add failing tests for embeddings api support

Author: nicohrubec

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

@@ -74,6 +74,28 @@ class MockOpenAI {
         };
       },
     };
+
+    this.embeddings = {
+      create: async params => {
+        await new Promise(resolve => setTimeout(resolve, 10));
+
+        return {
+          object: 'list',
+          data: [
+            {
+              object: 'embedding',
+              embedding: [0.1, 0.2, 0.3],
+              index: 0,
+            },
+          ],
+          model: params.model,
+          usage: {
+            prompt_tokens: 10,
+            total_tokens: 10,
+          },
+        };
+      },
+    };
   }
 
   // Create a mock streaming response for chat completions
@@ -312,6 +334,24 @@ async function run() {
     } catch {
       // Error is expected and handled
     }
+
+    // Seventh test: embeddings API
+    await client.embeddings.create({
+      input: 'Embedding test!',
+      model: 'text-embedding-3-small',
+      dimensions: 1536,
+      encoding_format: 'float',
+    });
+
+    // Eighth test: embeddings API error model
+    try {
+      await client.embeddings.create({
+        input: 'Error embedding test!',
+        model: 'error-model',
+      });
+    } catch {
+      // Error is expected and handled
+    }
   });
 }
 

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

@@ -144,6 +144,26 @@ describe('OpenAI integration', () => {
         origin: 'auto.ai.openai',
         status: 'internal_error',
       }),
+      // Seventh span - embeddings API
+      expect.objectContaining({
+        data: {
+          'gen_ai.operation.name': 'embeddings',
+          'sentry.op': 'gen_ai.embeddings',
+          'sentry.origin': 'auto.ai.openai',
+          'gen_ai.system': 'openai',
+          'gen_ai.request.model': 'text-embedding-3-small',
+        },
+      }),
+      // Eighth span - embeddings API error model
+      expect.objectContaining({
+        data: {
+          'gen_ai.operation.name': 'embeddings',
+          'sentry.op': 'gen_ai.embeddings',
+          'sentry.origin': 'auto.ai.openai',
+          'gen_ai.system': 'openai',
+          'gen_ai.request.model': 'error-model',
+        },
+      }),
     ]),
   };
 
@@ -297,6 +317,26 @@ describe('OpenAI integration', () => {
         origin: 'auto.ai.openai',
         status: 'internal_error',
       }),
+      // Seventh span - embeddings API
+      expect.objectContaining({
+        data: {
+          'gen_ai.operation.name': 'embeddings',
+          'sentry.op': 'gen_ai.embeddings',
+          'sentry.origin': 'auto.ai.openai',
+          'gen_ai.system': 'openai',
+          'gen_ai.request.model': 'text-embedding-3-small',
+        },
+      }),
+      // Eighth span - embeddings API error model
+      expect.objectContaining({
+        data: {
+          'gen_ai.operation.name': 'embeddings',
+          'sentry.op': 'gen_ai.embeddings',
+          'sentry.origin': 'auto.ai.openai',
+          'gen_ai.system': 'openai',
+          'gen_ai.request.model': 'error-model',
+        },
+      }),
     ]),
   };
 
@@ -400,7 +440,7 @@ describe('OpenAI integration', () => {
 
   createEsmAndCjsTests(
     __dirname,
-    'scenario-message-truncation-completions.mjs',
+    'truncation/scenario-message-truncation-completions.mjs',
     'instrument-with-pii.mjs',
     (createRunner, test) => {
       test('truncates messages when they exceed byte limit - keeps only last message and crops it', async () => {
@@ -436,7 +476,7 @@ describe('OpenAI integration', () => {
 
   createEsmAndCjsTests(
     __dirname,
-    'scenario-message-truncation-responses.mjs',
+    'truncation/scenario-message-truncation-responses.mjs',
     'instrument-with-pii.mjs',
     (createRunner, test) => {
       test('truncates string inputs when they exceed byte limit', async () => {
@@ -469,4 +509,30 @@ describe('OpenAI integration', () => {
       });
     },
   );
+
+  createEsmAndCjsTests(
+    __dirname,
+    'truncation/scenario-message-truncation-embeddings.mjs',
+    'instrument-with-pii.mjs',
+    (createRunner, test) => {
+      test('truncates messages when they exceed byte limit - keeps only last message and crops it', async () => {
+        await createRunner()
+          .ignore('event')
+          .expect({
+            transaction: {
+              transaction: 'main',
+              spans: expect.arrayContaining([
+                expect.objectContaining({
+                  data: expect.objectContaining({
+                    'gen_ai.operation.name': 'embeddings',
+                  }),
+                }),
+              ]),
+            },
+          })
+          .start()
+          .completed();
+      });
+    },
+  );
 });

dev-packages/node-integration-tests/suites/tracing/openai/scenario-message-truncation-completions.mjs renamed to dev-packages/node-integration-tests/suites/tracing/openai/truncation/scenario-message-truncation-completions.mjs

@@ -0,0 +1,66 @@
+import { instrumentOpenAiClient } from '@sentry/core';
+import * as Sentry from '@sentry/node';
+
+class MockOpenAI {
+  constructor(config) {
+    this.apiKey = config.apiKey;
+
+    this.embeddings = {
+      create: async params => {
+        await new Promise(resolve => setTimeout(resolve, 10));
+
+        return {
+          object: 'list',
+          data: [
+            {
+              object: 'embedding',
+              embedding: [0.1, 0.2, 0.3],
+              index: 0,
+            },
+          ],
+          model: params.model,
+          usage: {
+            prompt_tokens: 10,
+            total_tokens: 10,
+          },
+        };
+      },
+    };
+  }
+}
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
+    const mockClient = new MockOpenAI({
+      apiKey: 'mock-api-key',
+    });
+
+    const client = instrumentOpenAiClient(mockClient);
+
+    // Create 1 large message that gets truncated to fit within the 20KB limit
+    const largeContent = 'A'.repeat(25000) + 'B'.repeat(25000); // ~50KB gets truncated to include only As
+
+    await client.embeddings.create({
+      input: largeContent,
+      model: 'text-embedding-3-small',
+      dimensions: 1536,
+      encoding_format: 'float',
+    });
+
+    // Create 3 large messages where:
+    // - First 2 messages are very large (will be dropped)
+    // - Last message is large but will be truncated to fit within the 20KB limit
+    const largeContent1 = 'A'.repeat(15000); // ~15KB
+    const largeContent2 = 'B'.repeat(15000); // ~15KB
+    const largeContent3 = 'C'.repeat(25000); // ~25KB (will be truncated)
+
+    await client.embeddings.create({
+      input: [largeContent1, largeContent2, largeContent3],
+      model: 'text-embedding-3-small',
+      dimensions: 1536,
+      encoding_format: 'float',
+    });
+  });
+}
+
+run();