Metrics

MetricType

Bases: Enum

Enumeration of metric types in Ragas.

Attributes:

Name	Type	Description
SINGLE_TURN	str	Represents a single-turn metric type.
MULTI_TURN	str	Represents a multi-turn metric type.

Metric dataclass

Metric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: ABC

Abstract base class for metrics in Ragas.

Attributes:

Name	Type	Description
name	str	The name of the metric.
required_columns	Dict[str, Set[str]]	A dictionary mapping metric type names to sets of required column names. This is a property and raises ValueError if columns are not in VALID_COLUMNS.

init abstractmethod

init(run_config: RunConfig) -> None

Initialize the metric with the given run configuration.

Parameters:

Name	Type	Description	Default
run_config	RunConfig	Configuration for the metric run including timeouts and other settings.	required

Source code in src/ragas/metrics/base.py

@abstractmethod
def init(self, run_config: RunConfig) -> None:
    """
    Initialize the metric with the given run configuration.

    Parameters
    ----------
    run_config : RunConfig
        Configuration for the metric run including timeouts and other settings.
    """
    ...

MetricWithLLM dataclass

MetricWithLLM(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '', llm: Optional[BaseRagasLLM] = None, output_type: Optional[MetricOutputType] = None)

Bases: Metric, PromptMixin

A metric class that uses a language model for evaluation.

Attributes:

Name	Type	Description
llm	Optional[BaseRagasLLM]	The language model used for the metric. Both BaseRagasLLM and InstructorBaseRagasLLM are accepted at runtime via duck typing (both have compatible methods).

init

init(run_config: RunConfig) -> None

Initialize the metric with run configuration and validate LLM is present.

Parameters:

Name	Type	Description	Default
run_config	RunConfig	Configuration for the metric run.	required

Raises:

Type	Description
ValueError	If no LLM is provided to the metric.

Source code in src/ragas/metrics/base.py

def init(self, run_config: RunConfig) -> None:
    """
    Initialize the metric with run configuration and validate LLM is present.

    Parameters
    ----------
    run_config : RunConfig
        Configuration for the metric run.

    Raises
    ------
    ValueError
        If no LLM is provided to the metric.
    """
    if self.llm is None:
        raise ValueError(
            f"Metric '{self.name}' has no valid LLM provided (self.llm is None). Please instantiate the metric with an LLM to run."
        )
    # Only BaseRagasLLM has set_run_config method, not InstructorBaseRagasLLM
    if isinstance(self.llm, BaseRagasLLM):
        self.llm.set_run_config(run_config)

train

train(path: str, demonstration_config: Optional[DemonstrationConfig] = None, instruction_config: Optional[InstructionConfig] = None, callbacks: Optional[Callbacks] = None, run_config: Optional[RunConfig] = None, batch_size: Optional[int] = None, with_debugging_logs=False, raise_exceptions: bool = True) -> None

Train the metric using local JSON data

Parameters:

Name	Type	Description	Default
path	str	Path to local JSON training data file	required
demonstration_config	DemonstrationConfig	Configuration for demonstration optimization	None
instruction_config	InstructionConfig	Configuration for instruction optimization	None
callbacks	Callbacks	List of callback functions	None
run_config	RunConfig	Run configuration	None
batch_size	int	Batch size for training	None
with_debugging_logs	bool	Enable debugging logs	False
raise_exceptions	bool	Whether to raise exceptions during training	True

Raises:

Type	Description
ValueError	If path is not provided or not a JSON file

Source code in src/ragas/metrics/base.py

def train(
    self,
    path: str,
    demonstration_config: t.Optional[DemonstrationConfig] = None,
    instruction_config: t.Optional[InstructionConfig] = None,
    callbacks: t.Optional[Callbacks] = None,
    run_config: t.Optional[RunConfig] = None,
    batch_size: t.Optional[int] = None,
    with_debugging_logs=False,
    raise_exceptions: bool = True,
) -> None:
    """
    Train the metric using local JSON data

    Parameters
    ----------
    path : str
        Path to local JSON training data file
    demonstration_config : DemonstrationConfig, optional
        Configuration for demonstration optimization
    instruction_config : InstructionConfig, optional
        Configuration for instruction optimization
    callbacks : Callbacks, optional
        List of callback functions
    run_config : RunConfig, optional
        Run configuration
    batch_size : int, optional
        Batch size for training
    with_debugging_logs : bool, default=False
        Enable debugging logs
    raise_exceptions : bool, default=True
        Whether to raise exceptions during training

    Raises
    ------
    ValueError
        If path is not provided or not a JSON file
    """
    # Validate input parameters
    if not path:
        raise ValueError("Path to training data file must be provided")

    if not path.endswith(".json"):
        raise ValueError("Train data must be in json format")

    run_config = run_config or RunConfig()
    callbacks = callbacks or []

    # Load the dataset from JSON file
    dataset = MetricAnnotation.from_json(path, metric_name=self.name)

    # only optimize the instruction if instruction_config is provided
    if instruction_config is not None:
        self._optimize_instruction(
            instruction_config=instruction_config,
            dataset=dataset,
            callbacks=callbacks,
            run_config=run_config,
            batch_size=batch_size,
            with_debugging_logs=with_debugging_logs,
            raise_exceptions=raise_exceptions,
        )

    # if demonstration_config is provided, optimize the demonstrations
    if demonstration_config is not None:
        self._optimize_demonstration(
            demonstration_config=demonstration_config,
            dataset=dataset,
        )

SingleTurnMetric dataclass

SingleTurnMetric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: Metric

A metric class for evaluating single-turn interactions.

This class provides methods to score single-turn samples, both synchronously and asynchronously.

single_turn_score

single_turn_score(sample: SingleTurnSample, callbacks: Callbacks = None) -> float

Synchronously score a single-turn sample.

May raise ImportError if nest_asyncio is not installed in a Jupyter-like environment.

Source code in src/ragas/metrics/base.py

def single_turn_score(
    self,
    sample: SingleTurnSample,
    callbacks: Callbacks = None,
) -> float:
    """
    Synchronously score a single-turn sample.

    May raise ImportError if nest_asyncio is not installed in a Jupyter-like environment.
    """
    callbacks = callbacks or []
    # only get the required columns
    sample = self._only_required_columns_single_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )

    async def _async_wrapper():
        try:
            result = await self._single_turn_ascore(
                sample=sample, callbacks=group_cm
            )
        except Exception as e:
            if not group_cm.ended:
                rm.on_chain_error(e)
            raise e
        else:
            if not group_cm.ended:
                rm.on_chain_end({"output": result})
            return result

    apply_nest_asyncio()
    score = run(_async_wrapper)

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

single_turn_ascore async

single_turn_ascore(sample: SingleTurnSample, callbacks: Callbacks = None, timeout: Optional[float] = None) -> float

Asynchronously score a single-turn sample with an optional timeout.

May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.

Source code in src/ragas/metrics/base.py

async def single_turn_ascore(
    self,
    sample: SingleTurnSample,
    callbacks: Callbacks = None,
    timeout: t.Optional[float] = None,
) -> float:
    """
    Asynchronously score a single-turn sample with an optional timeout.

    May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.
    """
    callbacks = callbacks or []
    # only get the required columns
    sample = self._only_required_columns_single_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )
    try:
        score = await asyncio.wait_for(
            self._single_turn_ascore(sample=sample, callbacks=group_cm),
            timeout=timeout,
        )
    except Exception as e:
        if not group_cm.ended:
            rm.on_chain_error(e)
        raise e
    else:
        if not group_cm.ended:
            rm.on_chain_end({"output": score})

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

MultiTurnMetric dataclass

MultiTurnMetric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: Metric

A metric class for evaluating multi-turn conversations.

This class extends the base Metric class to provide functionality for scoring multi-turn conversation samples.

multi_turn_score

multi_turn_score(sample: MultiTurnSample, callbacks: Callbacks = None) -> float

Score a multi-turn conversation sample synchronously.

May raise ImportError if nest_asyncio is not installed in Jupyter-like environments.

Source code in src/ragas/metrics/base.py

def multi_turn_score(
    self,
    sample: MultiTurnSample,
    callbacks: Callbacks = None,
) -> float:
    """
    Score a multi-turn conversation sample synchronously.

    May raise ImportError if nest_asyncio is not installed in Jupyter-like environments.
    """
    callbacks = callbacks or []
    sample = self._only_required_columns_multi_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )

    async def _async_wrapper():
        try:
            result = await self._multi_turn_ascore(
                sample=sample, callbacks=group_cm
            )
        except Exception as e:
            if not group_cm.ended:
                rm.on_chain_error(e)
            raise e
        else:
            if not group_cm.ended:
                rm.on_chain_end({"output": result})
            return result

    apply_nest_asyncio()
    score = run(_async_wrapper)

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

multi_turn_ascore async

multi_turn_ascore(sample: MultiTurnSample, callbacks: Callbacks = None, timeout: Optional[float] = None) -> float

Score a multi-turn conversation sample asynchronously.

May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.

Source code in src/ragas/metrics/base.py

async def multi_turn_ascore(
    self,
    sample: MultiTurnSample,
    callbacks: Callbacks = None,
    timeout: t.Optional[float] = None,
) -> float:
    """
    Score a multi-turn conversation sample asynchronously.

    May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.
    """
    callbacks = callbacks or []
    sample = self._only_required_columns_multi_turn(sample)

    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )
    try:
        score = await asyncio.wait_for(
            self._multi_turn_ascore(sample=sample, callbacks=group_cm),
            timeout=timeout,
        )
    except Exception as e:
        if not group_cm.ended:
            rm.on_chain_error(e)
        raise e
    else:
        if not group_cm.ended:
            rm.on_chain_end({"output": score})

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )

    return score

Ensember

Combine multiple llm outputs for same input (n>1) to a single output

from_discrete

from_discrete(inputs: list[list[Dict]], attribute: str) -> List[Dict]

Simple majority voting for binary values, ie [0,0,1] -> 0 inputs: list of list of dicts each containing verdict for a single input

Source code in src/ragas/metrics/base.py

def from_discrete(
    self, inputs: list[list[t.Dict]], attribute: str
) -> t.List[t.Dict]:
    """
    Simple majority voting for binary values, ie [0,0,1] -> 0
    inputs: list of list of dicts each containing verdict for a single input
    """

    if not isinstance(inputs, list):
        inputs = [inputs]

    if not all(len(item) == len(inputs[0]) for item in inputs):
        logger.warning("All inputs must have the same length")
        return inputs[0]

    if not all(attribute in item for input in inputs for item in input):
        logger.warning(f"All inputs must have {attribute} attribute")
        return inputs[0]

    if len(inputs) == 1:
        return inputs[0]

    verdict_agg = []
    for i in range(len(inputs[0])):
        item = inputs[0][i]
        verdicts = [inputs[k][i][attribute] for k in range(len(inputs))]
        verdict_counts = dict(Counter(verdicts).most_common())
        item[attribute] = list(verdict_counts.keys())[0]
        verdict_agg.append(item)

    return verdict_agg

SimpleBaseMetric dataclass

SimpleBaseMetric(name: str, allowed_values: AllowedValuesType = (lambda: ['pass', 'fail'])())

Bases: ABC

Base class for simple metrics that return MetricResult objects.

This class provides the foundation for metrics that evaluate inputs and return structured MetricResult objects containing scores and reasoning.

Attributes:

Name	Type	Description
name	str	The name of the metric.
allowed_values	AllowedValuesType	Allowed values for the metric output. Can be a list of strings for discrete metrics, a tuple of floats for numeric metrics, or an integer for ranking metrics.

Examples:

>>> from ragas.metrics import discrete_metric
>>>
>>> @discrete_metric(name="sentiment", allowed_values=["positive", "negative"])
>>> def sentiment_metric(user_input: str, response: str) -> str:
...     return "positive" if "good" in response else "negative"
>>>
>>> result = sentiment_metric(user_input="How are you?", response="I'm good!")
>>> print(result.value)  # "positive"

score abstractmethod

score(**kwargs) -> 'MetricResult'

Synchronously calculate the metric score.

Parameters:

Name	Type	Description	Default
**kwargs	dict	Input parameters required by the specific metric implementation.	{}

Returns:

Type	Description
MetricResult	The evaluation result containing the score and reasoning.

Source code in src/ragas/metrics/base.py

@abstractmethod
def score(self, **kwargs) -> "MetricResult":
    """
    Synchronously calculate the metric score.

    Parameters
    ----------
    **kwargs : dict
        Input parameters required by the specific metric implementation.

    Returns
    -------
    MetricResult
        The evaluation result containing the score and reasoning.
    """
    pass

ascore abstractmethod async

ascore(**kwargs) -> 'MetricResult'

Asynchronously calculate the metric score.

Parameters:

Name	Type	Description	Default
**kwargs	dict	Input parameters required by the specific metric implementation.	{}

Returns:

Type	Description
MetricResult	The evaluation result containing the score and reasoning.

Source code in src/ragas/metrics/base.py

@abstractmethod
async def ascore(self, **kwargs) -> "MetricResult":
    """
    Asynchronously calculate the metric score.

    Parameters
    ----------
    **kwargs : dict
        Input parameters required by the specific metric implementation.

    Returns
    -------
    MetricResult
        The evaluation result containing the score and reasoning.
    """
    pass

batch_score

batch_score(inputs: List[Dict[str, Any]]) -> List['MetricResult']

Synchronously calculate scores for a batch of inputs.

Parameters:

Name	Type	Description	Default
inputs	List[Dict[str, Any]]	List of input dictionaries, each containing parameters for the metric.	required

Returns:

Type	Description
List[MetricResult]	List of evaluation results, one for each input.

Source code in src/ragas/metrics/base.py

def batch_score(
    self,
    inputs: t.List[t.Dict[str, t.Any]],
) -> t.List["MetricResult"]:
    """
    Synchronously calculate scores for a batch of inputs.

    Parameters
    ----------
    inputs : List[Dict[str, Any]]
        List of input dictionaries, each containing parameters for the metric.

    Returns
    -------
    List[MetricResult]
        List of evaluation results, one for each input.
    """
    return [self.score(**input_dict) for input_dict in inputs]

abatch_score async

abatch_score(inputs: List[Dict[str, Any]]) -> List['MetricResult']

Asynchronously calculate scores for a batch of inputs in parallel.

Parameters:

Name	Type	Description	Default
inputs	List[Dict[str, Any]]	List of input dictionaries, each containing parameters for the metric.	required

Returns:

Type	Description
List[MetricResult]	List of evaluation results, one for each input.

Source code in src/ragas/metrics/base.py

async def abatch_score(
    self,
    inputs: t.List[t.Dict[str, t.Any]],
) -> t.List["MetricResult"]:
    """
    Asynchronously calculate scores for a batch of inputs in parallel.

    Parameters
    ----------
    inputs : List[Dict[str, Any]]
        List of input dictionaries, each containing parameters for the metric.

    Returns
    -------
    List[MetricResult]
        List of evaluation results, one for each input.
    """
    async_tasks = []
    for input_dict in inputs:
        # Process input asynchronously
        async_tasks.append(self.ascore(**input_dict))

    # Run all tasks concurrently and return results
    return await asyncio.gather(*async_tasks)

SimpleLLMMetric dataclass

SimpleLLMMetric(name: str, allowed_values: AllowedValuesType = (lambda: ['pass', 'fail'])(), prompt: Optional[Union[str, 'Prompt']] = None)

Bases: SimpleBaseMetric

LLM-based metric that uses prompts to generate structured responses.

save

save(path: Optional[str] = None) -> None

Save the metric configuration to a JSON file.

Parameters:

path : str, optional File path to save to. If not provided, saves to "./{metric.name}.json" Use .gz extension for compression.

Note:

If the metric has a response_model, its schema will be saved for reference but the model itself cannot be serialized. You'll need to provide it when loading.

Examples:

All these work:

metric.save() # → ./response_quality.json metric.save("custom.json") # → ./custom.json metric.save("/path/to/metrics/") # → /path/to/metrics/response_quality.json metric.save("no_extension") # → ./no_extension.json metric.save("compressed.json.gz") # → ./compressed.json.gz (compressed)

Source code in src/ragas/metrics/base.py

def save(self, path: t.Optional[str] = None) -> None:
    """
    Save the metric configuration to a JSON file.

    Parameters:
    -----------
    path : str, optional
        File path to save to. If not provided, saves to "./{metric.name}.json"
        Use .gz extension for compression.

    Note:
    -----
    If the metric has a response_model, its schema will be saved for reference
    but the model itself cannot be serialized. You'll need to provide it when loading.

    Examples:
    ---------
    All these work:
    >>> metric.save()                      # → ./response_quality.json
    >>> metric.save("custom.json")         # → ./custom.json
    >>> metric.save("/path/to/metrics/")   # → /path/to/metrics/response_quality.json
    >>> metric.save("no_extension")        # → ./no_extension.json
    >>> metric.save("compressed.json.gz")  # → ./compressed.json.gz (compressed)
    """
    import gzip
    import json
    import warnings
    from pathlib import Path

    # Handle default path
    if path is None:
        # Default to current directory with metric name as filename
        file_path = Path(f"./{self.name}.json")
    else:
        file_path = Path(path)

        # If path is a directory, append the metric name as filename
        if file_path.is_dir():
            file_path = file_path / f"{self.name}.json"
        # If path has no extension, add .json
        elif not file_path.suffix:
            file_path = file_path.with_suffix(".json")

    # Collect warning messages for data loss
    warning_messages = []

    if hasattr(self, "_response_model") and self._response_model:
        # Only warn for custom response models, not auto-generated ones
        if not getattr(self._response_model, "__ragas_auto_generated__", False):
            warning_messages.append(
                "- Custom response_model will be lost (set it manually after loading)"
            )

    # Serialize the prompt (may add embedding_model warning)
    prompt_data = self._serialize_prompt(warning_messages)

    # Determine the metric type
    metric_type = self.__class__.__name__

    # Get metric-specific config
    config = self._get_metric_config()

    # Emit consolidated warning if there's data loss
    if warning_messages:
        warnings.warn(
            "Some metric components cannot be saved and will be lost:\n"
            + "\n".join(warning_messages)
            + "\n\nYou'll need to provide these when loading the metric."
        )

    data = {
        "format_version": "1.0",
        "metric_type": metric_type,
        "name": self.name,
        "prompt": prompt_data,
        "config": config,
        "response_model_info": self._serialize_response_model_info(),
    }
    try:
        if file_path.suffix == ".gz":
            with gzip.open(file_path, "wt", encoding="utf-8") as f:
                json.dump(data, f, indent=2)
        else:
            with open(file_path, "w", encoding="utf-8") as f:
                json.dump(data, f, indent=2)
    except (OSError, IOError) as e:
        raise ValueError(f"Cannot save metric to {file_path}: {e}")

load classmethod

load(path: str, response_model: Optional[Type['BaseModel']] = None, embedding_model: Optional['EmbeddingModelType'] = None) -> 'SimpleLLMMetric'

Load a metric from a JSON file.

Parameters:

path : str File path to load from. Supports .gz compressed files. response_model : Optional[Type[BaseModel]] Pydantic model to use for response validation. Required for custom SimpleLLMMetrics. embedding_model : Optional[Any] Embedding model for DynamicFewShotPrompt. Required if the original used one.

Returns:

SimpleLLMMetric Loaded metric instance

Raises:

ValueError If file cannot be loaded, is invalid, or missing required models

Source code in src/ragas/metrics/base.py

@classmethod
def load(
    cls,
    path: str,
    response_model: t.Optional[t.Type["BaseModel"]] = None,
    embedding_model: t.Optional["EmbeddingModelType"] = None,
) -> "SimpleLLMMetric":
    """
    Load a metric from a JSON file.

    Parameters:
    -----------
    path : str
        File path to load from. Supports .gz compressed files.
    response_model : Optional[Type[BaseModel]]
        Pydantic model to use for response validation. Required for custom SimpleLLMMetrics.
    embedding_model : Optional[Any]
        Embedding model for DynamicFewShotPrompt. Required if the original used one.

    Returns:
    --------
    SimpleLLMMetric
        Loaded metric instance

    Raises:
    -------
    ValueError
        If file cannot be loaded, is invalid, or missing required models
    """
    import gzip
    import json
    from pathlib import Path

    file_path = Path(path)

    # Load JSON data
    try:
        if file_path.suffix == ".gz":
            with gzip.open(file_path, "rt", encoding="utf-8") as f:
                data = json.load(f)
        else:
            with open(file_path, "r", encoding="utf-8") as f:
                data = json.load(f)
    except (FileNotFoundError, json.JSONDecodeError, OSError) as e:
        raise ValueError(f"Cannot load metric from {path}: {e}")

    # Validate format
    if data.get("format_version") != "1.0":
        import warnings

        warnings.warn(
            f"Loading metric with format version {data.get('format_version')}, expected 1.0"
        )

    # Reconstruct the prompt
    prompt = cls._deserialize_prompt(data["prompt"], embedding_model)

    # Get config
    config = data.get("config", {})

    # Create the metric instance
    metric = cls(name=data["name"], prompt=prompt, **config)

    # Set response model if provided
    if response_model:
        metric._response_model = response_model

    return metric

get_correlation abstractmethod

get_correlation(gold_labels: List[str], predictions: List[str]) -> float

Calculate the correlation between gold scores and predicted scores. This is a placeholder method and should be implemented based on the specific metric.

Source code in src/ragas/metrics/base.py

@abstractmethod
def get_correlation(
    self, gold_labels: t.List[str], predictions: t.List[str]
) -> float:
    """
    Calculate the correlation between gold scores and predicted scores.
    This is a placeholder method and should be implemented based on the specific metric.
    """
    pass

align_and_validate

align_and_validate(dataset: 'Dataset', embedding_model: 'EmbeddingModelType', llm: 'BaseRagasLLM', test_size: float = 0.2, random_state: int = 42, **kwargs: Dict[str, Any])

Args: dataset: experiment to align the metric with. embedding_model: The embedding model used for dynamic few-shot prompting. llm: The LLM instance to use for scoring.

Align the metric with the specified experiments and validate it against a gold standard experiment. This method combines alignment and validation into a single step.

Source code in src/ragas/metrics/base.py

def align_and_validate(
    self,
    dataset: "Dataset",
    embedding_model: "EmbeddingModelType",
    llm: "BaseRagasLLM",
    test_size: float = 0.2,
    random_state: int = 42,
    **kwargs: t.Dict[str, t.Any],
):
    """
    Args:
        dataset: experiment to align the metric with.
        embedding_model: The embedding model used for dynamic few-shot prompting.
        llm: The LLM instance to use for scoring.

    Align the metric with the specified experiments and validate it against a gold standard experiment.
    This method combines alignment and validation into a single step.
    """
    train_dataset, test_dataset = dataset.train_test_split(
        test_size=test_size, random_state=random_state
    )

    self.align(train_dataset, embedding_model, **kwargs)  # type: ignore
    return self.validate_alignment(llm, test_dataset)  # type: ignore

align

align(train_dataset: 'Dataset', embedding_model: 'EmbeddingModelType', **kwargs: Dict[str, Any])

Args: train_dataset: train_dataset to align the metric with. embedding_model: The embedding model used for dynamic few-shot prompting.

Align the metric with the specified experiments by different optimization methods.

Source code in src/ragas/metrics/base.py

def align(
    self,
    train_dataset: "Dataset",
    embedding_model: "EmbeddingModelType",
    **kwargs: t.Dict[str, t.Any],
):
    """
    Args:
        train_dataset: train_dataset to align the metric with.
        embedding_model: The embedding model used for dynamic few-shot prompting.

    Align the metric with the specified experiments by different optimization methods.
    """

    # get prompt
    if not self.prompt:
        raise Exception("prompt not passed")
    from ragas.prompt.simple_prompt import Prompt

    self.prompt = (
        self.prompt if isinstance(self.prompt, Prompt) else Prompt(self.prompt)
    )
    # Extract specific parameters for from_prompt method
    max_similar_examples_val = kwargs.get("max_similar_examples", 3)
    similarity_threshold_val = kwargs.get("similarity_threshold", 0.7)
    max_similar_examples = (
        int(max_similar_examples_val)
        if isinstance(max_similar_examples_val, (int, str))
        else 3
    )
    similarity_threshold = (
        float(similarity_threshold_val)
        if isinstance(similarity_threshold_val, (int, float, str))
        else 0.7
    )
    # Convert BaseRagasEmbeddings to BaseRagasEmbedding if needed
    if hasattr(embedding_model, "embed_query"):
        # For legacy BaseRagasEmbeddings, we need to wrap it
        # Create a wrapper that implements BaseRagasEmbedding interface
        class EmbeddingWrapper:
            def __init__(self, legacy_embedding):
                self.legacy_embedding = legacy_embedding

            def embed_text(self, text: str, **kwargs) -> t.List[float]:
                return self.legacy_embedding.embed_query(text)

            async def aembed_text(self, text: str, **kwargs) -> t.List[float]:
                return await self.legacy_embedding.aembed_query(text)

        actual_embedding_model = EmbeddingWrapper(embedding_model)
    else:
        # Already BaseRagasEmbedding
        actual_embedding_model = embedding_model

    from ragas.prompt.dynamic_few_shot import DynamicFewShotPrompt

    self.prompt = DynamicFewShotPrompt.from_prompt(
        self.prompt,
        actual_embedding_model,  # type: ignore[arg-type]
        max_similar_examples,
        similarity_threshold,
    )
    train_dataset.reload()
    total_items = len(train_dataset)
    input_vars = self.get_variables()
    output_vars = [self.name, f"{self.name}_reason"]

    from rich.progress import Progress

    with Progress() as progress:
        task = progress.add_task("Processing examples", total=total_items)
        for row in train_dataset:
            inputs = {
                var: train_dataset.get_row_value(row, var) for var in input_vars
            }
            inputs = {k: v for k, v in inputs.items() if v is not None}
            output = {
                var: train_dataset.get_row_value(row, var) for var in output_vars
            }
            output = {k: v for k, v in output.items() if v is not None}

            if output:
                self.prompt.add_example(inputs, output)
            progress.update(task, advance=1)

validate_alignment

validate_alignment(llm: 'BaseRagasLLM', test_dataset: 'Dataset', mapping: Dict[str, str] = {})

Args: llm: The LLM instance to use for scoring. test_dataset: An Dataset instance containing the gold standard scores. mapping: A dictionary mapping variable names expected by metrics to their corresponding names in the gold experiment.

Validate the alignment of the metric by comparing the scores against a gold standard experiment. This method computes the Cohen's Kappa score and agreement rate between the gold standard scores and the predicted scores from the metric.

Source code in src/ragas/metrics/base.py

def validate_alignment(
    self,
    llm: "BaseRagasLLM",
    test_dataset: "Dataset",
    mapping: t.Dict[str, str] = {},
):
    """
    Args:
        llm: The LLM instance to use for scoring.
        test_dataset: An Dataset instance containing the gold standard scores.
        mapping: A dictionary mapping variable names expected by metrics to their corresponding names in the gold experiment.

    Validate the alignment of the metric by comparing the scores against a gold standard experiment.
    This method computes the Cohen's Kappa score and agreement rate between the gold standard scores and
    the predicted scores from the metric.
    """

    test_dataset.reload()
    gold_scores_raw = [
        test_dataset.get_row_value(row, self.name) for row in test_dataset
    ]
    pred_scores = []
    for row in test_dataset:
        values = {
            v: (
                test_dataset.get_row_value(row, v)
                if v not in mapping
                else test_dataset.get_row_value(row, mapping.get(v, v))
            )
            for v in self.get_variables()
        }
        score = self.score(llm=llm, **values)
        pred_scores.append(score.value)

    # Convert to strings for correlation calculation, filtering out None values
    gold_scores = [str(score) for score in gold_scores_raw if score is not None]
    pred_scores_str = [str(score) for score in pred_scores if score is not None]

    df = test_dataset.to_pandas()
    df[f"{self.name}_pred"] = pred_scores
    correlation = self.get_correlation(gold_scores, pred_scores_str)
    agreement_rate = sum(
        x == y for x, y in zip(gold_scores, pred_scores_str)
    ) / len(gold_scores)
    return {
        "correlation": correlation,
        "agreement_rate": agreement_rate,
        "df": df,
    }

create_auto_response_model

create_auto_response_model(name: str, **fields) -> Type['BaseModel']

Create a response model and mark it as auto-generated by Ragas.

This function creates a Pydantic model using create_model and marks it with a special attribute to indicate it was auto-generated. This allows the save() method to distinguish between auto-generated models (which are recreated on load) and custom user models.

Parameters:

Name	Type	Description	Default
name	str	Name for the model class	required
**fields		Field definitions in create_model format. Each field is specified as: field_name=(type, default_or_field_info)	{}

Returns:

Type	Description
Type[BaseModel]	Pydantic model class marked as auto-generated

Examples:

>>> from pydantic import Field
>>> # Simple model with required fields
>>> ResponseModel = create_auto_response_model(
...     "ResponseModel",
...     value=(str, ...),
...     reason=(str, ...)
... )
>>>
>>> # Model with Field validators and descriptions
>>> ResponseModel = create_auto_response_model(
...     "ResponseModel",
...     value=(str, Field(..., description="The predicted value")),
...     reason=(str, Field(..., description="Reasoning for the prediction"))
... )

Source code in src/ragas/metrics/base.py

def create_auto_response_model(name: str, **fields) -> t.Type["BaseModel"]:
    """
    Create a response model and mark it as auto-generated by Ragas.

    This function creates a Pydantic model using create_model and marks it
    with a special attribute to indicate it was auto-generated. This allows
    the save() method to distinguish between auto-generated models (which
    are recreated on load) and custom user models.

    Parameters
    ----------
    name : str
        Name for the model class
    **fields
        Field definitions in create_model format.
        Each field is specified as: field_name=(type, default_or_field_info)

    Returns
    -------
    Type[BaseModel]
        Pydantic model class marked as auto-generated

    Examples
    --------
    >>> from pydantic import Field
    >>> # Simple model with required fields
    >>> ResponseModel = create_auto_response_model(
    ...     "ResponseModel",
    ...     value=(str, ...),
    ...     reason=(str, ...)
    ... )
    >>>
    >>> # Model with Field validators and descriptions
    >>> ResponseModel = create_auto_response_model(
    ...     "ResponseModel",
    ...     value=(str, Field(..., description="The predicted value")),
    ...     reason=(str, Field(..., description="Reasoning for the prediction"))
    ... )
    """
    from pydantic import create_model

    model = create_model(name, **fields)
    setattr(model, "__ragas_auto_generated__", True)  # type: ignore[attr-defined]
    return model

Metric dataclass

Metric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: ABC

Abstract base class for metrics in Ragas.

Attributes:

Name	Type	Description
name	str	The name of the metric.
required_columns	Dict[str, Set[str]]	A dictionary mapping metric type names to sets of required column names. This is a property and raises ValueError if columns are not in VALID_COLUMNS.

init abstractmethod

init(run_config: RunConfig) -> None

Initialize the metric with the given run configuration.

Parameters:

Name	Type	Description	Default
run_config	RunConfig	Configuration for the metric run including timeouts and other settings.	required

Source code in src/ragas/metrics/base.py

@abstractmethod
def init(self, run_config: RunConfig) -> None:
    """
    Initialize the metric with the given run configuration.

    Parameters
    ----------
    run_config : RunConfig
        Configuration for the metric run including timeouts and other settings.
    """
    ...

MetricType

Bases: Enum

Enumeration of metric types in Ragas.

Attributes:

Name	Type	Description
SINGLE_TURN	str	Represents a single-turn metric type.
MULTI_TURN	str	Represents a multi-turn metric type.

MetricWithLLM dataclass

MetricWithLLM(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '', llm: Optional[BaseRagasLLM] = None, output_type: Optional[MetricOutputType] = None)

Bases: Metric, PromptMixin

A metric class that uses a language model for evaluation.

Attributes:

Name	Type	Description
llm	Optional[BaseRagasLLM]	The language model used for the metric. Both BaseRagasLLM and InstructorBaseRagasLLM are accepted at runtime via duck typing (both have compatible methods).

init

init(run_config: RunConfig) -> None

Initialize the metric with run configuration and validate LLM is present.

Parameters:

Name	Type	Description	Default
run_config	RunConfig	Configuration for the metric run.	required

Raises:

Type	Description
ValueError	If no LLM is provided to the metric.

Source code in src/ragas/metrics/base.py

def init(self, run_config: RunConfig) -> None:
    """
    Initialize the metric with run configuration and validate LLM is present.

    Parameters
    ----------
    run_config : RunConfig
        Configuration for the metric run.

    Raises
    ------
    ValueError
        If no LLM is provided to the metric.
    """
    if self.llm is None:
        raise ValueError(
            f"Metric '{self.name}' has no valid LLM provided (self.llm is None). Please instantiate the metric with an LLM to run."
        )
    # Only BaseRagasLLM has set_run_config method, not InstructorBaseRagasLLM
    if isinstance(self.llm, BaseRagasLLM):
        self.llm.set_run_config(run_config)

train

train(path: str, demonstration_config: Optional[DemonstrationConfig] = None, instruction_config: Optional[InstructionConfig] = None, callbacks: Optional[Callbacks] = None, run_config: Optional[RunConfig] = None, batch_size: Optional[int] = None, with_debugging_logs=False, raise_exceptions: bool = True) -> None

Train the metric using local JSON data

Parameters:

Name	Type	Description	Default
path	str	Path to local JSON training data file	required
demonstration_config	DemonstrationConfig	Configuration for demonstration optimization	None
instruction_config	InstructionConfig	Configuration for instruction optimization	None
callbacks	Callbacks	List of callback functions	None
run_config	RunConfig	Run configuration	None
batch_size	int	Batch size for training	None
with_debugging_logs	bool	Enable debugging logs	False
raise_exceptions	bool	Whether to raise exceptions during training	True

Raises:

Type	Description
ValueError	If path is not provided or not a JSON file

Source code in src/ragas/metrics/base.py

def train(
    self,
    path: str,
    demonstration_config: t.Optional[DemonstrationConfig] = None,
    instruction_config: t.Optional[InstructionConfig] = None,
    callbacks: t.Optional[Callbacks] = None,
    run_config: t.Optional[RunConfig] = None,
    batch_size: t.Optional[int] = None,
    with_debugging_logs=False,
    raise_exceptions: bool = True,
) -> None:
    """
    Train the metric using local JSON data

    Parameters
    ----------
    path : str
        Path to local JSON training data file
    demonstration_config : DemonstrationConfig, optional
        Configuration for demonstration optimization
    instruction_config : InstructionConfig, optional
        Configuration for instruction optimization
    callbacks : Callbacks, optional
        List of callback functions
    run_config : RunConfig, optional
        Run configuration
    batch_size : int, optional
        Batch size for training
    with_debugging_logs : bool, default=False
        Enable debugging logs
    raise_exceptions : bool, default=True
        Whether to raise exceptions during training

    Raises
    ------
    ValueError
        If path is not provided or not a JSON file
    """
    # Validate input parameters
    if not path:
        raise ValueError("Path to training data file must be provided")

    if not path.endswith(".json"):
        raise ValueError("Train data must be in json format")

    run_config = run_config or RunConfig()
    callbacks = callbacks or []

    # Load the dataset from JSON file
    dataset = MetricAnnotation.from_json(path, metric_name=self.name)

    # only optimize the instruction if instruction_config is provided
    if instruction_config is not None:
        self._optimize_instruction(
            instruction_config=instruction_config,
            dataset=dataset,
            callbacks=callbacks,
            run_config=run_config,
            batch_size=batch_size,
            with_debugging_logs=with_debugging_logs,
            raise_exceptions=raise_exceptions,
        )

    # if demonstration_config is provided, optimize the demonstrations
    if demonstration_config is not None:
        self._optimize_demonstration(
            demonstration_config=demonstration_config,
            dataset=dataset,
        )

MultiTurnMetric dataclass

MultiTurnMetric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: Metric

A metric class for evaluating multi-turn conversations.

This class extends the base Metric class to provide functionality for scoring multi-turn conversation samples.

multi_turn_score

multi_turn_score(sample: MultiTurnSample, callbacks: Callbacks = None) -> float

Score a multi-turn conversation sample synchronously.

May raise ImportError if nest_asyncio is not installed in Jupyter-like environments.

Source code in src/ragas/metrics/base.py

def multi_turn_score(
    self,
    sample: MultiTurnSample,
    callbacks: Callbacks = None,
) -> float:
    """
    Score a multi-turn conversation sample synchronously.

    May raise ImportError if nest_asyncio is not installed in Jupyter-like environments.
    """
    callbacks = callbacks or []
    sample = self._only_required_columns_multi_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )

    async def _async_wrapper():
        try:
            result = await self._multi_turn_ascore(
                sample=sample, callbacks=group_cm
            )
        except Exception as e:
            if not group_cm.ended:
                rm.on_chain_error(e)
            raise e
        else:
            if not group_cm.ended:
                rm.on_chain_end({"output": result})
            return result

    apply_nest_asyncio()
    score = run(_async_wrapper)

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

multi_turn_ascore async

multi_turn_ascore(sample: MultiTurnSample, callbacks: Callbacks = None, timeout: Optional[float] = None) -> float

Score a multi-turn conversation sample asynchronously.

May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.

Source code in src/ragas/metrics/base.py

async def multi_turn_ascore(
    self,
    sample: MultiTurnSample,
    callbacks: Callbacks = None,
    timeout: t.Optional[float] = None,
) -> float:
    """
    Score a multi-turn conversation sample asynchronously.

    May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.
    """
    callbacks = callbacks or []
    sample = self._only_required_columns_multi_turn(sample)

    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )
    try:
        score = await asyncio.wait_for(
            self._multi_turn_ascore(sample=sample, callbacks=group_cm),
            timeout=timeout,
        )
    except Exception as e:
        if not group_cm.ended:
            rm.on_chain_error(e)
        raise e
    else:
        if not group_cm.ended:
            rm.on_chain_end({"output": score})

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )

    return score

BaseMetric dataclass

BaseMetric(name: str, allowed_values: AllowedValuesType = (lambda: ['pass', 'fail'])())

Bases: ABC

Base class for simple metrics that return MetricResult objects.

This class provides the foundation for metrics that evaluate inputs and return structured MetricResult objects containing scores and reasoning.

Attributes:

Name	Type	Description
name	str	The name of the metric.
allowed_values	AllowedValuesType	Allowed values for the metric output. Can be a list of strings for discrete metrics, a tuple of floats for numeric metrics, or an integer for ranking metrics.

Examples:

>>> from ragas.metrics import discrete_metric
>>>
>>> @discrete_metric(name="sentiment", allowed_values=["positive", "negative"])
>>> def sentiment_metric(user_input: str, response: str) -> str:
...     return "positive" if "good" in response else "negative"
>>>
>>> result = sentiment_metric(user_input="How are you?", response="I'm good!")
>>> print(result.value)  # "positive"

score abstractmethod

score(**kwargs) -> 'MetricResult'

Synchronously calculate the metric score.

Parameters:

Name	Type	Description	Default
**kwargs	dict	Input parameters required by the specific metric implementation.	{}

Returns:

Type	Description
MetricResult	The evaluation result containing the score and reasoning.

Source code in src/ragas/metrics/base.py

@abstractmethod
def score(self, **kwargs) -> "MetricResult":
    """
    Synchronously calculate the metric score.

    Parameters
    ----------
    **kwargs : dict
        Input parameters required by the specific metric implementation.

    Returns
    -------
    MetricResult
        The evaluation result containing the score and reasoning.
    """
    pass

ascore abstractmethod async

ascore(**kwargs) -> 'MetricResult'

Asynchronously calculate the metric score.

Parameters:

Name	Type	Description	Default
**kwargs	dict	Input parameters required by the specific metric implementation.	{}

Returns:

Type	Description
MetricResult	The evaluation result containing the score and reasoning.

Source code in src/ragas/metrics/base.py

@abstractmethod
async def ascore(self, **kwargs) -> "MetricResult":
    """
    Asynchronously calculate the metric score.

    Parameters
    ----------
    **kwargs : dict
        Input parameters required by the specific metric implementation.

    Returns
    -------
    MetricResult
        The evaluation result containing the score and reasoning.
    """
    pass

batch_score

batch_score(inputs: List[Dict[str, Any]]) -> List['MetricResult']

Synchronously calculate scores for a batch of inputs.

Parameters:

Name	Type	Description	Default
inputs	List[Dict[str, Any]]	List of input dictionaries, each containing parameters for the metric.	required

Returns:

Type	Description
List[MetricResult]	List of evaluation results, one for each input.

Source code in src/ragas/metrics/base.py

def batch_score(
    self,
    inputs: t.List[t.Dict[str, t.Any]],
) -> t.List["MetricResult"]:
    """
    Synchronously calculate scores for a batch of inputs.

    Parameters
    ----------
    inputs : List[Dict[str, Any]]
        List of input dictionaries, each containing parameters for the metric.

    Returns
    -------
    List[MetricResult]
        List of evaluation results, one for each input.
    """
    return [self.score(**input_dict) for input_dict in inputs]

abatch_score async

abatch_score(inputs: List[Dict[str, Any]]) -> List['MetricResult']

Asynchronously calculate scores for a batch of inputs in parallel.

Parameters:

Name	Type	Description	Default
inputs	List[Dict[str, Any]]	List of input dictionaries, each containing parameters for the metric.	required

Returns:

Type	Description
List[MetricResult]	List of evaluation results, one for each input.

Source code in src/ragas/metrics/base.py

async def abatch_score(
    self,
    inputs: t.List[t.Dict[str, t.Any]],
) -> t.List["MetricResult"]:
    """
    Asynchronously calculate scores for a batch of inputs in parallel.

    Parameters
    ----------
    inputs : List[Dict[str, Any]]
        List of input dictionaries, each containing parameters for the metric.

    Returns
    -------
    List[MetricResult]
        List of evaluation results, one for each input.
    """
    async_tasks = []
    for input_dict in inputs:
        # Process input asynchronously
        async_tasks.append(self.ascore(**input_dict))

    # Run all tasks concurrently and return results
    return await asyncio.gather(*async_tasks)

LLMMetric dataclass

LLMMetric(name: str, allowed_values: AllowedValuesType = (lambda: ['pass', 'fail'])(), prompt: Optional[Union[str, 'Prompt']] = None)

Bases: SimpleBaseMetric

LLM-based metric that uses prompts to generate structured responses.

save

save(path: Optional[str] = None) -> None

Save the metric configuration to a JSON file.

Parameters:

path : str, optional File path to save to. If not provided, saves to "./{metric.name}.json" Use .gz extension for compression.

Note:

If the metric has a response_model, its schema will be saved for reference but the model itself cannot be serialized. You'll need to provide it when loading.

Examples:

All these work:

metric.save() # → ./response_quality.json metric.save("custom.json") # → ./custom.json metric.save("/path/to/metrics/") # → /path/to/metrics/response_quality.json metric.save("no_extension") # → ./no_extension.json metric.save("compressed.json.gz") # → ./compressed.json.gz (compressed)

Source code in src/ragas/metrics/base.py

def save(self, path: t.Optional[str] = None) -> None:
    """
    Save the metric configuration to a JSON file.

    Parameters:
    -----------
    path : str, optional
        File path to save to. If not provided, saves to "./{metric.name}.json"
        Use .gz extension for compression.

    Note:
    -----
    If the metric has a response_model, its schema will be saved for reference
    but the model itself cannot be serialized. You'll need to provide it when loading.

    Examples:
    ---------
    All these work:
    >>> metric.save()                      # → ./response_quality.json
    >>> metric.save("custom.json")         # → ./custom.json
    >>> metric.save("/path/to/metrics/")   # → /path/to/metrics/response_quality.json
    >>> metric.save("no_extension")        # → ./no_extension.json
    >>> metric.save("compressed.json.gz")  # → ./compressed.json.gz (compressed)
    """
    import gzip
    import json
    import warnings
    from pathlib import Path

    # Handle default path
    if path is None:
        # Default to current directory with metric name as filename
        file_path = Path(f"./{self.name}.json")
    else:
        file_path = Path(path)

        # If path is a directory, append the metric name as filename
        if file_path.is_dir():
            file_path = file_path / f"{self.name}.json"
        # If path has no extension, add .json
        elif not file_path.suffix:
            file_path = file_path.with_suffix(".json")

    # Collect warning messages for data loss
    warning_messages = []

    if hasattr(self, "_response_model") and self._response_model:
        # Only warn for custom response models, not auto-generated ones
        if not getattr(self._response_model, "__ragas_auto_generated__", False):
            warning_messages.append(
                "- Custom response_model will be lost (set it manually after loading)"
            )

    # Serialize the prompt (may add embedding_model warning)
    prompt_data = self._serialize_prompt(warning_messages)

    # Determine the metric type
    metric_type = self.__class__.__name__

    # Get metric-specific config
    config = self._get_metric_config()

    # Emit consolidated warning if there's data loss
    if warning_messages:
        warnings.warn(
            "Some metric components cannot be saved and will be lost:\n"
            + "\n".join(warning_messages)
            + "\n\nYou'll need to provide these when loading the metric."
        )

    data = {
        "format_version": "1.0",
        "metric_type": metric_type,
        "name": self.name,
        "prompt": prompt_data,
        "config": config,
        "response_model_info": self._serialize_response_model_info(),
    }
    try:
        if file_path.suffix == ".gz":
            with gzip.open(file_path, "wt", encoding="utf-8") as f:
                json.dump(data, f, indent=2)
        else:
            with open(file_path, "w", encoding="utf-8") as f:
                json.dump(data, f, indent=2)
    except (OSError, IOError) as e:
        raise ValueError(f"Cannot save metric to {file_path}: {e}")

load classmethod

load(path: str, response_model: Optional[Type['BaseModel']] = None, embedding_model: Optional['EmbeddingModelType'] = None) -> 'SimpleLLMMetric'

Load a metric from a JSON file.

Parameters:

path : str File path to load from. Supports .gz compressed files. response_model : Optional[Type[BaseModel]] Pydantic model to use for response validation. Required for custom SimpleLLMMetrics. embedding_model : Optional[Any] Embedding model for DynamicFewShotPrompt. Required if the original used one.

Returns:

SimpleLLMMetric Loaded metric instance

Raises:

ValueError If file cannot be loaded, is invalid, or missing required models

Source code in src/ragas/metrics/base.py

@classmethod
def load(
    cls,
    path: str,
    response_model: t.Optional[t.Type["BaseModel"]] = None,
    embedding_model: t.Optional["EmbeddingModelType"] = None,
) -> "SimpleLLMMetric":
    """
    Load a metric from a JSON file.

    Parameters:
    -----------
    path : str
        File path to load from. Supports .gz compressed files.
    response_model : Optional[Type[BaseModel]]
        Pydantic model to use for response validation. Required for custom SimpleLLMMetrics.
    embedding_model : Optional[Any]
        Embedding model for DynamicFewShotPrompt. Required if the original used one.

    Returns:
    --------
    SimpleLLMMetric
        Loaded metric instance

    Raises:
    -------
    ValueError
        If file cannot be loaded, is invalid, or missing required models
    """
    import gzip
    import json
    from pathlib import Path

    file_path = Path(path)

    # Load JSON data
    try:
        if file_path.suffix == ".gz":
            with gzip.open(file_path, "rt", encoding="utf-8") as f:
                data = json.load(f)
        else:
            with open(file_path, "r", encoding="utf-8") as f:
                data = json.load(f)
    except (FileNotFoundError, json.JSONDecodeError, OSError) as e:
        raise ValueError(f"Cannot load metric from {path}: {e}")

    # Validate format
    if data.get("format_version") != "1.0":
        import warnings

        warnings.warn(
            f"Loading metric with format version {data.get('format_version')}, expected 1.0"
        )

    # Reconstruct the prompt
    prompt = cls._deserialize_prompt(data["prompt"], embedding_model)

    # Get config
    config = data.get("config", {})

    # Create the metric instance
    metric = cls(name=data["name"], prompt=prompt, **config)

    # Set response model if provided
    if response_model:
        metric._response_model = response_model

    return metric

get_correlation abstractmethod

get_correlation(gold_labels: List[str], predictions: List[str]) -> float

Calculate the correlation between gold scores and predicted scores. This is a placeholder method and should be implemented based on the specific metric.

Source code in src/ragas/metrics/base.py

@abstractmethod
def get_correlation(
    self, gold_labels: t.List[str], predictions: t.List[str]
) -> float:
    """
    Calculate the correlation between gold scores and predicted scores.
    This is a placeholder method and should be implemented based on the specific metric.
    """
    pass

align_and_validate

align_and_validate(dataset: 'Dataset', embedding_model: 'EmbeddingModelType', llm: 'BaseRagasLLM', test_size: float = 0.2, random_state: int = 42, **kwargs: Dict[str, Any])

Args: dataset: experiment to align the metric with. embedding_model: The embedding model used for dynamic few-shot prompting. llm: The LLM instance to use for scoring.

Align the metric with the specified experiments and validate it against a gold standard experiment. This method combines alignment and validation into a single step.

Source code in src/ragas/metrics/base.py

def align_and_validate(
    self,
    dataset: "Dataset",
    embedding_model: "EmbeddingModelType",
    llm: "BaseRagasLLM",
    test_size: float = 0.2,
    random_state: int = 42,
    **kwargs: t.Dict[str, t.Any],
):
    """
    Args:
        dataset: experiment to align the metric with.
        embedding_model: The embedding model used for dynamic few-shot prompting.
        llm: The LLM instance to use for scoring.

    Align the metric with the specified experiments and validate it against a gold standard experiment.
    This method combines alignment and validation into a single step.
    """
    train_dataset, test_dataset = dataset.train_test_split(
        test_size=test_size, random_state=random_state
    )

    self.align(train_dataset, embedding_model, **kwargs)  # type: ignore
    return self.validate_alignment(llm, test_dataset)  # type: ignore

align

align(train_dataset: 'Dataset', embedding_model: 'EmbeddingModelType', **kwargs: Dict[str, Any])

Args: train_dataset: train_dataset to align the metric with. embedding_model: The embedding model used for dynamic few-shot prompting.

Align the metric with the specified experiments by different optimization methods.

Source code in src/ragas/metrics/base.py

def align(
    self,
    train_dataset: "Dataset",
    embedding_model: "EmbeddingModelType",
    **kwargs: t.Dict[str, t.Any],
):
    """
    Args:
        train_dataset: train_dataset to align the metric with.
        embedding_model: The embedding model used for dynamic few-shot prompting.

    Align the metric with the specified experiments by different optimization methods.
    """

    # get prompt
    if not self.prompt:
        raise Exception("prompt not passed")
    from ragas.prompt.simple_prompt import Prompt

    self.prompt = (
        self.prompt if isinstance(self.prompt, Prompt) else Prompt(self.prompt)
    )
    # Extract specific parameters for from_prompt method
    max_similar_examples_val = kwargs.get("max_similar_examples", 3)
    similarity_threshold_val = kwargs.get("similarity_threshold", 0.7)
    max_similar_examples = (
        int(max_similar_examples_val)
        if isinstance(max_similar_examples_val, (int, str))
        else 3
    )
    similarity_threshold = (
        float(similarity_threshold_val)
        if isinstance(similarity_threshold_val, (int, float, str))
        else 0.7
    )
    # Convert BaseRagasEmbeddings to BaseRagasEmbedding if needed
    if hasattr(embedding_model, "embed_query"):
        # For legacy BaseRagasEmbeddings, we need to wrap it
        # Create a wrapper that implements BaseRagasEmbedding interface
        class EmbeddingWrapper:
            def __init__(self, legacy_embedding):
                self.legacy_embedding = legacy_embedding

            def embed_text(self, text: str, **kwargs) -> t.List[float]:
                return self.legacy_embedding.embed_query(text)

            async def aembed_text(self, text: str, **kwargs) -> t.List[float]:
                return await self.legacy_embedding.aembed_query(text)

        actual_embedding_model = EmbeddingWrapper(embedding_model)
    else:
        # Already BaseRagasEmbedding
        actual_embedding_model = embedding_model

    from ragas.prompt.dynamic_few_shot import DynamicFewShotPrompt

    self.prompt = DynamicFewShotPrompt.from_prompt(
        self.prompt,
        actual_embedding_model,  # type: ignore[arg-type]
        max_similar_examples,
        similarity_threshold,
    )
    train_dataset.reload()
    total_items = len(train_dataset)
    input_vars = self.get_variables()
    output_vars = [self.name, f"{self.name}_reason"]

    from rich.progress import Progress

    with Progress() as progress:
        task = progress.add_task("Processing examples", total=total_items)
        for row in train_dataset:
            inputs = {
                var: train_dataset.get_row_value(row, var) for var in input_vars
            }
            inputs = {k: v for k, v in inputs.items() if v is not None}
            output = {
                var: train_dataset.get_row_value(row, var) for var in output_vars
            }
            output = {k: v for k, v in output.items() if v is not None}

            if output:
                self.prompt.add_example(inputs, output)
            progress.update(task, advance=1)

validate_alignment

validate_alignment(llm: 'BaseRagasLLM', test_dataset: 'Dataset', mapping: Dict[str, str] = {})

Args: llm: The LLM instance to use for scoring. test_dataset: An Dataset instance containing the gold standard scores. mapping: A dictionary mapping variable names expected by metrics to their corresponding names in the gold experiment.

Validate the alignment of the metric by comparing the scores against a gold standard experiment. This method computes the Cohen's Kappa score and agreement rate between the gold standard scores and the predicted scores from the metric.

Source code in src/ragas/metrics/base.py

def validate_alignment(
    self,
    llm: "BaseRagasLLM",
    test_dataset: "Dataset",
    mapping: t.Dict[str, str] = {},
):
    """
    Args:
        llm: The LLM instance to use for scoring.
        test_dataset: An Dataset instance containing the gold standard scores.
        mapping: A dictionary mapping variable names expected by metrics to their corresponding names in the gold experiment.

    Validate the alignment of the metric by comparing the scores against a gold standard experiment.
    This method computes the Cohen's Kappa score and agreement rate between the gold standard scores and
    the predicted scores from the metric.
    """

    test_dataset.reload()
    gold_scores_raw = [
        test_dataset.get_row_value(row, self.name) for row in test_dataset
    ]
    pred_scores = []
    for row in test_dataset:
        values = {
            v: (
                test_dataset.get_row_value(row, v)
                if v not in mapping
                else test_dataset.get_row_value(row, mapping.get(v, v))
            )
            for v in self.get_variables()
        }
        score = self.score(llm=llm, **values)
        pred_scores.append(score.value)

    # Convert to strings for correlation calculation, filtering out None values
    gold_scores = [str(score) for score in gold_scores_raw if score is not None]
    pred_scores_str = [str(score) for score in pred_scores if score is not None]

    df = test_dataset.to_pandas()
    df[f"{self.name}_pred"] = pred_scores
    correlation = self.get_correlation(gold_scores, pred_scores_str)
    agreement_rate = sum(
        x == y for x, y in zip(gold_scores, pred_scores_str)
    ) / len(gold_scores)
    return {
        "correlation": correlation,
        "agreement_rate": agreement_rate,
        "df": df,
    }

SingleTurnMetric dataclass

SingleTurnMetric(_required_columns: Dict[MetricType, Set[str]] = dict(), name: str = '')

Bases: Metric

A metric class for evaluating single-turn interactions.

This class provides methods to score single-turn samples, both synchronously and asynchronously.

single_turn_score

single_turn_score(sample: SingleTurnSample, callbacks: Callbacks = None) -> float

Synchronously score a single-turn sample.

May raise ImportError if nest_asyncio is not installed in a Jupyter-like environment.

Source code in src/ragas/metrics/base.py

def single_turn_score(
    self,
    sample: SingleTurnSample,
    callbacks: Callbacks = None,
) -> float:
    """
    Synchronously score a single-turn sample.

    May raise ImportError if nest_asyncio is not installed in a Jupyter-like environment.
    """
    callbacks = callbacks or []
    # only get the required columns
    sample = self._only_required_columns_single_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )

    async def _async_wrapper():
        try:
            result = await self._single_turn_ascore(
                sample=sample, callbacks=group_cm
            )
        except Exception as e:
            if not group_cm.ended:
                rm.on_chain_error(e)
            raise e
        else:
            if not group_cm.ended:
                rm.on_chain_end({"output": result})
            return result

    apply_nest_asyncio()
    score = run(_async_wrapper)

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

single_turn_ascore async

single_turn_ascore(sample: SingleTurnSample, callbacks: Callbacks = None, timeout: Optional[float] = None) -> float

Asynchronously score a single-turn sample with an optional timeout.

May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.

Source code in src/ragas/metrics/base.py

async def single_turn_ascore(
    self,
    sample: SingleTurnSample,
    callbacks: Callbacks = None,
    timeout: t.Optional[float] = None,
) -> float:
    """
    Asynchronously score a single-turn sample with an optional timeout.

    May raise asyncio.TimeoutError if the scoring process exceeds the specified timeout.
    """
    callbacks = callbacks or []
    # only get the required columns
    sample = self._only_required_columns_single_turn(sample)
    rm, group_cm = new_group(
        self.name,
        inputs=sample.to_dict(),
        callbacks=callbacks,
        metadata={"type": ChainType.METRIC},
    )
    try:
        score = await asyncio.wait_for(
            self._single_turn_ascore(sample=sample, callbacks=group_cm),
            timeout=timeout,
        )
    except Exception as e:
        if not group_cm.ended:
            rm.on_chain_error(e)
        raise e
    else:
        if not group_cm.ended:
            rm.on_chain_end({"output": score})

    # track the evaluation event
    _analytics_batcher.add_evaluation(
        EvaluationEvent(
            metrics=[self.name],
            num_rows=1,
            evaluation_type=MetricType.SINGLE_TURN.name,
            language=get_metric_language(self),
        )
    )
    return score

DiscreteMetric dataclass

DiscreteMetric(name: str, allowed_values: List[str] = (lambda: ['pass', 'fail'])(), prompt: Optional[Union[str, 'Prompt']] = None)

Bases: SimpleLLMMetric, DiscreteValidator

Metric for categorical/discrete evaluations with predefined allowed values.

This class is used for metrics that output categorical values like "pass/fail", "good/bad/excellent", or custom discrete categories. Uses the instructor library for structured LLM outputs.

Attributes:

Name	Type	Description
allowed_values	List[str]	List of allowed categorical values the metric can output. Default is ["pass", "fail"].
prompt	Optional[Union[str, Prompt]]	The prompt template for the metric. Should contain placeholders for evaluation inputs that will be formatted at runtime.

Examples:

>>> from ragas.metrics import DiscreteMetric
>>> from ragas.llms import llm_factory
>>> from openai import OpenAI
>>>
>>> # Create an LLM instance
>>> client = OpenAI(api_key="your-api-key")
>>> llm = llm_factory("gpt-4o-mini", client=client)
>>>
>>> # Create a custom discrete metric
>>> metric = DiscreteMetric(
...     name="quality_check",
...     prompt="Check the quality of the response: {response}. Return 'excellent', 'good', or 'poor'.",
...     allowed_values=["excellent", "good", "poor"]
... )
>>>
>>> # Score with the metric
>>> result = metric.score(
...     llm=llm,
...     response="This is a great response!"
... )
>>> print(result.value)  # Output: "excellent" or similar

get_correlation

get_correlation(gold_labels: List[str], predictions: List[str]) -> float

Calculate the correlation between gold labels and predictions. This is a placeholder method and should be implemented based on the specific metric.

Source code in src/ragas/metrics/discrete.py

def get_correlation(
    self, gold_labels: t.List[str], predictions: t.List[str]
) -> float:
    """
    Calculate the correlation between gold labels and predictions.
    This is a placeholder method and should be implemented based on the specific metric.
    """
    try:
        from sklearn.metrics import cohen_kappa_score
    except ImportError:
        raise ImportError(
            "scikit-learn is required for correlation calculation. "
            "Please install it with `pip install scikit-learn`."
        )
    return cohen_kappa_score(gold_labels, predictions)

load classmethod

load(path: str, embedding_model: Optional[EmbeddingModelType] = None) -> DiscreteMetric

Load a DiscreteMetric from a JSON file.

Parameters:

path : str File path to load from. Supports .gz compressed files. embedding_model : Optional[Any] Embedding model for DynamicFewShotPrompt. Required if the original used one.

Returns:

DiscreteMetric Loaded metric instance

Raises:

ValueError If file cannot be loaded or is not a DiscreteMetric

Source code in src/ragas/metrics/discrete.py

@classmethod
def load(
    cls, path: str, embedding_model: t.Optional["EmbeddingModelType"] = None
) -> "DiscreteMetric":
    """
    Load a DiscreteMetric from a JSON file.

    Parameters:
    -----------
    path : str
        File path to load from. Supports .gz compressed files.
    embedding_model : Optional[Any]
        Embedding model for DynamicFewShotPrompt. Required if the original used one.

    Returns:
    --------
    DiscreteMetric
        Loaded metric instance

    Raises:
    -------
    ValueError
        If file cannot be loaded or is not a DiscreteMetric
    """
    # Validate metric type before loading
    cls._validate_metric_type(path)

    # Load using parent class method
    metric = super().load(path, embedding_model=embedding_model)

    # Additional type check for safety
    if not isinstance(metric, cls):
        raise ValueError(f"Loaded metric is not a {cls.__name__}")

    return metric

NumericMetric dataclass

NumericMetric(name: str, allowed_values: Union[Tuple[float, float], range] = (0.0, 1.0), prompt: Optional[Union[str, 'Prompt']] = None)

Bases: SimpleLLMMetric, NumericValidator

Metric for continuous numeric evaluations within a specified range.

This class is used for metrics that output numeric scores within a defined range, such as 0.0 to 1.0 for similarity scores or 1-10 ratings. Uses the instructor library for structured LLM outputs.

Attributes:

Name	Type	Description
allowed_values	Union[Tuple[float, float], range]	The valid range for metric outputs. Can be a tuple of (min, max) floats or a range object. Default is (0.0, 1.0).
llm	Optional[BaseRagasLLM]	The language model instance for evaluation. Can be created using llm_factory().
prompt	Optional[Union[str, Prompt]]	The prompt template for the metric. Should contain placeholders for evaluation inputs that will be formatted at runtime.

Examples:

>>> from ragas.metrics import NumericMetric
>>> from ragas.llms import llm_factory
>>> from openai import OpenAI
>>>
>>> # Create an LLM instance
>>> client = OpenAI(api_key="your-api-key")
>>> llm = llm_factory("gpt-4o-mini", client=client)
>>>
>>> # Create a custom numeric metric with 0-10 range
>>> metric = NumericMetric(
...     name="quality_score",
...     llm=llm,
...     prompt="Rate the quality of this response on a scale of 0-10: {response}",
...     allowed_values=(0.0, 10.0)
... )
>>>
>>> # Score with the metric
>>> result = metric.score(
...     llm=llm,
...     response="This is a great response!"
... )
>>> print(result.value)  # Output: a float between 0.0 and 10.0

get_correlation

get_correlation(gold_labels: List[str], predictions: List[str]) -> float

Calculate the correlation between gold labels and predictions. This is a placeholder method and should be implemented based on the specific metric.

Source code in src/ragas/metrics/numeric.py

def get_correlation(
    self, gold_labels: t.List[str], predictions: t.List[str]
) -> float:
    """
    Calculate the correlation between gold labels and predictions.
    This is a placeholder method and should be implemented based on the specific metric.
    """
    try:
        from scipy.stats import pearsonr
    except ImportError:
        raise ImportError(
            "scipy is required for correlation calculation. "
            "Please install it with `pip install scipy`."
        )
    # Convert strings to floats for correlation calculation
    gold_floats = [float(x) for x in gold_labels]
    pred_floats = [float(x) for x in predictions]
    result = pearsonr(gold_floats, pred_floats)
    # pearsonr returns (correlation, p-value) tuple
    correlation = t.cast(float, result[0])
    return correlation

load classmethod

load(path: str, embedding_model: Optional[EmbeddingModelType] = None) -> NumericMetric

Load a NumericMetric from a JSON file.

Parameters:

path : str File path to load from. Supports .gz compressed files. embedding_model : Optional[Any] Embedding model for DynamicFewShotPrompt. Required if the original used one.

Returns:

NumericMetric Loaded metric instance

Raises:

ValueError If file cannot be loaded or is not a NumericMetric

Source code in src/ragas/metrics/numeric.py

@classmethod
def load(
    cls, path: str, embedding_model: t.Optional["EmbeddingModelType"] = None
) -> "NumericMetric":
    """
    Load a NumericMetric from a JSON file.

    Parameters:
    -----------
    path : str
        File path to load from. Supports .gz compressed files.
    embedding_model : Optional[Any]
        Embedding model for DynamicFewShotPrompt. Required if the original used one.

    Returns:
    --------
    NumericMetric
        Loaded metric instance

    Raises:
    -------
    ValueError
        If file cannot be loaded or is not a NumericMetric
    """
    # Validate metric type before loading
    cls._validate_metric_type(path)

    # Load using parent class method
    metric = super().load(path, embedding_model=embedding_model)

    # Additional type check for safety
    if not isinstance(metric, cls):
        raise ValueError(f"Loaded metric is not a {cls.__name__}")

    # Convert allowed_values back to tuple if it's a list (due to JSON serialization)
    if hasattr(metric, "allowed_values") and isinstance(
        metric.allowed_values, list
    ):
        # Ensure it's a 2-element tuple for NumericMetric
        if len(metric.allowed_values) == 2:
            metric.allowed_values = (
                metric.allowed_values[0],
                metric.allowed_values[1],
            )
        else:
            metric.allowed_values = tuple(metric.allowed_values)  # type: ignore

    return metric

RankingMetric dataclass

RankingMetric(name: str, allowed_values: int = 2, prompt: Optional[Union[str, 'Prompt']] = None)

Bases: SimpleLLMMetric, RankingValidator

Metric for evaluations that produce ranked lists of items.

This class is used for metrics that output ordered lists, such as ranking search results, prioritizing features, or ordering responses by relevance. Uses the instructor library for structured LLM outputs.

Attributes:

Name	Type	Description
allowed_values	int	Expected number of items in the ranking list. Default is 2.
llm	Optional[BaseRagasLLM]	The language model instance for evaluation. Can be created using llm_factory().
prompt	Optional[Union[str, Prompt]]	The prompt template for the metric. Should contain placeholders for evaluation inputs that will be formatted at runtime.

Examples:

>>> from ragas.metrics import RankingMetric
>>> from ragas.llms import llm_factory
>>> from openai import OpenAI
>>>
>>> # Create an LLM instance
>>> client = OpenAI(api_key="your-api-key")
>>> llm = llm_factory("gpt-4o-mini", client=client)
>>>
>>> # Create a ranking metric that returns top 3 items
>>> metric = RankingMetric(
...     name="relevance_ranking",
...     llm=llm,
...     prompt="Rank these results by relevance: {results}",
...     allowed_values=3
... )
>>>
>>> # Score with the metric
>>> result = metric.score(
...     llm=llm,
...     results="result1, result2, result3"
... )
>>> print(result.value)  # Output: a list of 3 ranked items

get_correlation

get_correlation(gold_labels: List[str], predictions: List[str]) -> float

Calculate the correlation between gold labels and predictions. This is a placeholder method and should be implemented based on the specific metric.

Source code in src/ragas/metrics/ranking.py

def get_correlation(
    self, gold_labels: t.List[str], predictions: t.List[str]
) -> float:
    """
    Calculate the correlation between gold labels and predictions.
    This is a placeholder method and should be implemented based on the specific metric.
    """
    try:
        from sklearn.metrics import cohen_kappa_score
    except ImportError:
        raise ImportError(
            "scikit-learn is required for correlation calculation. "
            "Please install it with `pip install scikit-learn`."
        )

    kappa_scores = []
    for gold_item, prediction in zip(gold_labels, predictions):
        kappa = cohen_kappa_score(gold_item, prediction, weights="quadratic")
        kappa_scores.append(kappa)

    return sum(kappa_scores) / len(kappa_scores) if kappa_scores else 0.0

load classmethod

load(path: str, embedding_model: Optional[EmbeddingModelType] = None) -> RankingMetric

Load a RankingMetric from a JSON file.

Parameters:

path : str File path to load from. Supports .gz compressed files. embedding_model : Optional[Any] Embedding model for DynamicFewShotPrompt. Required if the original used one.

Returns:

RankingMetric Loaded metric instance

Raises:

ValueError If file cannot be loaded or is not a RankingMetric

Source code in src/ragas/metrics/ranking.py

@classmethod
def load(
    cls, path: str, embedding_model: t.Optional["EmbeddingModelType"] = None
) -> "RankingMetric":
    """
    Load a RankingMetric from a JSON file.

    Parameters:
    -----------
    path : str
        File path to load from. Supports .gz compressed files.
    embedding_model : Optional[Any]
        Embedding model for DynamicFewShotPrompt. Required if the original used one.

    Returns:
    --------
    RankingMetric
        Loaded metric instance

    Raises:
    -------
    ValueError
        If file cannot be loaded or is not a RankingMetric
    """
    # Validate metric type before loading
    cls._validate_metric_type(path)

    # Load using parent class method
    metric = super().load(path, embedding_model=embedding_model)

    # Additional type check for safety
    if not isinstance(metric, cls):
        raise ValueError(f"Loaded metric is not a {cls.__name__}")

    return metric

MetricResult

MetricResult(value: Any, reason: Optional[str] = None, traces: Optional[Dict[str, Any]] = None)

Class to hold the result of a metric evaluation.

This class behaves like its underlying result value but still provides access to additional metadata like reasoning.

Works with: - DiscreteMetrics (string results) - NumericMetrics (float/int results) - RankingMetrics (list results)

Source code in src/ragas/metrics/result.py

def __init__(
    self,
    value: t.Any,
    reason: t.Optional[str] = None,
    traces: t.Optional[t.Dict[str, t.Any]] = None,
):
    if traces is not None:
        invalid_keys = [
            key for key in traces.keys() if key not in {"input", "output"}
        ]
        if invalid_keys:
            raise ValueError(
                f"Invalid keys in traces: {invalid_keys}. Allowed keys are 'input' and 'output'."
            )
    self._value = value
    self.reason = reason
    self.traces = traces

value property

value

Get the raw result value.

to_dict

to_dict()

Convert the result to a dictionary.

Source code in src/ragas/metrics/result.py

def to_dict(self):
    """Convert the result to a dictionary."""
    return {"result": self._value, "reason": self.reason}

validate classmethod

validate(value: Any, info: ValidationInfo)

Provide compatibility with older Pydantic versions.

Source code in src/ragas/metrics/result.py

@classmethod
def validate(cls, value: t.Any, info: ValidationInfo):
    """Provide compatibility with older Pydantic versions."""
    if isinstance(value, MetricResult):
        return value
    return cls(value=value)

discrete_metric

discrete_metric(*, name: Optional[str] = None, allowed_values: Optional[List[str]] = None, **metric_params: Any) -> Callable[[Callable[..., Any]], DiscreteMetricProtocol]

Decorator for creating discrete/categorical metrics.

This decorator transforms a regular function into a DiscreteMetric instance that can be used for evaluation with predefined categorical outputs.

Parameters:

Name	Type	Description	Default
name	str	Name for the metric. If not provided, uses the function name.	None
allowed_values	List[str]	List of allowed categorical values for the metric output. Default is ["pass", "fail"].	None
**metric_params	Any	Additional parameters to pass to the metric initialization.	{}

Returns:

Type	Description
Callable[[Callable[..., Any]], DiscreteMetricProtocol]	A decorator that transforms a function into a DiscreteMetric instance.

Examples:

>>> from ragas.metrics import discrete_metric
>>>
>>> @discrete_metric(name="sentiment", allowed_values=["positive", "neutral", "negative"])
>>> def sentiment_analysis(user_input: str, response: str) -> str:
...     '''Analyze sentiment of the response.'''
...     if "great" in response.lower() or "good" in response.lower():
...         return "positive"
...     elif "bad" in response.lower() or "poor" in response.lower():
...         return "negative"
...     return "neutral"
>>>
>>> result = sentiment_analysis(
...     user_input="How was your day?",
...     response="It was great!"
... )
>>> print(result.value)  # "positive"

Source code in src/ragas/metrics/discrete.py

def discrete_metric(
    *,
    name: t.Optional[str] = None,
    allowed_values: t.Optional[t.List[str]] = None,
    **metric_params: t.Any,
) -> t.Callable[[t.Callable[..., t.Any]], DiscreteMetricProtocol]:
    """
    Decorator for creating discrete/categorical metrics.

    This decorator transforms a regular function into a DiscreteMetric instance
    that can be used for evaluation with predefined categorical outputs.

    Parameters
    ----------
    name : str, optional
        Name for the metric. If not provided, uses the function name.
    allowed_values : List[str], optional
        List of allowed categorical values for the metric output.
        Default is ["pass", "fail"].
    **metric_params : Any
        Additional parameters to pass to the metric initialization.

    Returns
    -------
    Callable[[Callable[..., Any]], DiscreteMetricProtocol]
        A decorator that transforms a function into a DiscreteMetric instance.

    Examples
    --------
    >>> from ragas.metrics import discrete_metric
    >>>
    >>> @discrete_metric(name="sentiment", allowed_values=["positive", "neutral", "negative"])
    >>> def sentiment_analysis(user_input: str, response: str) -> str:
    ...     '''Analyze sentiment of the response.'''
    ...     if "great" in response.lower() or "good" in response.lower():
    ...         return "positive"
    ...     elif "bad" in response.lower() or "poor" in response.lower():
    ...         return "negative"
    ...     return "neutral"
    >>>
    >>> result = sentiment_analysis(
    ...     user_input="How was your day?",
    ...     response="It was great!"
    ... )
    >>> print(result.value)  # "positive"
    """
    if allowed_values is None:
        allowed_values = ["pass", "fail"]

    decorator_factory = create_metric_decorator()
    return decorator_factory(name=name, allowed_values=allowed_values, **metric_params)  # type: ignore[return-value]

numeric_metric

numeric_metric(*, name: Optional[str] = None, allowed_values: Optional[Union[Tuple[float, float], range]] = None, **metric_params: Any) -> Callable[[Callable[..., Any]], NumericMetricProtocol]

Decorator for creating numeric/continuous metrics.

This decorator transforms a regular function into a NumericMetric instance that outputs continuous values within a specified range.

Parameters:

Name	Type	Description	Default
name	str	Name for the metric. If not provided, uses the function name.	None
allowed_values	Union[Tuple[float, float], range]	The valid range for metric outputs as (min, max) tuple or range object. Default is (0.0, 1.0).	None
**metric_params	Any	Additional parameters to pass to the metric initialization.	{}

Returns:

Type	Description
Callable[[Callable[..., Any]], NumericMetricProtocol]	A decorator that transforms a function into a NumericMetric instance.

Examples:

>>> from ragas.metrics import numeric_metric
>>>
>>> @numeric_metric(name="relevance_score", allowed_values=(0.0, 1.0))
>>> def calculate_relevance(user_input: str, response: str) -> float:
...     '''Calculate relevance score between 0 and 1.'''
...     # Simple word overlap example
...     user_words = set(user_input.lower().split())
...     response_words = set(response.lower().split())
...     if not user_words:
...         return 0.0
...     overlap = len(user_words & response_words)
...     return overlap / len(user_words)
>>>
>>> result = calculate_relevance(
...     user_input="What is Python?",
...     response="Python is a programming language"
... )
>>> print(result.value)  # Numeric score between 0.0 and 1.0

Source code in src/ragas/metrics/numeric.py

def numeric_metric(
    *,
    name: t.Optional[str] = None,
    allowed_values: t.Optional[t.Union[t.Tuple[float, float], range]] = None,
    **metric_params: t.Any,
) -> t.Callable[[t.Callable[..., t.Any]], NumericMetricProtocol]:
    """
    Decorator for creating numeric/continuous metrics.

    This decorator transforms a regular function into a NumericMetric instance
    that outputs continuous values within a specified range.

    Parameters
    ----------
    name : str, optional
        Name for the metric. If not provided, uses the function name.
    allowed_values : Union[Tuple[float, float], range], optional
        The valid range for metric outputs as (min, max) tuple or range object.
        Default is (0.0, 1.0).
    **metric_params : Any
        Additional parameters to pass to the metric initialization.

    Returns
    -------
    Callable[[Callable[..., Any]], NumericMetricProtocol]
        A decorator that transforms a function into a NumericMetric instance.

    Examples
    --------
    >>> from ragas.metrics import numeric_metric
    >>>
    >>> @numeric_metric(name="relevance_score", allowed_values=(0.0, 1.0))
    >>> def calculate_relevance(user_input: str, response: str) -> float:
    ...     '''Calculate relevance score between 0 and 1.'''
    ...     # Simple word overlap example
    ...     user_words = set(user_input.lower().split())
    ...     response_words = set(response.lower().split())
    ...     if not user_words:
    ...         return 0.0
    ...     overlap = len(user_words & response_words)
    ...     return overlap / len(user_words)
    >>>
    >>> result = calculate_relevance(
    ...     user_input="What is Python?",
    ...     response="Python is a programming language"
    ... )
    >>> print(result.value)  # Numeric score between 0.0 and 1.0
    """
    if allowed_values is None:
        allowed_values = (0.0, 1.0)

    decorator_factory = create_metric_decorator()
    return decorator_factory(name=name, allowed_values=allowed_values, **metric_params)  # type: ignore[return-value]

ranking_metric

ranking_metric(*, name: Optional[str] = None, allowed_values: Optional[int] = None, **metric_params: Any) -> Callable[[Callable[..., Any]], RankingMetricProtocol]

Decorator for creating ranking/ordering metrics.

This decorator transforms a regular function into a RankingMetric instance that outputs ordered lists of items.

Parameters:

Name	Type	Description	Default
name	str	Name for the metric. If not provided, uses the function name.	None
allowed_values	int	Expected number of items in the ranking list. Default is 2.	None
**metric_params	Any	Additional parameters to pass to the metric initialization.	{}

Returns:

Type	Description
Callable[[Callable[..., Any]], RankingMetricProtocol]	A decorator that transforms a function into a RankingMetric instance.

Examples:

>>> from ragas.metrics import ranking_metric
>>>
>>> @ranking_metric(name="priority_ranker", allowed_values=3)
>>> def rank_by_urgency(user_input: str, responses: list) -> list:
...     '''Rank responses by urgency keywords.'''
...     urgency_keywords = ["urgent", "asap", "critical"]
...     scored = []
...     for resp in responses:
...         score = sum(kw in resp.lower() for kw in urgency_keywords)
...         scored.append((score, resp))
...     # Sort by score descending and return top items
...     ranked = sorted(scored, key=lambda x: x[0], reverse=True)
...     return [item[1] for item in ranked[:3]]
>>>
>>> result = rank_by_urgency(
...     user_input="What should I do first?",
...     responses=["This is urgent", "Take your time", "Critical issue!"]
... )
>>> print(result.value)  # Ranked list of responses

Source code in src/ragas/metrics/ranking.py

def ranking_metric(
    *,
    name: t.Optional[str] = None,
    allowed_values: t.Optional[int] = None,
    **metric_params: t.Any,
) -> t.Callable[[t.Callable[..., t.Any]], RankingMetricProtocol]:
    """
    Decorator for creating ranking/ordering metrics.

    This decorator transforms a regular function into a RankingMetric instance
    that outputs ordered lists of items.

    Parameters
    ----------
    name : str, optional
        Name for the metric. If not provided, uses the function name.
    allowed_values : int, optional
        Expected number of items in the ranking list. Default is 2.
    **metric_params : Any
        Additional parameters to pass to the metric initialization.

    Returns
    -------
    Callable[[Callable[..., Any]], RankingMetricProtocol]
        A decorator that transforms a function into a RankingMetric instance.

    Examples
    --------
    >>> from ragas.metrics import ranking_metric
    >>>
    >>> @ranking_metric(name="priority_ranker", allowed_values=3)
    >>> def rank_by_urgency(user_input: str, responses: list) -> list:
    ...     '''Rank responses by urgency keywords.'''
    ...     urgency_keywords = ["urgent", "asap", "critical"]
    ...     scored = []
    ...     for resp in responses:
    ...         score = sum(kw in resp.lower() for kw in urgency_keywords)
    ...         scored.append((score, resp))
    ...     # Sort by score descending and return top items
    ...     ranked = sorted(scored, key=lambda x: x[0], reverse=True)
    ...     return [item[1] for item in ranked[:3]]
    >>>
    >>> result = rank_by_urgency(
    ...     user_input="What should I do first?",
    ...     responses=["This is urgent", "Take your time", "Critical issue!"]
    ... )
    >>> print(result.value)  # Ranked list of responses
    """
    if allowed_values is None:
        allowed_values = 2

    decorator_factory = create_metric_decorator()
    return decorator_factory(name=name, allowed_values=allowed_values, **metric_params)  # type: ignore[return-value]