test_conversation() — langchain Function Reference

Architecture documentation for the test_conversation() function in chat_models.py from the langchain codebase.

Function python LangChainCore LanguageModelBase

Entity Profile

LangChainCore→ LanguageModelBase→ test_conversation() — langchain Function Reference

Dependency Diagram

graph TD
  45aa0084_a472_4984_6620_9adfdf390819["test_conversation()"]
  971e928f_9c9b_ce7a_b93d_e762f2f5aa54["ChatModelIntegrationTests"]
  45aa0084_a472_4984_6620_9adfdf390819 -->|defined in| 971e928f_9c9b_ce7a_b93d_e762f2f5aa54
  style 45aa0084_a472_4984_6620_9adfdf390819 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/standard-tests/langchain_tests/integration_tests/chat_models.py lines 1097–1127

    def test_conversation(self, model: BaseChatModel) -> None:
        """Test to verify that the model can handle multi-turn conversations.

        This should pass for all integrations. Tests the model's ability to process
        a sequence of alternating `HumanMessage` and `AIMessage` objects as context for
        generating the next response.

        ??? question "Troubleshooting"

            First, debug
            `langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`
            because this test also uses `model.invoke`.

            If that test passes but not this one, you should verify that:

            1. Your model correctly processes the message history
            2. The model maintains appropriate context from previous messages
            3. The response is a valid `langchain_core.messages.AIMessage`

        """
        messages = [
            HumanMessage("hello"),
            AIMessage("hello"),
            HumanMessage("how are you"),
        ]

        result = model.invoke(messages)
        assert result is not None
        assert isinstance(result, AIMessage)
        assert isinstance(result.text, str)
        assert len(result.content) > 0