Module haystack_integrations.components.evaluators.deepeval.evaluator

DeepEvalEvaluator

@component
class DeepEvalEvaluator()

A component that uses the DeepEval framework to evaluate inputs against a specific metric. Supported metrics are defined by DeepEvalMetric.

Usage example:

from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric

evaluator = DeepEvalEvaluator(
    metric=DeepEvalMetric.FAITHFULNESS,
    metric_params={"model": "gpt-4"},
)
output = evaluator.run(
    questions=["Which is the most popular global sport?"],
    contexts=[
        [
            "Football is undoubtedly the world's most popular sport with"
            "major events like the FIFA World Cup and sports personalities"
            "like Ronaldo and Messi, drawing a followership of more than 4"
            "billion people."
        ]
    ],
    responses=["Football is the most popular sport with around 4 billion" "followers worldwide"],
)
print(output["results"])

DeepEvalEvaluator.__init__

def __init__(metric: Union[str, DeepEvalMetric],
             metric_params: Optional[Dict[str, Any]] = None)

Construct a new DeepEval evaluator.

Arguments:

• metric: The metric to use for evaluation.
• metric_params: Parameters to pass to the metric's constructor. Refer to the RagasMetric class for more details on required parameters.

DeepEvalEvaluator.run

@component.output_types(results=List[List[Dict[str, Any]]])
def run(**inputs) -> Dict[str, Any]

Run the DeepEval evaluator on the provided inputs.

Arguments:

• inputs: The inputs to evaluate. These are determined by the metric being calculated. See DeepEvalMetric for more information.

Returns:

A dictionary with a single results entry that contains a nested list of metric results. Each input can have one or more results, depending on the metric. Each result is a dictionary containing the following keys and values:

• name - The name of the metric.
• score - The score of the metric.
• explanation - An optional explanation of the score.

DeepEvalEvaluator.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Raises:

• DeserializationError: If the component cannot be serialized.

Returns:

Dictionary with serialized data.

DeepEvalEvaluator.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "DeepEvalEvaluator"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

Module haystack_integrations.components.evaluators.deepeval.metrics

DeepEvalMetric

class DeepEvalMetric(Enum)

Metrics supported by DeepEval.

All metrics require a model parameter, which specifies the model to use for evaluation. Refer to the DeepEval documentation for information on the supported models.

ANSWER_RELEVANCY

Answer relevancy.
Inputs - questions: List[str], contexts: List[List[str]], responses: List[str]

FAITHFULNESS

Faithfulness.
Inputs - questions: List[str], contexts: List[List[str]], responses: List[str]

CONTEXTUAL_PRECISION

Contextual precision.
Inputs - questions: List[str], contexts: List[List[str]], responses: List[str], ground_truths: List[str]
The ground truth is the expected response.

CONTEXTUAL_RECALL

Contextual recall.
Inputs - questions: List[str], contexts: List[List[str]], responses: List[str], ground_truths: List[str]
The ground truth is the expected response.\

CONTEXTUAL_RELEVANCE

Contextual relevance.
Inputs - questions: List[str], contexts: List[List[str]], responses: List[str]

DeepEvalMetric.from_str

@classmethod
def from_str(cls, string: str) -> "DeepEvalMetric"

Create a metric type from a string.

Arguments:

• string: The string to convert.

Returns:

The metric.