force tool call on with_structured_output (#140)

langchain-ai · Apr 12, 2024 · 27a9fbf · 27a9fbf
1 parent 62ba6ca
commit 27a9fbf
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Showing 2 changed files with 44 additions and 2 deletions.
diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -63,6 +63,9 @@
     GenerativeModel,
     Part,
 )
+from vertexai.generative_models._generative_models import (  # type: ignore
+    ToolConfig,
+)
 from vertexai.language_models import (  # type: ignore
     ChatMessage,
     ChatModel,
@@ -860,9 +863,20 @@ class AnswerWithJustification(BaseModel):
             parser: OutputParserLike = PydanticOutputFunctionsParser(
                 pydantic_schema=schema
             )
+            name = schema.schema()["title"]
         else:
             parser = JsonOutputFunctionsParser()
-        llm = self.bind(functions=[schema])
+            name = schema["name"]
+
+        llm = self.bind(
+            functions=[schema],
+            tool_config={
+                "function_calling_config": {
+                    "mode": ToolConfig.FunctionCallingConfig.Mode.ANY,
+                    "allowed_function_names": [name],
+                }
+            },
+        )
         if include_raw:
             parser_with_fallback = RunnablePassthrough.assign(
                 parsed=itemgetter("raw") | parser, parsing_error=lambda _: None
@@ -877,6 +891,7 @@ class AnswerWithJustification(BaseModel):
     def bind_tools(
         self,
         tools: Sequence[Union[Type[BaseModel], Callable, BaseTool]],
+        tool_config: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
         """Bind tool-like objects to this chat model.
@@ -903,7 +918,7 @@ def bind_tools(
                 raise ValueError(
                     "Tool must be a BaseTool, Pydantic model, or callable."
                 )
-        return super().bind(functions=formatted_tools, **kwargs)
+        return self.bind(functions=formatted_tools, tool_config=tool_config, **kwargs)
 
     def _start_chat(
         self, history: _ChatHistory, **kwargs: Any

diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -408,3 +408,30 @@ class MyModel(BaseModel):
     assert response.content != ""
     function_call = response.additional_kwargs.get("function_call")
     assert function_call is None
+
+
+@pytest.mark.release
+def test_chat_vertexai_gemini_function_calling_with_structured_output() -> None:
+    class MyModel(BaseModel):
+        name: str
+        age: int
+
+    safety = {
+        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH
+    }
+    llm = ChatVertexAI(model_name="gemini-1.5-pro-preview-0409", safety_settings=safety)
+    model = llm.with_structured_output(MyModel)
+    message = HumanMessage(content="My name is Erick and I am 27 years old")
+
+    response = model.invoke([message])
+    assert isinstance(response, MyModel)
+    assert response == MyModel(name="Erick", age=27)
+
+    model = llm.with_structured_output(
+        {"name": "MyModel", "description": "MyModel", "parameters": MyModel.schema()}
+    )
+    response = model.invoke([message])
+    assert response == {
+        "name": "Erick",
+        "age": 27,
+    }