return combined_text_output
        else:
            params = self._get_parameters(stop)
            params = {**params, **kwargs}
            result = self.client(prompt=prompt, **params)
            return result["choices"][0]["text"]
[docs]    def stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> Generator[Dict, None, None]:
        """Yields results objects as they are generated in real time.
        BETA: this is a beta feature while we figure out the right abstraction.
        Once that happens, this interface could change.
        It also calls the callback manager's on_llm_new_token event with
        similar parameters to the OpenAI LLM class method of the same name.
        Args:
            prompt: The prompts to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            A generator representing the stream of tokens being generated.
        Yields:
            A dictionary like objects containing a string token and metadata.
            See llama-cpp-python docs and below for more.
        Example:
            .. code-block:: python
                from langchain.llms import LlamaCpp
                llm = LlamaCpp(
                    model_path="/path/to/local/model.bin",
                    temperature = 0.5
                )
                for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
                        stop=["'","\n"]):
                    result = chunk["choices"][0]
                    print(result["text"], end='', flush=True)
        """
        params = self._get_parameters(stop)
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/llamacpp.html