langchain/client/langchain.py/LangChainPlusClient/run_on_dataset
class LangChainPlusClient:    def run_on_dataset(
        self,
        dataset_name: str,
        llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
        *,
        num_repetitions: int = 1,
        session_name: Optional[str] = None,
        verbose: bool = False,
    ) -> Dict[str, Any]:
        """Run the chain on a dataset and store traces to the specified session name.

        Args:
            dataset_name: Name of the dataset to run the chain on.
            llm_or_chain_factory: Language model or Chain constructor to run
                over the dataset. The Chain constructor is used to permit
                independent calls on each example without carrying over state.
            concurrency_level: Number of async workers to run in parallel.
            num_repetitions: Number of times to run the model on each example.
                This is useful when testing success rates or generating confidence
                intervals.
            session_name: Name of the session to store the traces in.
                Defaults to {dataset_name}-{chain class name}-{datetime}.
            verbose: Whether to print progress.

        Returns:
            A dictionary mapping example ids to the model outputs.
        """
        session_name = LangChainPlusClient._get_session_name(
            session_name, llm_or_chain_factory, dataset_name
        )
        dataset = self.read_dataset(dataset_name=dataset_name)
        examples = self.list_examples(dataset_id=str(dataset.id))
        return run_on_examples(
            examples,
            llm_or_chain_factory,
            num_repetitions=num_repetitions,
            session_name=session_name,
            verbose=verbose,
        )