test_tool_message_histories_string_content() — langchain Function Reference

Source Code

libs/standard-tests/langchain_tests/integration_tests/chat_models.py lines 1742–1814

    def test_tool_message_histories_string_content(
        self, model: BaseChatModel, my_adder_tool: BaseTool
    ) -> None:
        """Test that message histories are compatible with string tool contents.

        For instance with OpenAI format contents.
        If a model passes this test, it should be compatible
        with messages generated from providers following OpenAI format.

        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:

            1. The model can correctly handle message histories that include
                `AIMessage` objects with `""` content.
            2. The `tool_calls` attribute on `AIMessage` objects is correctly
                handled and passed to the model in an appropriate format.
            3. The model can correctly handle `ToolMessage` objects with string
                content and arbitrary string values for `tool_call_id`.

            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_tool_message_histories_string_content(self, *args: Any) -> None:
                super().test_tool_message_histories_string_content(*args)
            ```
        """
        if not self.has_tool_calling:
            pytest.skip("Test requires tool calling.")

        model_with_tools = model.bind_tools([my_adder_tool])
        function_name = "my_adder_tool"
        function_args = {"a": 1, "b": 2}

        messages_string_content = [
            HumanMessage("What is 1 + 2"),
            # string content (e.g. OpenAI)
            AIMessage(
                "",
                tool_calls=[
                    {
                        "name": function_name,
                        "args": function_args,
                        "id": "abc123",
                        "type": "tool_call",
                    },
                ],
            ),
            ToolMessage(
                json.dumps({"result": 3}),
                name=function_name,
                tool_call_id="abc123",
            ),
        ]
        result_string_content = model_with_tools.invoke(messages_string_content)
        assert isinstance(result_string_content, AIMessage)