test_structured_few_shot_examples() — langchain Function Reference

Source Code

libs/standard-tests/langchain_tests/integration_tests/chat_models.py lines 2093–2153

    def test_structured_few_shot_examples(
        self, model: BaseChatModel, my_adder_tool: BaseTool
    ) -> None:
        """Test that the model can process few-shot examples with tool calls.

        These are represented as a sequence of messages of the following form:

        - `HumanMessage` with string content;
        - `AIMessage` with the `tool_calls` attribute populated;
        - `ToolMessage` with string content;
        - `AIMessage` with string content (an answer);
        - `HumanMessage` with string content (a follow-up question).

        This test should be skipped if the model does not support tool calling
        (see configuration below).

        ??? note "Configuration"

            To disable tool calling tests, set `has_tool_calling` to `False` in your
            test class:

            ```python
            class TestMyChatModelIntegration(ChatModelIntegrationTests):
                @property
                def has_tool_calling(self) -> bool:
                    return False
            ```

        ??? question "Troubleshooting"

            If this test fails, check that the model can correctly handle this
            sequence of messages.

            You can `xfail` the test if tool calling is implemented but this format
            is not supported.

            ```python
            @pytest.mark.xfail(reason=("Not implemented."))
            def test_structured_few_shot_examples(self, *args: Any) -> None:
                super().test_structured_few_shot_examples(*args)
            ```
        """
        if not self.has_tool_calling:
            pytest.skip("Test requires tool calling.")

        model_with_tools = model.bind_tools([my_adder_tool], tool_choice="any")
        function_result = json.dumps({"result": 3})

        tool_schema = my_adder_tool.args_schema
        assert isinstance(tool_schema, type)
        assert issubclass(tool_schema, BaseModel)
        few_shot_messages = tool_example_to_messages(
            "What is 1 + 2",
            [tool_schema(a=1, b=2)],
            tool_outputs=[function_result],
            ai_response=function_result,
        )

        messages = [*few_shot_messages, HumanMessage("What is 3 + 4")]
        result = model_with_tools.invoke(messages)
        assert isinstance(result, AIMessage)