Refine OpenAI serving entrypoint to remove batch requests (#7372)

Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>
Co-authored-by: Chang Su <csu272@usc.edu>

python/sglang/srt/entrypoints/openai/serving_embedding.py

@@ -54,35 +54,25 @@ class OpenAIServingEmbedding(OpenAIServingBase):
                         return f"All items in input list must be integers"
                     if item < 0:
                         return f"Token ID at index {i} must be non-negative"
-            elif isinstance(first_item, list):
-                # List of lists (multiple token sequences)
-                for i, item in enumerate(input):
-                    if not isinstance(item, list):
-                        return f"Input at index {i} must be a list"
-                    if not item:
-                        return f"Input at index {i} cannot be empty"
-                    if not all(isinstance(token, int) for token in item):
-                        return f"Input at index {i} must contain only integers"
-                    if any(token < 0 for token in item):
-                        return f"Input at index {i} contains negative token IDs"
-            # Note: MultimodalEmbeddingInput validation would be handled by Pydantic
-
         return None
 
     def _convert_to_internal_request(
         self,
         request: EmbeddingRequest,
-        request_id: str,
-    ) -> tuple[EmbeddingReqInput, Union[EmbeddingRequest, List[EmbeddingRequest]]]:
+    ) -> tuple[EmbeddingReqInput, EmbeddingRequest]:
         """Convert OpenAI embedding request to internal format"""
         prompt = request.input
+
         if isinstance(prompt, str):
             # Single string input
             prompt_kwargs = {"text": prompt}
         elif isinstance(prompt, list):
             if len(prompt) > 0 and isinstance(prompt[0], str):
-                # List of strings
-                prompt_kwargs = {"text": prompt}
+                # List of strings - if it's a single string in a list, treat as single string
+                if len(prompt) == 1:
+                    prompt_kwargs = {"text": prompt[0]}
+                else:
+                    prompt_kwargs = {"text": prompt}
             elif len(prompt) > 0 and isinstance(prompt[0], MultimodalEmbeddingInput):
                 # Handle multimodal embedding inputs
                 texts = []
@@ -94,7 +84,6 @@ class OpenAIServingEmbedding(OpenAIServingBase):
 
                 generate_prompts = []
                 # Check if we have a chat template for multimodal embeddings
-                # This would need to be passed in from the server configuration
                 chat_template_name = getattr(
                     self.tokenizer_manager, "chat_template_name", None
                 )
@@ -121,6 +110,7 @@ class OpenAIServingEmbedding(OpenAIServingBase):
         else:
             # Other types (should not happen but handle gracefully)
             prompt_kwargs = {"input_ids": prompt}
+
         adapted_request = EmbeddingReqInput(
             **prompt_kwargs,
         )