test_pydantic_model() — langchain Function Reference

Architecture documentation for the test_pydantic_model() function in test_response_format.py from the langchain codebase.

Function python LangChainCore LanguageModelBase calls 1

Entity Profile

LangChainCore→ LanguageModelBase→ test_pydantic_model() — langchain Function Reference

Dependency Diagram

graph TD
  061a614a_464f_9e0b_35c0_a82acc8032e9["test_pydantic_model()"]
  ab8da393_2432_e6a3_7963_47ccc7b82fac["TestResponseFormatAsProviderStrategy"]
  061a614a_464f_9e0b_35c0_a82acc8032e9 -->|defined in| ab8da393_2432_e6a3_7963_47ccc7b82fac
  bceba0ea_866a_8753_87af_396975cc6dbe["test_pydantic_model()"]
  061a614a_464f_9e0b_35c0_a82acc8032e9 -->|calls| bceba0ea_866a_8753_87af_396975cc6dbe
  style 061a614a_464f_9e0b_35c0_a82acc8032e9 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain_v1/tests/unit_tests/agents/test_response_format.py lines 688–704

    def test_pydantic_model(self) -> None:
        """Test response_format as ProviderStrategy with Pydantic model."""
        tool_calls = [
            [{"args": {}, "id": "1", "name": "get_weather"}],
        ]

        model = FakeToolCallingModel(
            tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
        )

        agent = create_agent(
            model, [get_weather], response_format=ProviderStrategy(WeatherBaseModel)
        )
        response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})

        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
        assert len(response["messages"]) == 4