if final_chunk is None:
                    final_chunk = chunk
                else:
                    final_chunk += chunk
                if run_manager:
                    run_manager.on_llm_new_token(
                        chunk.text,
                        verbose=verbose,
                    )
        if final_chunk is None:
            raise ValueError("No data received from Ollama stream.")
        return final_chunk
[docs]class Ollama(BaseLLM, _OllamaCommon):
    """Ollama locally runs large language models.
    To use, follow the instructions at https://ollama.ai/.
    Example:
        .. code-block:: python
            from langchain.llms import Ollama
            ollama = Ollama(model="llama2")
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "ollama-llm"
    def _generate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Call out to Ollama's generate endpoint.
        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            The string generated by the model.
        Example:
            .. code-block:: python
                response = ollama("Tell me a joke.")
        """
        # TODO: add caching here.
        generations = []
        for prompt in prompts:
            final_chunk = super()._stream_with_aggregation(