[Model][2/N] Improve all pooling task | Support multi-vector retrieval (#25370)

Signed-off-by: wang.yuqi <noooop@126.com>

tests/entrypoints/pooling/openai/test_embedding.py

@@ -17,6 +17,7 @@ from tests.utils import RemoteOpenAIServer
 from vllm.entrypoints.openai.protocol import (
     EMBED_DTYPE_TO_TORCH_DTYPE,
     EmbeddingResponse,
+    PoolingResponse,
 )
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
@@ -509,3 +510,20 @@ async def test_normalize(server: RemoteOpenAIServer, model_name: str):
     assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
         "w_normal should be close to normal(wo_normal)."
     )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_pooling(server: RemoteOpenAIServer, model_name: str):
+    input_text = ["The chef prepared a delicious meal."]
+
+    response = requests.post(
+        server.url_for("pooling"),
+        json={"model": model_name, "input": input_text, "encoding_format": "float"},
+    )
+
+    poolings = PoolingResponse.model_validate(response.json())
+
+    assert len(poolings.data) == 1
+    assert len(poolings.data[0].data) == 11
+    assert len(poolings.data[0].data[0]) == 384