[FEATURE] Add OpenAI-Compatible LoRA Adapter Selection (#11570)

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

@@ -204,7 +204,10 @@ class BatchResponse(BaseModel):
 class CompletionRequest(BaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/completions/create
-    model: str = DEFAULT_MODEL_NAME
+    model: str = Field(
+        default=DEFAULT_MODEL_NAME,
+        description="Model name. Supports LoRA adapters via 'base-model:adapter-name' syntax.",
+    )
     prompt: Union[List[int], List[List[int]], str, List[str]]
     best_of: Optional[int] = None
     echo: bool = False
@@ -441,7 +444,10 @@ class ChatCompletionRequest(BaseModel):
     # Ordered by official OpenAI API documentation
     # https://platform.openai.com/docs/api-reference/chat/create
     messages: List[ChatCompletionMessageParam]
-    model: str = DEFAULT_MODEL_NAME
+    model: str = Field(
+        default=DEFAULT_MODEL_NAME,
+        description="Model name. Supports LoRA adapters via 'base-model:adapter-name' syntax.",
+    )
     frequency_penalty: float = 0.0
     logit_bias: Optional[Dict[str, float]] = None
     logprobs: bool = False
@@ -1099,7 +1105,7 @@ class ResponsesResponse(BaseModel):
                 Union[
                     ResponseOutputItem, ResponseReasoningItem, ResponseFunctionToolCall
                 ]
-            ]
+            ],
         ) -> bool:
             if not items:
                 return False

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

@@ -4,7 +4,7 @@ import json
 import logging
 import uuid
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
 
 import orjson
 from fastapi import HTTPException, Request
@@ -35,6 +35,52 @@ class OpenAIServingBase(ABC):
             else None
         )
 
+    def _parse_model_parameter(self, model: str) -> Tuple[str, Optional[str]]:
+        """Parse 'base-model:adapter-name' syntax to extract LoRA adapter.
+
+        Returns (base_model, adapter_name) or (model, None) if no colon present.
+        """
+        if ":" not in model:
+            return model, None
+
+        # Split on first colon only to handle model paths with multiple colons
+        parts = model.split(":", 1)
+        base_model = parts[0].strip()
+        adapter_name = parts[1].strip() or None
+
+        return base_model, adapter_name
+
+    def _resolve_lora_path(
+        self,
+        request_model: str,
+        explicit_lora_path: Optional[Union[str, List[Optional[str]]]],
+    ) -> Optional[Union[str, List[Optional[str]]]]:
+        """Resolve LoRA adapter with priority: model parameter > explicit lora_path.
+
+        Returns adapter name or None. Supports both single values and lists (batches).
+        """
+        _, adapter_from_model = self._parse_model_parameter(request_model)
+
+        # Model parameter adapter takes precedence
+        if adapter_from_model is not None:
+            return adapter_from_model
+
+        # Fall back to explicit lora_path
+        return explicit_lora_path
+
+    def _validate_lora_enabled(self, adapter_name: str) -> None:
+        """Check that LoRA is enabled before attempting to use an adapter.
+
+        Raises ValueError with actionable guidance if --enable-lora flag is missing.
+        Adapter existence is validated later by TokenizerManager.lora_registry.
+        """
+        if not self.tokenizer_manager.server_args.enable_lora:
+            raise ValueError(
+                f"LoRA adapter '{adapter_name}' was requested, but LoRA is not enabled. "
+                "Please launch the server with --enable-lora flag and preload adapters "
+                "using --lora-paths or /load_lora_adapter endpoint."
+            )
+
     async def handle_request(
         self, request: OpenAIServingRequest, raw_request: Request
     ) -> Union[Any, StreamingResponse, ErrorResponse]:

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

@@ -164,6 +164,17 @@ class OpenAIServingChat(OpenAIServingBase):
         # Extract custom labels from raw request headers
         custom_labels = self.extract_custom_labels(raw_request)
 
+        # Resolve LoRA adapter from model parameter or explicit lora_path
+        lora_path = self._resolve_lora_path(request.model, request.lora_path)
+        if lora_path:
+            first_adapter = (
+                lora_path
+                if isinstance(lora_path, str)
+                else next((a for a in lora_path if a), None)
+            )
+            if first_adapter:
+                self._validate_lora_enabled(first_adapter)
+
         adapted_request = GenerateReqInput(
             **prompt_kwargs,
             image_data=processed_messages.image_data,
@@ -176,7 +187,7 @@ class OpenAIServingChat(OpenAIServingBase):
             stream=request.stream,
             return_text_in_logprobs=True,
             modalities=processed_messages.modalities,
-            lora_path=request.lora_path,
+            lora_path=lora_path,
             bootstrap_host=request.bootstrap_host,
             bootstrap_port=request.bootstrap_port,
             bootstrap_room=request.bootstrap_room,

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

@@ -93,6 +93,17 @@ class OpenAIServingCompletion(OpenAIServingBase):
         # Extract custom labels from raw request headers
         custom_labels = self.extract_custom_labels(raw_request)
 
+        # Resolve LoRA adapter from model parameter or explicit lora_path
+        lora_path = self._resolve_lora_path(request.model, request.lora_path)
+        if lora_path:
+            first_adapter = (
+                lora_path
+                if isinstance(lora_path, str)
+                else next((a for a in lora_path if a), None)
+            )
+            if first_adapter:
+                self._validate_lora_enabled(first_adapter)
+
         adapted_request = GenerateReqInput(
             **prompt_kwargs,
             sampling_params=sampling_params,
@@ -101,7 +112,7 @@ class OpenAIServingCompletion(OpenAIServingBase):
             logprob_start_len=logprob_start_len,
             return_text_in_logprobs=True,
             stream=request.stream,
-            lora_path=request.lora_path,
+            lora_path=lora_path,
             bootstrap_host=request.bootstrap_host,
             bootstrap_port=request.bootstrap_port,
             bootstrap_room=request.bootstrap_room,