Revert embedding integration tests from 5f3a47d8a (#15914)

sgl-model-gateway/py_test/e2e_grpc/basic/test_embedding_server.py

@@ -1,110 +0,0 @@
-"""
-gRPC Router E2E Test - Embedding Server
-
-Test the embedding functionality of the gRPC router.
-"""
-
-import sys
-import unittest
-from pathlib import Path
-
-import openai
-
-_TEST_DIR = Path(__file__).parent
-sys.path.insert(0, str(_TEST_DIR.parent))
-from fixtures import popen_launch_workers_and_router
-from util import (
-    DEFAULT_EMBEDDING_MODEL_PATH,
-    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-    DEFAULT_URL_FOR_TEST,
-    CustomTestCase,
-    kill_process_tree,
-)
-
-
-class TestEmbeddingServer(CustomTestCase):
-    """
-    Test Embedding API through gRPC router.
-    """
-
-    @classmethod
-    def setUpClass(cls):
-        cls.model = DEFAULT_EMBEDDING_MODEL_PATH
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        cls.api_key = "sk-123456"
-
-        # Launch workers with --is-embedding flag
-        cls.cluster = popen_launch_workers_and_router(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            num_workers=1,
-            tp_size=1,
-            policy="round_robin",
-            api_key=cls.api_key,
-            worker_args=["--is-embedding"],
-        )
-
-        cls.base_url += "/v1"
-
-    @classmethod
-    def tearDownClass(cls):
-        # Cleanup router and workers
-        kill_process_tree(cls.cluster["router"].pid)
-        for worker in cls.cluster.get("workers", []):
-            kill_process_tree(worker.pid)
-
-    def test_embedding(self):
-        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
-
-        input_text = "Hello world"
-        response = client.embeddings.create(
-            model=self.model,
-            input=input_text,
-        )
-
-        assert response.object == "list"
-        assert len(response.data) == 1
-        embedding = response.data[0]
-        assert embedding.object == "embedding"
-        assert embedding.index == 0
-        assert len(embedding.embedding) > 0
-        assert isinstance(embedding.embedding[0], float)
-
-        # Verify usage statistics
-        assert response.usage.prompt_tokens > 0
-        assert response.usage.total_tokens == response.usage.prompt_tokens
-
-    def test_embedding_batch(self):
-        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
-
-        input_texts = ["Hello world", "SGLang is fast"]
-        response = client.embeddings.create(
-            model=self.model,
-            input=input_texts,
-        )
-
-        assert len(response.data) == 1
-        assert response.data[0].index == 0
-        assert len(response.data[0].embedding) > 0
-
-    def test_embedding_dimensions(self):
-        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
-
-        response1 = client.embeddings.create(
-            model=self.model,
-            input="A short text",
-        )
-        dim1 = len(response1.data[0].embedding)
-
-        response2 = client.embeddings.create(
-            model=self.model,
-            input="A much longer text to ensure dimensions match",
-        )
-        dim2 = len(response2.data[0].embedding)
-
-        assert dim1 == dim2
-
-
-if __name__ == "__main__":
-    unittest.main()

sgl-model-gateway/py_test/e2e_grpc/util.py

@@ -92,9 +92,6 @@ DEFAULT_MISTRAL_FUNCTION_CALLING_MODEL_PATH = _get_model_path(
 # GPT-OSS models
 DEFAULT_GPT_OSS_MODEL_PATH = _get_model_path("openai/gpt-oss-20b")
 
-# Embedding models
-DEFAULT_EMBEDDING_MODEL_PATH = _get_model_path("intfloat/e5-mistral-7b-instruct")
-
 
 # ============================================================================
 # Process Management