Fix/enable thinking

graphgen/common/init_llm.py

@@ -46,6 +46,10 @@ def __init__(self, backend: str, config: Dict[str, Any]):
             from graphgen.models.llm.local.vllm_wrapper import VLLMWrapper
 
             self.llm_instance = VLLMWrapper(**config)
+        elif backend == "ray_serve":
+            from graphgen.models.llm.api.ray_serve_client import RayServeClient
+
+            self.llm_instance = RayServeClient(**config)
         else:
             raise NotImplementedError(f"Backend {backend} is not implemented yet.")
 

graphgen/models/llm/api/ray_serve_client.py

@@ -0,0 +1,88 @@
+from typing import Any, List, Optional
+
+from graphgen.bases.base_llm_wrapper import BaseLLMWrapper
+from graphgen.bases.datatypes import Token
+
+
+class RayServeClient(BaseLLMWrapper):
+    """
+    A client to interact with a Ray Serve deployment.
+    """
+
+    def __init__(
+        self,
+        *,
+        app_name: Optional[str] = None,
+        deployment_name: Optional[str] = None,
+        serve_backend: Optional[str] = None,
+        **kwargs: Any,
+    ):
+        try:
+            from ray import serve
+        except ImportError as e:
+            raise ImportError(
+                "Ray is not installed. Please install it with `pip install ray[serve]`."
+            ) from e
+
+        super().__init__(**kwargs)
+
+        # Try to get existing handle first
+        self.handle = None
+        if app_name:
+            try:
+                self.handle = serve.get_app_handle(app_name)
+            except Exception:
+                pass
+        elif deployment_name:
+            try:
+                self.handle = serve.get_deployment(deployment_name).get_handle()
+            except Exception:
+                pass
+
+        # If no handle found, try to deploy if serve_backend is provided
+        if self.handle is None:
+            if serve_backend:
+                if not app_name:
+                    import uuid
+
+                    app_name = f"llm_app_{serve_backend}_{uuid.uuid4().hex[:8]}"
+
+                print(
+                    f"Deploying Ray Serve app '{app_name}' with backend '{serve_backend}'..."
+                )
+                from graphgen.models.llm.local.ray_serve_deployment import LLMDeployment
+
+                # Filter kwargs to avoid passing unrelated args if necessary,
+                # but LLMDeployment config accepts everything for now.
+                # Note: We need to pass kwargs as the config dict.
+                deployment = LLMDeployment.bind(backend=serve_backend, config=kwargs)
+                serve.run(deployment, name=app_name, route_prefix=f"/{app_name}")
+                self.handle = serve.get_app_handle(app_name)
+            elif app_name or deployment_name:
+                raise ValueError(
+                    f"Ray Serve app/deployment '{app_name or deployment_name}' "
+                    "not found and 'serve_backend' not provided to deploy it."
+                )
+            else:
+                raise ValueError(
+                    "Either 'app_name', 'deployment_name' or 'serve_backend' "
+                    "must be provided for RayServeClient."
+                )
+
+    async def generate_answer(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> str:
+        """Generate answer from the model."""
+        return await self.handle.generate_answer.remote(text, history, **extra)
+
+    async def generate_topk_per_token(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> List[Token]:
+        """Generate top-k tokens for the next token prediction."""
+        return await self.handle.generate_topk_per_token.remote(text, history, **extra)
+
+    async def generate_inputs_prob(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> List[Token]:
+        """Generate probabilities for each token in the input."""
+        return await self.handle.generate_inputs_prob.remote(text, history, **extra)

graphgen/models/llm/local/ray_serve_deployment.py

@@ -0,0 +1,84 @@
+import os
+from typing import Any, Dict, List, Optional
+
+from ray import serve
+from starlette.requests import Request
+
+from graphgen.bases.datatypes import Token
+from graphgen.models.tokenizer import Tokenizer
+
+
+@serve.deployment
+class LLMDeployment:
+    def __init__(self, backend: str, config: Dict[str, Any]):
+        self.backend = backend
+
+        # Initialize tokenizer if needed
+        tokenizer_model = os.environ.get("TOKENIZER_MODEL", "cl100k_base")
+        if "tokenizer" not in config:
+            tokenizer = Tokenizer(model_name=tokenizer_model)
+            config["tokenizer"] = tokenizer
+
+        if backend == "vllm":
+            from graphgen.models.llm.local.vllm_wrapper import VLLMWrapper
+
+            self.llm_instance = VLLMWrapper(**config)
+        elif backend == "huggingface":
+            from graphgen.models.llm.local.hf_wrapper import HuggingFaceWrapper
+
+            self.llm_instance = HuggingFaceWrapper(**config)
+        elif backend == "sglang":
+            from graphgen.models.llm.local.sglang_wrapper import SGLangWrapper
+
+            self.llm_instance = SGLangWrapper(**config)
+        else:
+            raise NotImplementedError(
+                f"Backend {backend} is not implemented for Ray Serve yet."
+            )
+
+    async def generate_answer(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> str:
+        return await self.llm_instance.generate_answer(text, history, **extra)
+
+    async def generate_topk_per_token(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> List[Token]:
+        return await self.llm_instance.generate_topk_per_token(text, history, **extra)
+
+    async def generate_inputs_prob(
+        self, text: str, history: Optional[List[str]] = None, **extra: Any
+    ) -> List[Token]:
+        return await self.llm_instance.generate_inputs_prob(text, history, **extra)
+
+    async def __call__(self, request: Request) -> Dict:
+        try:
+            data = await request.json()
+            text = data.get("text")
+            history = data.get("history")
+            method = data.get("method", "generate_answer")
+            kwargs = data.get("kwargs", {})
+
+            if method == "generate_answer":
+                result = await self.generate_answer(text, history, **kwargs)
+            elif method == "generate_topk_per_token":
+                result = await self.generate_topk_per_token(text, history, **kwargs)
+            elif method == "generate_inputs_prob":
+                result = await self.generate_inputs_prob(text, history, **kwargs)
+            else:
+                return {"error": f"Method {method} not supported"}
+
+            return {"result": result}
+        except Exception as e:
+            return {"error": str(e)}
+
+
+def app_builder(args: Dict[str, str]) -> Any:
+    """
+    Builder function for 'serve run'.
+    Usage: serve run graphgen.models.llm.local.ray_serve_deployment:app_builder backend=vllm model=...
+    """
+    # args comes from the command line key=value pairs
+    backend = args.pop("backend", "vllm")
+    # remaining args are treated as config
+    return LLMDeployment.bind(backend=backend, config=args)

graphgen/models/llm/local/vllm_wrapper.py

@@ -51,7 +51,8 @@ def _build_inputs(self, prompt: str, history: Optional[List[dict]] = None) -> An
         return self.tokenizer.apply_chat_template(
             messages,
             tokenize=False,
-            add_generation_prompt=True
+            add_generation_prompt=True,
+            enable_thinking=False
         )
 
     async def _consume_generator(self, generator):
@@ -72,7 +73,7 @@ async def generate_answer(
             temperature=self.temperature if self.temperature >= 0 else 1.0,
             top_p=self.top_p if self.top_p >= 0 else 1.0,
             max_tokens=extra.get("max_new_tokens", 2048),
-            repetition_penalty=extra.get("repetition_penalty", 1.05),
+            repetition_penalty=extra.get("repetition_penalty", 1.05)
         )
 
         try:
@@ -101,7 +102,7 @@ async def generate_topk_per_token(
         sp = self.SamplingParams(
             temperature=0,
             max_tokens=1,
-            logprobs=self.top_k,
+            logprobs=self.top_k
         )
 
         try: