fix(huggingface): Helper logic for init_chat_model with HuggingFace backend (#34259)

Author: MirrorDNA-Reflection-Protocol

libs/langchain/langchain_classic/chat_models/base.py

@@ -444,10 +444,9 @@ def _init_chat_model_helper(
 
     if model_provider == "huggingface":
         _check_pkg("langchain_huggingface")
-        from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
+        from langchain_huggingface import ChatHuggingFace
 
-        llm = HuggingFacePipeline.from_model_id(model_id=model, **kwargs)
-        return ChatHuggingFace(llm=llm)
+        return ChatHuggingFace.from_model_id(model_id=model, **kwargs)
 
     if model_provider == "groq":
         _check_pkg("langchain_groq")

libs/langchain_v1/langchain/chat_models/base.py

@@ -405,7 +405,7 @@ def _init_chat_model_helper(
         _check_pkg("langchain_huggingface")
         from langchain_huggingface import ChatHuggingFace
 
-        return ChatHuggingFace(model_id=model, **kwargs)
+        return ChatHuggingFace.from_model_id(model_id=model, **kwargs)
     if model_provider == "groq":
         _check_pkg("langchain_groq")
         from langchain_groq import ChatGroq

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py

@@ -7,7 +7,11 @@
 from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
 from dataclasses import dataclass
 from operator import itemgetter
-from typing import Any, Literal, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+if TYPE_CHECKING:
+    from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
+    from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline
 
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -599,6 +603,51 @@ def _set_model_profile(self) -> Self:
             self.profile = _get_default_model_profile(self.model_id)
         return self
 
+    @classmethod
+    def from_model_id(
+        cls,
+        model_id: str,
+        task: str | None = None,
+        backend: Literal["pipeline", "endpoint", "text-gen"] = "pipeline",
+        **kwargs: Any,
+    ) -> ChatHuggingFace:
+        """Construct a ChatHuggingFace model from a model_id.
+
+        Args:
+            model_id: The model ID of the Hugging Face model.
+            task: The task to perform (e.g., "text-generation").
+            backend: The backend to use. One of "pipeline", "endpoint", "text-gen".
+            **kwargs: Additional arguments to pass to the backend or ChatHuggingFace.
+        """
+        llm: (
+            Any  # HuggingFacePipeline, HuggingFaceEndpoint, HuggingFaceTextGenInference
+        )
+        if backend == "pipeline":
+            from langchain_huggingface.llms.huggingface_pipeline import (
+                HuggingFacePipeline,
+            )
+
+            llm = HuggingFacePipeline.from_model_id(
+                model_id=model_id, task=cast(str, task), **kwargs
+            )
+        elif backend == "endpoint":
+            from langchain_huggingface.llms.huggingface_endpoint import (
+                HuggingFaceEndpoint,
+            )
+
+            llm = HuggingFaceEndpoint(repo_id=model_id, task=task, **kwargs)
+        elif backend == "text-gen":
+            from langchain_community.llms.huggingface_text_gen_inference import (  # type: ignore[import-not-found]
+                HuggingFaceTextGenInference,
+            )
+
+            llm = HuggingFaceTextGenInference(inference_server_url=model_id, **kwargs)
+        else:
+            msg = f"Unknown backend: {backend}"
+            raise ValueError(msg)
+
+        return cls(llm=llm, **kwargs)
+
     def _create_chat_result(self, response: dict) -> ChatResult:
         generations = []
         token_usage = response.get("usage", {})