langgraph: use create_model_v2 and fallback on create_model (#1708)

* langgraph: use create_model_v2 and fallback on create_model

* fix

* code review

* fix

libs/langgraph/langgraph/graph/state.py

@@ -19,7 +19,6 @@
 
 from langchain_core.runnables import Runnable, RunnableConfig
 from langchain_core.runnables.base import RunnableLike
-from langchain_core.runnables.utils import create_model
 from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1
 
@@ -47,6 +46,7 @@
 from langgraph.pregel.write import SKIP_WRITE, ChannelWrite, ChannelWriteEntry
 from langgraph.store.base import BaseStore
 from langgraph.utils.fields import get_field_default
+from langgraph.utils.pydantic import create_model
 from langgraph.utils.runnable import coerce_to_runnable
 
 logger = logging.getLogger(__name__)
@@ -784,12 +784,12 @@ def _get_schema(
         if len(keys) == 1 and keys[0] == "__root__":
             return create_model(  # type: ignore[call-overload]
                 name,
-                __root__=(channels[keys[0]].UpdateType, None),
+                root=(channels[keys[0]].UpdateType, None),
             )
         else:
             return create_model(  # type: ignore[call-overload]
                 name,
-                **{
+                field_definitions={
                     k: (
                         channels[k].UpdateType,
                         (

libs/langgraph/langgraph/pregel/__init__.py

@@ -34,7 +34,6 @@
 )
 from langchain_core.runnables.utils import (
     ConfigurableFieldSpec,
-    create_model,
     get_function_nonlocals,
     get_unique_config_specs,
 )
@@ -91,6 +90,7 @@
     patch_config,
     patch_configurable,
 )
+from langgraph.utils.pydantic import create_model
 from langgraph.utils.runnable import RunnableCallable
 
 WriteValue = Union[Callable[[Input], Output], Any]
@@ -309,7 +309,7 @@ def get_input_schema(
         else:
             return create_model(  # type: ignore[call-overload]
                 self.get_name("Input"),
-                **{
+                field_definitions={
                     k: (self.channels[k].UpdateType, None)
                     for k in self.input_channels or self.channels.keys()
                 },
@@ -329,7 +329,9 @@ def get_output_schema(
         else:
             return create_model(  # type: ignore[call-overload]
                 self.get_name("Output"),
-                **{k: (self.channels[k].ValueType, None) for k in self.output_channels},
+                field_definitions={
+                    k: (self.channels[k].ValueType, None) for k in self.output_channels
+                },
             )
 
     @property

libs/langgraph/langgraph/utils/pydantic.py

@@ -0,0 +1,37 @@
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel
+from pydantic.v1 import BaseModel as BaseModelV1
+
+
+def create_model(
+    model_name: str,
+    *,
+    field_definitions: Optional[Dict[str, Any]] = None,
+    root: Optional[Any] = None,
+) -> Union[BaseModel, BaseModelV1]:
+    """Create a pydantic model with the given field definitions.
+
+    Args:
+        model_name: The name of the model.
+        field_definitions: The field definitions for the model.
+        root: Type for a root model (RootModel)
+    """
+    try:
+        # for langchain-core >= 0.3.0
+        from langchain_core.runnables.pydantic import create_model_v2
+
+        return create_model_v2(
+            model_name,
+            field_definitions=field_definitions,
+            root=root,
+        )
+    except ImportError:
+        # for langchain-core < 0.3.0
+        from langchain_core.runnables.utils import create_model
+
+        v1_kwargs = {}
+        if root is not None:
+            v1_kwargs["__root__"] = root
+
+        return create_model(model_name, **v1_kwargs, **(field_definitions or {}))