evaluate()

Perform the evaluation on the dataset with different metrics

Parameters:

Name	Type	Description	Default
dataset	(Dataset, EvaluationDataset)	The dataset used by the metrics to evaluate the RAG pipeline.	required
metrics	list[Metric]	List of metrics to use for evaluation. If not provided, ragas will run the evaluation on the best set of metrics to give a complete view.	None
llm	BaseRagasLLM	The language model (LLM) to use to generate the score for calculating the metrics. If not provided, ragas will use the default language model for metrics that require an LLM. This can be overridden by the LLM specified in the metric level with metric.llm.	None
embeddings	BaseRagasEmbeddings	The embeddings model to use for the metrics. If not provided, ragas will use the default embeddings for metrics that require embeddings. This can be overridden by the embeddings specified in the metric level with metric.embeddings.	None
experiment_name	str	The name of the experiment to track. This is used to track the evaluation in the tracing tool.	None
callbacks	Callbacks	Lifecycle Langchain Callbacks to run during evaluation. Check the Langchain documentation for more information.	None
run_config	RunConfig	Configuration for runtime settings like timeout and retries. If not provided, default values are used.	None
token_usage_parser	TokenUsageParser	Parser to get the token usage from the LLM result. If not provided, the cost and total token count will not be calculated. Default is None.	None
raise_exceptions	False	Whether to raise exceptions or not. If set to True, the evaluation will raise an exception if any of the metrics fail. If set to False, the evaluation will return np.nan for the row that failed. Default is False.	False
column_map	dict[str, str]	The column names of the dataset to use for evaluation. If the column names of the dataset are different from the default ones, it is possible to provide the mapping as a dictionary here. Example: If the dataset column name is contexts_v1, it is possible to pass column_map as {"contexts": "contexts_v1"}.	None
show_progress	bool	Whether to show the progress bar during evaluation. If set to False, the progress bar will be disabled. The default is True.	True
batch_size	int	How large the batches should be. If set to None (default), no batching is done.	None

Returns:

Type	Description
EvaluationResult	EvaluationResult object containing the scores of each metric. You can use this do analysis later.

Raises:

Type	Description
ValueError	if validation fails because the columns required for the metrics are missing or if the columns are of the wrong format.

Examples:

the basic usage is as follows:

from ragas import evaluate

>>> dataset
Dataset({
    features: ['question', 'ground_truth', 'answer', 'contexts'],
    num_rows: 30
})

>>> result = evaluate(dataset)
>>> print(result)
{'context_precision': 0.817,
'faithfulness': 0.892,
'answer_relevancy': 0.874}

Source code in src/ragas/evaluation.py

@track_was_completed
def evaluate(
    dataset: t.Union[Dataset, EvaluationDataset],
    metrics: t.Optional[t.Sequence[Metric]] = None,
    llm: t.Optional[BaseRagasLLM | LangchainLLM] = None,
    embeddings: t.Optional[BaseRagasEmbeddings | LangchainEmbeddings] = None,
    experiment_name: t.Optional[str] = None,
    callbacks: Callbacks = None,
    run_config: t.Optional[RunConfig] = None,
    token_usage_parser: t.Optional[TokenUsageParser] = None,
    raise_exceptions: bool = False,
    column_map: t.Optional[t.Dict[str, str]] = None,
    show_progress: bool = True,
    batch_size: t.Optional[int] = None,
    _run_id: t.Optional[UUID] = None,
    _pbar: t.Optional[tqdm] = None,
) -> EvaluationResult:
    """
    Perform the evaluation on the dataset with different metrics

    Parameters
    ----------
    dataset : Dataset, EvaluationDataset
        The dataset used by the metrics to evaluate the RAG pipeline.
    metrics : list[Metric], optional
        List of metrics to use for evaluation. If not provided, ragas will run
        the evaluation on the best set of metrics to give a complete view.
    llm : BaseRagasLLM, optional
        The language model (LLM) to use to generate the score for calculating the metrics.
        If not provided, ragas will use the default
        language model for metrics that require an LLM. This can be overridden by the LLM
        specified in the metric level with `metric.llm`.
    embeddings : BaseRagasEmbeddings, optional
        The embeddings model to use for the metrics.
        If not provided, ragas will use the default embeddings for metrics that require embeddings.
        This can be overridden by the embeddings specified in the metric level with `metric.embeddings`.
    experiment_name : str, optional
        The name of the experiment to track. This is used to track the evaluation in the tracing tool.
    callbacks : Callbacks, optional
        Lifecycle Langchain Callbacks to run during evaluation.
        Check the [Langchain documentation](https://python.langchain.com/docs/modules/callbacks/) for more information.
    run_config : RunConfig, optional
        Configuration for runtime settings like timeout and retries. If not provided, default values are used.
    token_usage_parser : TokenUsageParser, optional
        Parser to get the token usage from the LLM result.
        If not provided, the cost and total token count will not be calculated. Default is None.
    raise_exceptions : False
        Whether to raise exceptions or not. If set to True, the evaluation will raise an exception
        if any of the metrics fail. If set to False, the evaluation will return `np.nan` for the row that failed. Default is False.
    column_map : dict[str, str], optional
        The column names of the dataset to use for evaluation. If the column names of the dataset are different from the default ones,
        it is possible to provide the mapping as a dictionary here. Example: If the dataset column name is `contexts_v1`, it is possible to pass column_map as `{"contexts": "contexts_v1"}`.
    show_progress : bool, optional
        Whether to show the progress bar during evaluation. If set to False, the progress bar will be disabled. The default is True.
    batch_size : int, optional
        How large the batches should be. If set to None (default), no batching is done.

    Returns
    -------
    EvaluationResult
        EvaluationResult object containing the scores of each metric.
        You can use this do analysis later.

    Raises
    ------
    ValueError
        if validation fails because the columns required for the metrics are missing or
        if the columns are of the wrong format.

    Examples
    --------
    the basic usage is as follows:
    ```
    from ragas import evaluate

    >>> dataset
    Dataset({
        features: ['question', 'ground_truth', 'answer', 'contexts'],
        num_rows: 30
    })

    >>> result = evaluate(dataset)
    >>> print(result)
    {'context_precision': 0.817,
    'faithfulness': 0.892,
    'answer_relevancy': 0.874}
    ```
    """
    column_map = column_map or {}
    callbacks = callbacks or []
    run_config = run_config or RunConfig()

    if helicone_config.is_enabled:
        import uuid

        helicone_config.session_name = "ragas-evaluation"
        helicone_config.session_id = str(uuid.uuid4())

    if dataset is None:
        raise ValueError("Provide dataset!")

    # default metrics
    if metrics is None:
        from ragas.metrics import (
            answer_relevancy,
            context_precision,
            context_recall,
            faithfulness,
        )

        metrics = [answer_relevancy, context_precision, faithfulness, context_recall]

    if isinstance(dataset, Dataset):
        # remap column names from the dataset
        dataset = remap_column_names(dataset, column_map)
        dataset = convert_v1_to_v2_dataset(dataset)
        # validation
        dataset = EvaluationDataset.from_list(dataset.to_list())

    if isinstance(dataset, EvaluationDataset):
        validate_required_columns(dataset, metrics)
        validate_supported_metrics(dataset, metrics)

    # set the llm and embeddings
    if isinstance(llm, LangchainLLM):
        llm = LangchainLLMWrapper(llm, run_config=run_config)
    if isinstance(embeddings, LangchainEmbeddings):
        embeddings = LangchainEmbeddingsWrapper(embeddings)

    # init llms and embeddings
    binary_metrics = []
    llm_changed: t.List[int] = []
    embeddings_changed: t.List[int] = []
    answer_correctness_is_set = -1

    # loop through the metrics and perform initializations
    for i, metric in enumerate(metrics):
        # set llm and embeddings if not set
        if isinstance(metric, AspectCritic):
            binary_metrics.append(metric.name)
        if isinstance(metric, MetricWithLLM) and metric.llm is None:
            if llm is None:
                llm = llm_factory()
            metric.llm = llm
            llm_changed.append(i)
        if isinstance(metric, MetricWithEmbeddings) and metric.embeddings is None:
            if embeddings is None:
                embeddings = embedding_factory()
            metric.embeddings = embeddings
            embeddings_changed.append(i)
        if isinstance(metric, AnswerCorrectness):
            if metric.answer_similarity is None:
                answer_correctness_is_set = i

        # init all the models
        metric.init(run_config)

    executor = Executor(
        desc="Evaluating",
        keep_progress_bar=True,
        raise_exceptions=raise_exceptions,
        run_config=run_config,
        show_progress=show_progress,
        batch_size=batch_size,
        pbar=_pbar,
    )

    # Ragas Callbacks
    # init the callbacks we need for various tasks
    ragas_callbacks: t.Dict[str, BaseCallbackHandler] = {}

    # Ragas Tracer which traces the run
    tracer = RagasTracer()
    ragas_callbacks["tracer"] = tracer

    # check if cost needs to be calculated
    if token_usage_parser is not None:
        from ragas.cost import CostCallbackHandler

        cost_cb = CostCallbackHandler(token_usage_parser=token_usage_parser)
        ragas_callbacks["cost_cb"] = cost_cb

    # append all the ragas_callbacks to the callbacks
    for cb in ragas_callbacks.values():
        if isinstance(callbacks, BaseCallbackManager):
            callbacks.add_handler(cb)
        else:
            callbacks.append(cb)

    # new evaluation chain
    row_run_managers = []
    evaluation_rm, evaluation_group_cm = new_group(
        name=experiment_name or RAGAS_EVALUATION_CHAIN_NAME,
        inputs={},
        callbacks=callbacks,
        metadata={"type": ChainType.EVALUATION},
    )

    sample_type = dataset.get_sample_type()
    for i, sample in enumerate(dataset):
        row = t.cast(t.Dict[str, t.Any], sample.model_dump())
        row_rm, row_group_cm = new_group(
            name=f"row {i}",
            inputs=row,
            callbacks=evaluation_group_cm,
            metadata={"type": ChainType.ROW, "row_index": i},
        )
        row_run_managers.append((row_rm, row_group_cm))
        if sample_type == SingleTurnSample:
            _ = [
                executor.submit(
                    metric.single_turn_ascore,
                    sample,
                    row_group_cm,
                    name=f"{metric.name}-{i}",
                    timeout=run_config.timeout,
                )
                for metric in metrics
                if isinstance(metric, SingleTurnMetric)
            ]
        elif sample_type == MultiTurnSample:
            _ = [
                executor.submit(
                    metric.multi_turn_ascore,
                    sample,
                    row_group_cm,
                    name=f"{metric.name}-{i}",
                    timeout=run_config.timeout,
                )
                for metric in metrics
                if isinstance(metric, MultiTurnMetric)
            ]
        else:
            raise ValueError(f"Unsupported sample type {sample_type}")

    scores: t.List[t.Dict[str, t.Any]] = []
    try:
        # get the results
        results = executor.results()
        if results == []:
            raise ExceptionInRunner()

        # convert results to dataset_like
        for i, _ in enumerate(dataset):
            s = {}
            for j, m in enumerate(metrics):
                if isinstance(m, ModeMetric):  # type: ignore
                    key = f"{m.name}(mode={m.mode})"
                else:
                    key = m.name
                s[key] = results[len(metrics) * i + j]
            scores.append(s)
            # close the row chain
            row_rm, row_group_cm = row_run_managers[i]
            if not row_group_cm.ended:
                row_rm.on_chain_end(s)

    # run evaluation task
    except Exception as e:
        if not evaluation_group_cm.ended:
            evaluation_rm.on_chain_error(e)

        raise e
    else:
        # evalution run was successful
        # now lets process the results
        cost_cb = ragas_callbacks["cost_cb"] if "cost_cb" in ragas_callbacks else None
        result = EvaluationResult(
            scores=scores,
            dataset=dataset,
            binary_columns=binary_metrics,
            cost_cb=t.cast(
                t.Union["CostCallbackHandler", None],
                cost_cb,
            ),
            ragas_traces=tracer.traces,
            run_id=_run_id,
        )
        if not evaluation_group_cm.ended:
            evaluation_rm.on_chain_end({"scores": result.scores})
    finally:
        # reset llms and embeddings if changed
        for i in llm_changed:
            t.cast(MetricWithLLM, metrics[i]).llm = None
        for i in embeddings_changed:
            t.cast(MetricWithEmbeddings, metrics[i]).embeddings = None
        if answer_correctness_is_set != -1:
            t.cast(
                AnswerCorrectness, metrics[answer_correctness_is_set]
            ).answer_similarity = None

        # flush the analytics batcher
        from ragas._analytics import _analytics_batcher

        _analytics_batcher.flush()

    return result