core[patch]: Respect injected in bound fns (langchain-ai#24733)

Since right now you cant use the nice injected arg syntas directly with
model.bind_tools()

libs/core/langchain_core/tools.py

@@ -120,14 +120,17 @@ def _get_filtered_args(
     func: Callable,
     *,
     filter_args: Sequence[str],
+    include_injected: bool = True,
 ) -> dict:
     """Get the arguments from a function's signature."""
     schema = inferred_model.schema()["properties"]
     valid_keys = signature(func).parameters
     return {
         k: schema[k]
         for i, (k, param) in enumerate(valid_keys.items())
-        if k not in filter_args and (i > 0 or param.name not in ("self", "cls"))
+        if k not in filter_args
+        and (i > 0 or param.name not in ("self", "cls"))
+        and (include_injected or not _is_injected_arg_type(param.annotation))
     }
 
 
@@ -247,6 +250,7 @@ def create_schema_from_function(
     filter_args: Optional[Sequence[str]] = None,
     parse_docstring: bool = False,
     error_on_invalid_docstring: bool = False,
+    include_injected: bool = True,
 ) -> Type[BaseModel]:
     """Create a pydantic schema from a function's signature.
 
@@ -260,6 +264,9 @@ def create_schema_from_function(
         error_on_invalid_docstring: if ``parse_docstring`` is provided, configure
             whether to raise ValueError on invalid Google Style docstrings.
             Defaults to False.
+        include_injected: Whether to include injected arguments in the schema.
+            Defaults to True, since we want to include them in the schema
+            when *validating* tool inputs.
 
     Returns:
         A pydantic model with the same arguments as the function.
@@ -277,7 +284,9 @@ def create_schema_from_function(
         error_on_invalid_docstring=error_on_invalid_docstring,
     )
     # Pydantic adds placeholder virtual fields we need to strip
-    valid_properties = _get_filtered_args(inferred_model, func, filter_args=filter_args)
+    valid_properties = _get_filtered_args(
+        inferred_model, func, filter_args=filter_args, include_injected=include_injected
+    )
     return _create_subset_model(
         f"{model_name}Schema",
         inferred_model,

libs/core/langchain_core/utils/function_calling.py

@@ -179,6 +179,7 @@ def convert_python_function_to_openai_function(
         filter_args=(),
         parse_docstring=True,
         error_on_invalid_docstring=False,
+        include_injected=False,
     )
     return convert_pydantic_to_openai_function(
         model,

libs/core/tests/unit_tests/test_tools.py

@@ -1429,6 +1429,36 @@ def test_tool_injected_arg_with_schema(tool_: BaseTool) -> None:
     }
 
 
+def _get_parametrized_tools() -> list:
+    def my_tool(x: int, y: str, some_tool: Annotated[Any, InjectedToolArg]) -> str:
+        """my_tool."""
+        return some_tool
+
+    async def my_async_tool(
+        x: int, y: str, *, some_tool: Annotated[Any, InjectedToolArg]
+    ) -> str:
+        """my_tool."""
+        return some_tool
+
+    return [my_tool, my_async_tool]
+
+
+@pytest.mark.parametrize("tool_", _get_parametrized_tools())
+def test_fn_injected_arg_with_schema(tool_: Callable) -> None:
+    assert convert_to_openai_function(tool_) == {
+        "name": tool_.__name__,
+        "description": "my_tool.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "x": {"type": "integer"},
+                "y": {"type": "string"},
+            },
+            "required": ["x", "y"],
+        },
+    }
+
+
 def generate_models() -> List[Any]:
     """Generate a list of base models depending on the pydantic version."""
     from pydantic import BaseModel as BaseModelProper  # pydantic: ignore