test_typed_dict() — langchain Function Reference

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

Function python LangChainCore Runnables calls 1

Entity Profile

LangChainCore→ Runnables→ test_typed_dict() — langchain Function Reference

Dependency Diagram

graph TD
  b3705022_dc43_a898_a2cd_673c954416b8["test_typed_dict()"]
  ab8da393_2432_e6a3_7963_47ccc7b82fac["TestResponseFormatAsProviderStrategy"]
  b3705022_dc43_a898_a2cd_673c954416b8 -->|defined in| ab8da393_2432_e6a3_7963_47ccc7b82fac
  af9e7681_f120_a3f3_f3cb_98c627af8e9d["test_typed_dict()"]
  b3705022_dc43_a898_a2cd_673c954416b8 -->|calls| af9e7681_f120_a3f3_f3cb_98c627af8e9d
  style b3705022_dc43_a898_a2cd_673c954416b8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain_v1/tests/unit_tests/agents/test_response_format.py lines 752–768

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

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

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

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