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 called by 1

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LangChainCore→ LanguageModelBase→ test_pydantic_model() — langchain Function Reference

Dependency Diagram

graph TD
  e78b3523_64fb_b7d4_d54b_4890d8859016["test_pydantic_model()"]
  d1d579f8_d411_ce75_6fa3_d97e725405cb["TestResponseFormatAsToolStrategy"]
  e78b3523_64fb_b7d4_d54b_4890d8859016 -->|defined in| d1d579f8_d411_ce75_6fa3_d97e725405cb
  bceba0ea_866a_8753_87af_396975cc6dbe["test_pydantic_model()"]
  bceba0ea_866a_8753_87af_396975cc6dbe -->|calls| e78b3523_64fb_b7d4_d54b_4890d8859016
  bceba0ea_866a_8753_87af_396975cc6dbe["test_pydantic_model()"]
  e78b3523_64fb_b7d4_d54b_4890d8859016 -->|calls| bceba0ea_866a_8753_87af_396975cc6dbe
  style e78b3523_64fb_b7d4_d54b_4890d8859016 fill:#6366f1,stroke:#818cf8,color:#fff

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Source Code

libs/langchain_v1/tests/unit_tests/agents/test_response_format.py lines 233–252

    def test_pydantic_model(self) -> None:
        """Test response_format as ToolStrategy with Pydantic model."""
        tool_calls = [
            [{"args": {}, "id": "1", "name": "get_weather"}],
            [
                {
                    "name": "WeatherBaseModel",
                    "id": "2",
                    "args": WEATHER_DATA,
                }
            ],
        ]

        model = FakeToolCallingModel(tool_calls=tool_calls)

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

        assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
        assert len(response["messages"]) == 5