_ATTRIBUTE]: 'google_genai',
+          [GEN_AI_REQUEST_MODEL_ATTRIBUTE]: 'text-embedding-004',
+          [GEN_AI_EMBEDDINGS_INPUT_ATTRIBUTE]:
+            '[{"role":"user","parts":[{"text":"First input text"}]},{"role":"user","parts":[{"text":"Second input text"}]}]',
+        },
+        description: 'embeddings text-embedding-004',
+        op: 'gen_ai.embeddings',
+        origin: 'auto.ai.google_genai',
+        status: 'ok',
+      }),
+    ]),
+  };
+
+  createEsmAndCjsTests(__dirname, 'scenario-embeddings.mjs', 'instrument.mjs', (createRunner, test) => {
+    test('creates google genai embeddings spans with sendDefaultPii: false', async () => {
+      await createRunner()
+        .ignore('event')
+        .expect({ transaction: EXPECTED_TRANSACTION_DEFAULT_PII_FALSE_EMBEDDINGS })
+        .start()
+        .completed();
+    });
+  });
+
+  createEsmAndCjsTests(__dirname, 'scenario-embeddings.mjs', 'instrument-with-pii.mjs', (createRunner, test) => {
+    test('creates google genai embeddings spans with sendDefaultPii: true', async () => {
+      await createRunner()
+        .ignore('event')
+        .expect({ transaction: EXPECTED_TRANSACTION_DEFAULT_PII_TRUE_EMBEDDINGS })
+        .start()
+        .completed();
+    });
+  });
 });

packages/core/src/tracing/ai/utils.ts

@@ -23,6 +23,10 @@ export function getFinalOperationName(methodPath: string): string {
   if (methodPath.includes('generateContent')) {
     return 'generate_content';
   }
+  // Google GenAI: models.embedContent -> embeddings
+  if (methodPath.includes('embedContent')) {
+    return 'embeddings';
+  }
   // Anthropic: models.get/retrieve -> models (metadata retrieval only)
   if (methodPath.includes('models')) {
     return 'models';

packages/core/src/tracing/google-genai/constants.ts

@@ -7,6 +7,7 @@ export const GOOGLE_GENAI_INTEGRATION_NAME = 'Google_GenAI';
 export const GOOGLE_GENAI_INSTRUMENTED_METHODS = [
   'models.generateContent',
   'models.generateContentStream',
+  'models.embedContent',
   'chats.create',
   'sendMessage',
   'sendMessageStream',