fix: Add validation for langchain tools.

PiperOrigin-RevId: 625092824

Author: yeesian

tests/unit/vertex_langchain/test_reasoning_engine_templates_langchain.py

@@ -24,10 +24,12 @@
 from vertexai.preview import reasoning_engines
 import pytest
 
+
 from langchain_core import agents
 from langchain_core import messages
 from langchain_core import outputs
 from langchain_core import tools as lc_tools
+from langchain.tools.base import StructuredTool
 
 
 _DEFAULT_PLACE_TOOL_ACTIVITY = "museums"
@@ -100,7 +102,7 @@ def test_initialization_with_tools(self):
             model=_TEST_MODEL,
             tools=[
                 place_tool_query,
-                place_photo_query,
+                StructuredTool.from_function(place_photo_query),
             ],
         )
         for tool in agent._tools:
@@ -178,3 +180,22 @@ def test_parse_text_errors(self, vertexai_init_mock):
         agent.set_up()
         with pytest.raises(ValueError, match=r"Can only parse messages"):
             agent._output_parser.parse("text")
+
+
+class TestConvertToolsOrRaise:
+    def test_convert_tools_or_raise(self, vertexai_init_mock):
+        pass
+
+
+def _return_input_no_typing(input_):
+    """Returns input back to user."""
+    return input_
+
+
+class TestConvertToolsOrRaiseErrors:
+    def test_raise_untyped_input_args(self, vertexai_init_mock):
+        with pytest.raises(TypeError, match=r"has untyped input_arg"):
+            reasoning_engines.LangchainAgent(
+                model=_TEST_MODEL,
+                tools=[_return_input_no_typing],
+            )

vertexai/preview/reasoning_engines/templates/langchain.py

@@ -199,6 +199,40 @@ def _format_to_messages(
         ])
 
 
+def _validate_callable_parameters_are_annotated(callable: Callable):
+    """Validates that the parameters of the callable have type annotations.
+
+    This ensures that they can be used for constructing LangChain tools that are
+    usable with Gemini function calling.
+    """
+    import inspect
+    parameters = dict(inspect.signature(callable).parameters)
+    for name, parameter in parameters.items():
+        if parameter.annotation == inspect.Parameter.empty:
+            raise TypeError(
+                f"Callable={callable.__name__} has untyped input_arg={name}. "
+                f"Please specify a type when defining it, e.g. `{name}: str`."
+            )
+
+
+def _convert_tools_or_raise(
+    tools: Sequence[Union[Callable, "BaseTool"]]
+) -> Sequence["BaseTool"]:
+    """Converts the tools into Langchain tools (if needed).
+
+    See https://blog.langchain.dev/structured-tools/ for details.
+    """
+    from langchain_core import tools as lc_tools
+    from langchain.tools.base import StructuredTool
+    result = []
+    for tool in tools:
+        if not isinstance(tool, lc_tools.BaseTool):
+            _validate_callable_parameters_are_annotated(tool)
+            tool = StructuredTool.from_function(tool)
+        result.append(tool)
+    return result
+
+
 class LangchainAgent:
     """A Langchain Agent.
 
@@ -302,19 +336,19 @@ def __init__(
                 langchain.runnables.history.RunnableWithMessageHistory if
                 chat_history is specified. If chat_history is None, this will be
                 ignored.
+
+        Raises:
+            TypeError: If there is an invalid tool (e.g. function with an input
+            that did not specify its type).
         """
         from google.cloud.aiplatform import initializer
         self._project = initializer.global_config.project
         self._location = initializer.global_config.location
         self._tools = []
         if tools:
-            from langchain_core import tools as lc_tools
-            from langchain.tools.base import StructuredTool
-            self._tools = [
-                tool if isinstance(tool, lc_tools.BaseTool)
-                else StructuredTool.from_function(tool)
-                for tool in tools
-            ]
+            # Unlike the other fields, we convert tools at initialization to
+            # validate the functions/tools before they are deployed.
+            self._tools = _convert_tools_or_raise(tools)
         self._model_name = model
         self._prompt = prompt
         self._output_parser = output_parser