"""
    if not history:
        return _ChatHistory()
    first_message = history[0]
    system_message = first_message if isinstance(first_message, SystemMessage) else None
    chat_history = _ChatHistory(system_message=system_message)
    messages_left = history[1:] if system_message else history
    if len(messages_left) % 2 != 0:
        raise ValueError(
            f"Amount of messages in history should be even, got {len(messages_left)}!"
        )
    for question, answer in zip(messages_left[::2], messages_left[1::2]):
        if not isinstance(question, HumanMessage) or not isinstance(answer, AIMessage):
            raise ValueError(
                "A human message should follow a bot one, "
                f"got {question.type}, {answer.type}."
            )
        chat_history.history.append(_MessagePair(question=question, answer=answer))
    return chat_history
[docs]class ChatVertexAI(_VertexAICommon, BaseChatModel):
    """Wrapper around Vertex AI large language models."""
    model_name: str = "chat-bison"
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in environment."""
        cls._try_init_vertexai(values)
        try:
            from vertexai.preview.language_models import ChatModel
        except ImportError:
            raise_vertex_import_error()
        values["client"] = ChatModel.from_pretrained(values["model_name"])
        return values
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chat_models/vertexai.html