feat: Add embedding support to sgl backend (#3427)

Signed-off-by: krishung5 <krish@nvidia.com>

tests/utils/payloads.py

@@ -149,6 +149,47 @@ class CompletionPayload(BasePayload):
         return CompletionPayload.extract_text(response)
 
 
+@dataclass
+class EmbeddingPayload(BasePayload):
+    """Payload for embeddings endpoint."""
+
+    endpoint: str = "/v1/embeddings"
+
+    @staticmethod
+    def extract_embeddings(response):
+        """
+        Process embeddings API responses.
+        """
+        response.raise_for_status()
+        result = response.json()
+        assert "object" in result, "Missing 'object' in response"
+        assert (
+            result["object"] == "list"
+        ), f"Expected object='list', got {result['object']}"
+        assert "data" in result, "Missing 'data' in response"
+        assert len(result["data"]) > 0, "Empty data in response"
+
+        # Extract embedding vectors and validate structure
+        embeddings = []
+        for item in result["data"]:
+            assert "object" in item, "Missing 'object' in embedding item"
+            assert (
+                item["object"] == "embedding"
+            ), f"Expected object='embedding', got {item['object']}"
+            assert "embedding" in item, "Missing 'embedding' vector in item"
+            assert isinstance(
+                item["embedding"], list
+            ), "Embedding should be a list of floats"
+            assert len(item["embedding"]) > 0, "Embedding vector should not be empty"
+            embeddings.append(item["embedding"])
+
+        # Return a summary string for validation
+        return f"Generated {len(embeddings)} embeddings with dimension {len(embeddings[0])}"
+
+    def response_handler(self, response: Any) -> str:
+        return EmbeddingPayload.extract_embeddings(response)
+
+
 @dataclass
 class MetricsPayload(BasePayload):
     endpoint: str = "/metrics"