Metrics

Metrics module.

BaseMetric

Bases: ABC

Abstract class for metrics.

This class defines the interface for all metrics.

Attributes:

Name	Type	Description
name	str	The name of the metric.
required_fields	set[str]	The required fields for this metric to evaluate data.

can_evaluate(data)

Check if this metric can evaluate the given data.

Parameters:

Name	Type	Description	Default
data	MetricInput	The input data to check.	required

Returns:

Name	Type	Description
bool	bool	True if the metric can evaluate the data, False otherwise.

evaluate(data) async

Evaluate the metric on the given dataset.

Parameters:

Name	Type	Description	Default
data	MetricInput	The data to evaluate the metric on.	required

Returns:

Name	Type	Description
MetricOutput	MetricOutput	A dictionary where the key are the namespace and the value are the scores.

get_input_fields() classmethod

Return declared input field names if input_type is provided; otherwise None.

Returns:

Type	Description
list[str] | None	list[str] | None: The input fields.

get_input_spec() classmethod

Return structured spec for input fields if input_type is provided; otherwise None.

Returns:

Type	Description
list[dict[str, Any]] | None	list[dict[str, Any]] | None: The input spec.

CompletenessMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, prompt_builder=None, response_schema=None)

Bases: LMBasedMetric

Completeness metric.

Attributes:

Name	Type	Description
name	str	The name of the metric.
response_schema	ResponseSchema	The response schema to use for the metric.
prompt_builder	PromptBuilder	The prompt builder to use for the metric.
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.
model_credentials	str	The model credentials to use for the metric.

Initialize the CompletenessMetric class.

Default expected input: - query (str): The query to evaluate the completeness of the model's output. - expected_response (str): The expected response to evaluate the completeness of the model's output. - generated_response (str): The generated response to evaluate the completeness of the model's output.

Parameters:

Name	Type	Description	Default
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.	None
prompt_builder	PromptBuilder | None	The prompt builder to use for the metric. Defaults to default prompt builder.	None
response_schema	ResponseSchema | None	The response schema to use for the metric. Defaults to CompletenessResponseSchema.	None

DeepEvalAnswerRelevancyMetric(threshold=0.5, model=DefaultValues.MODEL, model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Answer Relevancy Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Initializes the DeepEvalAnswerRelevancyMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalBiasMetric(threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Bias Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more biased, closer to 0.0 means less biased.

Initializes the DeepEvalBiasMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalContextualPrecisionMetric(threshold=0.5, model=DefaultValues.MODEL, model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval ContextualPrecision Metric Integration.

Required Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - expected_response (str | list[str]): The expected response to evaluate the metric. Similar to expected_output in LLMTestCaseParams. If the expected response is a list, the responses are concatenated into a single string. - retrieved_context (str | list[str]): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalContextualPrecisionMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalContextualRecallMetric(threshold=0.5, model=DefaultValues.MODEL, model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval ContextualRecall Metric Integration.

Required Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - expected_response (str | list[str]): The expected response to evaluate the metric. Similar to expected_output in LLMTestCaseParams. If the expected response is a list, the responses are concatenated into a single string. - retrieved_context (str | list[str]): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalContextualRecallMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalContextualRelevancyMetric(threshold=0.5, model=DefaultValues.MODEL, model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval ContextualRelevancy Metric Integration.

Required Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - retrieved_context (str | list[str]): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalContextualRelevancyMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalFaithfulnessMetric(threshold=0.5, model=DefaultValues.MODEL, model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Faithfulness Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - retrieved_context (str | list[str]): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalFaithfulnessMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalGEvalMetric(name, evaluation_params, model=DefaultValues.MODEL, criteria=None, evaluation_steps=None, rubric=None, model_credentials=None, model_config=None, threshold=0.5, additional_context=None)

Bases: DeepEvalMetricFactory, PromptExtractionMixin

DeepEval GEval Metric Integration.

This class is a wrapper for the DeepEvalGEval class. It is used to wrap the GEval class and provide a unified interface for the DeepEval library.

GEval is a versatile evaluation metric framework that can be used to create custom evaluation metrics.

Available Fields: - query (str, optional): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str], optional): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - expected_response (str | list[str], optional): The expected response to evaluate the metric. Similar to expected_output in LLMTestCaseParams. If the expected response is a list, the responses are concatenated into a single string. - expected_retrieved_context (str | list[str], optional): The expected retrieved context to evaluate the metric. Similar to context in LLMTestCaseParams. If the expected retrieved context is a str, it will be converted into a list with a single element. - retrieved_context (str | list[str], optional): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalGEvalMetric class.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
evaluation_params	list[LLMTestCaseParams]	The evaluation parameters.	required
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	MODEL
criteria	str | None	The criteria to use for the metric. Defaults to None.	None
evaluation_steps	list[str] | None	The evaluation steps to use for the metric. Defaults to None.	None
rubric	list[Rubric] | None	The rubric to use for the metric. Defaults to None.	None
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
additional_context	str | None	Additional context like few-shot examples. Defaults to None.	None

get_full_prompt(data)

Get the full prompt that DeepEval generates for this metric.

Parameters:

Name	Type	Description	Default
data	MetricInput	The metric input.	required

Returns:

Name	Type	Description
str	str	The complete prompt (system + user) as a string.

DeepEvalHallucinationMetric(threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Hallucination Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - expected_retrieved_context (str | list[str]): The expected context to evaluate the metric. Similar to context in LLMTestCaseParams.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more hallucinated, closer to 0.0 means less hallucinated.

Initializes the DeepEvalHallucinationMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalJsonCorrectnessMetric(expected_schema, threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval JSON Correctness Metric Integration.

This metric evaluates whether a response is JSON correct according to a specified schema. It helps ensure that AI responses follow the expected JSON structure.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Categorical): - 0: The response is not JSON correct according to the schema. - 1: The response is JSON correct according to the schema.

Initializes the DeepEvalJsonCorrectnessMetric class.

Parameters:

Name	Type	Description	Default
expected_schema	Type[BaseModel]	The expected schema class (not instance) for the response. Example: ExampleSchema (the class, not an instance).	required
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4o".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

Raises:

Type	Description
ValueError	If expected_schema is not a valid BaseModel class.

DeepEvalMetric(metric, name)

Bases: BaseMetric

DeepEval Metric Integration.

Attributes:

Name	Type	Description
metric	BaseMetric	The DeepEval metric to wrap.
name	str	The name of the metric.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str], optional): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string. - expected_response (str | list[str], optional): The expected response to evaluate the metric. Similar to expected_output in LLMTestCaseParams. If the expected response is a list, the responses are concatenated into a single string. - expected_retrieved_context (str | list[str], optional): The expected retrieved context to evaluate the metric. Similar to context in LLMTestCaseParams. If the expected retrieved context is a str, it will be converted into a list with a single element. - retrieved_context (str | list[str], optional): The list of retrieved contexts to evaluate the metric. Similar to retrieval_context in LLMTestCaseParams. If the retrieved context is a str, it will be converted into a list with a single element.

Initializes the DeepEvalMetric class.

Parameters:

Name	Type	Description	Default
metric	BaseMetric	The DeepEval metric to wrap.	required
name	str	The name of the metric.	required

DeepEvalMetricFactory(name, model, model_credentials, model_config, **kwargs)

Bases: DeepEvalMetric, ABC

Abstract base class for creating DeepEval metrics with a shared model invoker.

Initializes the metric, handling common model invoker creation.

Parameters:

Name	Type	Description	Default
name	str	The name for the metric.	required
model	Union[str, ModelId, BaseLMInvoker]	The model identifier or an existing LM invoker instance.	required
model_credentials	Optional[str]	Credentials for the model, required if model is a string.	required
model_config	Optional[Dict[str, Any]]	Configuration for the model.	required
**kwargs		Additional arguments for the specific DeepEval metric constructor.	{}

DeepEvalMisuseMetric(domain, threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Misuse Metric Integration.

This metric evaluates whether a response contains inappropriate misuse of the model. It helps ensure that AI responses don't provide harmful or inappropriate misuse of the model.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more misuse, closer to 0.0 means less misuse.

Initializes the DeepEvalMisuseMetric class.

Parameters:

Name	Type	Description	Default
domain	str	The domain to evaluate the metric. Common domains include: "finance", "health", "legal", "personal", "investment".	required
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4o".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

Raises:

Type	Description
ValueError	If domain is empty or contains invalid values.

DeepEvalNonAdviceMetric(advice_types, threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Non-Advice Metric Integration.

This metric evaluates whether a response contains inappropriate advice types. It helps ensure that AI responses don't provide harmful or inappropriate advice.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 0.0 means more inappropriate advice, closer to 1.0 means less inappropriate advice.

Initializes the DeepEvalNonAdviceMetric class.

Parameters:

Name	Type	Description	Default
advice_types	List[str]	List of advice types to detect as inappropriate. Common types include: ["financial", "medical", "legal", "personal", "investment"].	required
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4o".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

Raises:

Type	Description
ValueError	If advice_types is empty or contains invalid values.

DeepEvalPIILeakageMetric(threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval PII Leakage Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more privacy violations, closer to 0.0 means less privacy violations.

Initializes the DeepEvalPIILeakageMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

DeepEvalPromptAlignmentMetric(prompt_instructions, threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Prompt Alignment Metric Integration.

This metric evaluates whether a response is aligned with the prompt instructions. It helps ensure that AI responses are aligned with the prompt instructions.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output inLLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more aligned with the prompt instructions, closer to 0.0 means less aligned with the prompt instructions.

Initializes the DeepEvalPromptAlignmentMetric class.

Parameters:

Name	Type	Description	Default
prompt_instructions	List[str]	a list of strings specifying the instructions you want followed in your prompt template.	required
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4o".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

Raises:

Type	Description
ValueError	If prompt_instructions is empty or contains invalid values.

DeepEvalRoleViolationMetric(role, threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Role Violation Metric Integration.

This metric evaluates whether a response contains role violations. It helps ensure that AI responses don't provide harmful or inappropriate role violations.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 0.0 means more role violations, closer to 1.0 means less role violations.

Initializes the DeepEvalRoleViolationMetric class.

Parameters:

Name	Type	Description	Default
role	str	The role to evaluate the metric. Common roles include: "helpful customer assistant", "medical insurance agent".	required
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4o".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

Raises:

Type	Description
ValueError	If role is empty or contains invalid values.

DeepEvalToxicityMetric(threshold=0.5, model='openai/gpt-4.1', model_credentials=None, model_config=None)

Bases: DeepEvalMetricFactory

DeepEval Toxicity Metric Integration.

Available Fields: - query (str): The query to evaluate the metric. Similar to input in LLMTestCaseParams. - generated_response (str | list[str]): The generated response to evaluate the metric. Similar to actual_output in LLMTestCaseParams. If the generated response is a list, the responses are concatenated into a single string.

Scoring (Continuous): - 0.0-1.0: Scale where closer to 1.0 means more toxic, closer to 0.0 means less toxic.

Initializes the DeepEvalToxicityMetric class.

Parameters:

Name	Type	Description	Default
threshold	float	The threshold to use for the metric. Defaults to 0.5.	0.5
model	str | ModelId | BaseLMInvoker	The model to use for the metric. Defaults to "openai/gpt-4.1".	'openai/gpt-4.1'
model_credentials	str | None	The model credentials to use for the metric. Defaults to None. Required when model is a string.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to None.	None

GEvalCompletenessMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, criteria=None, evaluation_steps=None, rubric=None, threshold=0.5, evaluation_params=None, additional_context=None)

Bases: DeepEvalGEvalMetric

GEval Completeness Metric.

This metric is used to evaluate the completeness of the generated output.

Required Fields: - query (str): The query to evaluate the completeness of the model's output. - generated_response (str): The generated response to evaluate the completeness of the model's output. - expected_response (str): The expected response to evaluate the completeness of the model's output.

Attributes:

Name	Type	Description
name	str	The name of the metric.
model	str | ModelId | BaseLMInvoker	The model to use for the metric.
model_credentials	str | None	The model credentials to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric.

Initialize the GEval Completeness Metric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric.	None
model_config	dict[str, Any] | None	The model config to use for the metric.	None
criteria	str | None	The criteria to use for the metric. default is DEFAULT_CRITERIA	None
evaluation_steps	list[str] | None	The evaluation steps to use for the metric. default is DEFAULT_EVALUATION_STEPS	None
rubric	list[Rubric] | None	The rubric to use for the metric. default is DEFAULT_RUBRIC	None
threshold	float	The threshold to use for the metric. default is 0.5	0.5
evaluation_params	list[LLMTestCaseParams] | None	The evaluation parameters to use for the metric. default is [LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT]	None
additional_context	str | None	Additional context like few-shot examples. Defaults to None.	None

GEvalGroundednessMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, criteria=None, evaluation_steps=None, rubric=None, threshold=0.5, evaluation_params=None, additional_context=None)

Bases: DeepEvalGEvalMetric

GEval Groundedness Metric.

This metric is used to evaluate the groundedness of the generated output.

Required Fields: - query (str): The query to evaluate the groundedness of the model's output. - generated_response (str): The generated response to evaluate the groundedness of the model's output. - retrieved_context (str): The retrieved context to evaluate the groundedness of the model's output.

Attributes:

Name	Type	Description
name	str	The name of the metric.
model	str | ModelId | BaseLMInvoker	The model to use for the metric.
model_credentials	str | None	The model credentials to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric.

Initialize the GEval Groundedness Metric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric.	None
model_config	dict[str, Any] | None	The model config to use for the metric.	None
criteria	str | None	The criteria to use for the metric. default is DEFAULT_CRITERIA	None
evaluation_steps	list[str] | None	The evaluation steps to use for the metric. default is DEFAULT_EVALUATION_STEPS	None
rubric	list[Rubric] | None	The rubric to use for the metric. default is DEFAULT_RUBRIC	None
threshold	float	The threshold to use for the metric. default is 0.5	0.5
evaluation_params	list[LLMTestCaseParams] | None	The evaluation parameters to use for the metric. default is [LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.CONTEXT]	None
additional_context	str | None	Additional context like few-shot examples. Defaults to None.	None

GEvalRedundancyMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, criteria=None, evaluation_steps=None, rubric=None, threshold=0.5, evaluation_params=None, additional_context=None)

Bases: DeepEvalGEvalMetric

GEval Redundancy Metric.

This metric is used to evaluate the redundancy of the generated output.

Required Fields: - query (str): The query to evaluate the redundancy of the model's output. - generated_response (str): The generated response to evaluate the redundancy of the model's output.

Attributes:

Name	Type	Description
name	str	The name of the metric.
model	str | ModelId | BaseLMInvoker	The model to use for the metric.
model_credentials	str | None	The model credentials to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric.

Initialize the GEval Redundancy Metric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric.	None
model_config	dict[str, Any] | None	The model config to use for the metric.	None
criteria	str | None	The criteria to use for the metric. default is REDUNDANCY_CRITERIA	None
evaluation_steps	list[str] | None	The evaluation steps to use for the metric. default is REDUNDANCY_EVALUATION_STEPS	None
rubric	list[Rubric] | None	The rubric to use for the metric. default is REDUNDANCY_RUBRIC	None
threshold	float	The threshold to use for the metric. default is 0.5	0.5
evaluation_params	list[LLMTestCaseParams] | None	The evaluation parameters to use for the metric. default is [LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT]	None
additional_context	str | None	Additional context like few-shot examples. Defaults to None.	None

GroundednessMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, prompt_builder=None, response_schema=None)

Bases: LMBasedMetric

Groundedness metric.

Attributes:

Name	Type	Description
name	str	The name of the metric.
response_schema	ResponseSchema	The response schema to use for the metric.
prompt_builder	PromptBuilder	The prompt builder to use for the metric.
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.
model_credentials	str	The model credentials to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.

Initialize the GroundednessMetric class.

Default expected input: - query (str): The query to evaluate the groundedness of the model's output. - retrieved_context (str): The retrieved context to evaluate the groundedness of the model's output. - generated_response (str): The generated response to evaluate the groundedness of the model's output.

Parameters:

Name	Type	Description	Default
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.	MODEL
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.	None
model_credentials	str | None	The model credentials to use for the metric. Defaults to None.	None
prompt_builder	PromptBuilder | None	The prompt builder to use for the metric. Defaults to default prompt builder.	None
response_schema	ResponseSchema | None	The response schema to use for the metric. Defaults to GroundednessResponseSchema.	None

LMBasedMetric(name, response_schema, prompt_builder, model=DefaultValues.MODEL, model_credentials=None, model_config=None, parse_response_fn=None)

Bases: BaseMetric

A multi purpose LM-based metric class.

This class is a multi purpose LM-based metric class that can be used to evaluate the performance of a LM-based metric. It can be used to evaluate the performance of a LM-based metric by providing a response schema, a prompt builder, a model id, and a model credentials.

Attributes:

Name	Type	Description
name	str	The name of the metric.
response_schema	ResponseSchema	The response schema to use for the metric.
prompt_builder	PromptBuilder	The prompt builder to use for the metric.
model_credentials	str	The model credentials to use for the metric.
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty.

Initialize the LMBasedMetric class.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
response_schema	ResponseSchema	The response schema to use for the metric.	required
prompt_builder	PromptBuilder	The prompt builder to use for the metric.	required
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.	None
parse_response_fn	Callable[[str | LMOutput], MetricOutput] | None	The function to use to parse the response from the LM. Defaults to a function that parses the response from the LM.	None

LangChainAgentEvalsLLMAsAJudgeMetric(name, prompt, model, credentials=None, config=None, schema=None, feedback_key='trajectory_accuracy', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainAgentEvalsMetric

A metric that uses LangChain AgentEvals to evaluate Agent as a judge.

Available Fields: - agent_trajectory (list[dict[str, Any]]): The agent trajectory. - expected_agent_trajectory (list[dict[str, Any]]): The expected agent trajectory.

Attributes:

Name	Type	Description
name	str	The name of the metric.
evaluator	SimpleAsyncEvaluator	The evaluator to use.

Initialize the LangChainAgentEvalsLLMAsAJudgeMetric.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
prompt	str	The evaluation prompt, can be a string template, LangChain prompt template, or callable that returns a list of chat messages. Note that the default prompt allows a rubric in addition to the typical "inputs", "outputs", and "reference_outputs" parameters.	required
model	str | ModelId | BaseLMInvoker	The model to use.	required
credentials	str | None	The credentials to use for the model. Defaults to None.	None
config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "trajectory_accuracy".	'trajectory_accuracy'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainAgentEvalsMetric(name, evaluator)

Bases: BaseMetric

A metric that uses LangChain AgentEvals to evaluate Agent.

Available Fields: - agent_trajectory (list[dict[str, Any]]): The agent trajectory. - expected_agent_trajectory (list[dict[str, Any]]): The expected agent trajectory.

Attributes:

Name	Type	Description
name	str	The name of the metric.
evaluator	SimpleAsyncEvaluator	The evaluator to use.

Initialize the LangChainAgentEvalsMetric.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
evaluator	SimpleAsyncEvaluator	The evaluator to use.	required

LangChainAgentTrajectoryAccuracyMetric(model, prompt=None, model_credentials=None, model_config=None, schema=None, feedback_key='trajectory_accuracy', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None, use_reference=True)

Bases: LangChainAgentEvalsLLMAsAJudgeMetric

A metric that uses LangChain AgentEvals to evaluate the trajectory accuracy of the agent.

Available Fields: - agent_trajectory (list[dict[str, Any]]): The agent trajectory. - expected_agent_trajectory (list[dict[str, Any]] | None, optional): The expected agent trajectory.

Attributes:

Name	Type	Description
name	str	The name of the metric.
evaluator	SimpleAsyncEvaluator	The evaluator to use.

Initialize the LangChainAgentTrajectoryAccuracyMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	required
prompt	str | None	The prompt to use. Defaults to None.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "trajectory_accuracy".	'trajectory_accuracy'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None
use_reference	bool	If True, uses the expected agent trajectory to evaluate the trajectory accuracy. Defaults to True. If False, the TRAJECTORY_ACCURACY_CUSTOM_PROMPT is used to evaluate the trajectory accuracy. If custom prompt is provided, this parameter will be ignored.	True

LangChainConcisenessMetric(model=DefaultValues.MODEL, system=None, model_credentials=None, model_config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsLLMAsAJudgeMetric

A metric that uses LangChain and OpenEvals to evaluate the conciseness of the LLM.

Required Fields: - query (str): The query to evaluate the conciseness of. - generated_response (str): The generated response to evaluate the conciseness of.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainConcisenessMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	MODEL
system	str | None	Optional system message to prepend to the prompt.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainCorrectnessMetric(model=DefaultValues.MODEL, system=None, model_credentials=None, model_config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsLLMAsAJudgeMetric

A metric that uses LangChain and OpenEvals to evaluate the correctness of the LLM.

Required Fields: - query (str): The query to evaluate the correctness of. - generated_response (str): The generated response to evaluate the correctness of. - expected_response (str): The expected response to evaluate the correctness of.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainCorrectnessMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	MODEL
system	str | None	Optional system message to prepend to the prompt.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainGroundednessMetric(model=DefaultValues.MODEL, system=None, model_credentials=None, model_config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsLLMAsAJudgeMetric

A metric that uses LangChain and OpenEvals to evaluate the groundedness of the LLM.

Required Fields: - generated_response (str | list[str]): The generated response to evaluate the groundedness of. - retrieved_context (str | list[str]): The retrieved context to evaluate the groundedness of.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainGroundednessMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	MODEL
system	str | None	Optional system message to prepend to the prompt.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainHallucinationMetric(model=DefaultValues.MODEL, system=None, model_credentials=None, model_config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsLLMAsAJudgeMetric

A metric that uses LangChain and OpenEvals to evaluate the hallucination of the LLM.

Required Fields: - query (str): The query to evaluate the hallucination of. - generated_response (str): The generated response to evaluate the hallucination of. - expected_retrieved_context (str): The expected retrieved context to evaluate the hallucination of. - expected_response (str, optional): Additional information to help the model evaluate the hallucination.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainHallucinationMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	MODEL
system	str | None	Optional system message to prepend to the prompt.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainHelpfulnessMetric(model=DefaultValues.MODEL, system=None, model_credentials=None, model_config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsLLMAsAJudgeMetric

A metric that uses LangChain and OpenEvals to evaluate the helpfulness of the LLM.

Required Fields: - query (str): The query to evaluate the helpfulness of. - generated_response (str): The generated response to evaluate the helpfulness of.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainHelpfulnessMetric.

Parameters:

Name	Type	Description	Default
model	str | ModelId | BaseLMInvoker	The model to use.	MODEL
system	str | None	Optional system message to prepend to the prompt.	None
model_credentials	str | None	The credentials to use for the model. Defaults to None.	None
model_config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainOpenEvalsLLMAsAJudgeMetric(name, prompt, model, system=None, credentials=None, config=None, schema=None, feedback_key='score', continuous=False, choices=None, use_reasoning=True, few_shot_examples=None)

Bases: LangChainOpenEvalsMetric

A metric that uses LangChain and OpenEvals to evaluate the LLM as a judge.

Attributes:

Name	Type	Description
name	str	The name of the metric.
prompt	str	The prompt to use.
model	str | ModelId | BaseLMInvoker	The model to use.

Initialize the LangChainOpenEvalsLLMAsAJudgeMetric.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
prompt	str	The evaluation prompt, can be a string template, LangChain prompt template, or callable that returns a list of chat messages.	required
model	str | ModelId | BaseLMInvoker	The model to use.	required
system	str | None	Optional system message to prepend to the prompt.	None
credentials	str | None	The credentials to use for the model. Defaults to None.	None
config	dict[str, Any] | None	The config to use for the model. Defaults to None.	None
schema	ResponseSchema | None	The schema to use for the model. Defaults to None.	None
feedback_key	str	Key used to store the evaluation result, defaults to "score".	'score'
continuous	bool	If True, score will be a float between 0 and 1. If False, score will be boolean. Defaults to False.	False
choices	list[float] | None	Optional list of specific float values the score must be chosen from. Defaults to None.	None
use_reasoning	bool	If True, includes explanation for the score in the output. Defaults to True.	True
few_shot_examples	list[FewShotExample] | None	Optional list of example evaluations to append to the prompt. Defaults to None.	None

LangChainOpenEvalsMetric(name, evaluator)

Bases: BaseMetric

A metric that uses LangChain and OpenEvals.

Attributes:

Name	Type	Description
name	str	The name of the metric.
evaluator	Union[SimpleAsyncEvaluator, Callable[..., Awaitable[Any]]]	The evaluator to use.

Initialize the LangChainOpenEvalsMetric.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric.	required
evaluator	Union[SimpleAsyncEvaluator, Callable[..., Awaitable[Any]]]	The evaluator to use.	required

PyTrecMetric(metrics=None, k=20)

Bases: BaseMetric

Pytrec_eval metric.

Required fields: - retrieved_chunks: The retrieved chunk ids with their similarity score. - ground_truth_chunk_ids: The ground truth chunk ids.

Example:

data = RetrievalData(
    retrieved_chunks={
        "chunk1": 0.9,
        "chunk2": 0.8,
        "chunk3": 0.7,
    },
    ground_truth_chunk_ids=["chunk1", "chunk2", "chunk3"],
)
metric = PyTrecMetric()
await metric.evaluate(data)

Attributes:

Name	Type	Description
name	str	The name of the metric.
metrics	list[PyTrecEvalMetric | str] | set[PyTrecEvalMetric | str] | None	The metrics to evaluate.
k_values	int | list[int]	The number of retrieved chunks to consider.

Initializes the PyTrecMetric.

Parameters:

Name	Type	Description	Default
metrics	list[PyTrecEvalMetric | str] | set[PyTrecEvalMetric | str] | None	The metrics to evaluate. Defaults to all metrics.	None
k	int | list[int]	The number of retrieved chunks to consider. Defaults to 20.	20

RAGASMetric(metric, name=None, callbacks=None, timeout=None)

Bases: BaseMetric

RAGAS metric.

RAGAS is a metric for evaluating the quality of RAG systems.

Attributes:

Name	Type	Description
metric	SingleTurnMetric	The Ragas metric to use.
name	str	The name of the metric.
callbacks	Callbacks	The callbacks to use.
timeout	int	The timeout for the metric.

Available Fields: - query (str): The query to evaluate the metric. Similar to user_input in SingleTurnSample. - generated_response (str | list[str], optional): The generated response to evaluate the metric. Similar to response in SingleTurnSample. If the generated response is a list, the responses are concatenated into a single string. For multiple responses, use list[str]. - expected_response (str | list[str], optional): The expected response to evaluate the metric. Similar to reference in SingleTurnSample. If the expected response is a list, the responses are concatenated into a single string. - expected_retrieved_context (str | list[str], optional): The expected retrieved context to evaluate the metric. Similar to reference_contexts in SingleTurnSample. If the expected retrieved context is a str, it will be converted into a list with a single element. - retrieved_context (str | list[str], optional): The retrieved context to evaluate the metric. Similar to retrieved_contexts in SingleTurnSample. If the retrieved context is a str, it will be converted into a list with a single element. - rubrics (dict[str, str], optional): The rubrics to evaluate the metric. Similar to rubrics in SingleTurnSample.

Initialize the RAGASMetric.

Parameters:

Name	Type	Description	Default
metric	SingleTurnMetric	The Ragas metric to use.	required
name	str	The name of the metric. Default is the name of the metric.	None
callbacks	Callbacks	The callbacks to use. Default is None.	None
timeout	int	The timeout for the metric. Default is None.	None

RagasContextPrecisionWithoutReference(lm_model, lm_model_credentials=None, lm_model_config=None, **kwargs)

Bases: RAGASMetric

RAGAS Context Recall metric.

Required Fields: - query (str): The query to recall the context for. - generated_response (str): The generated response to recall the context for. - retrieved_contexts (list[str]): The retrieved contexts to recall the context for.

Initialize the RagasContextPrecisionWithoutReference metric.

Parameters:

Name	Type	Description	Default
lm_model	str | ModelId | BaseLMInvoker	The language model to use.	required
lm_model_credentials	str | None	The credentials to use for the language model. Default is None.	None
lm_model_config	dict[str, Any] | None	The configuration to use for the language model. Default is None.	None
**kwargs		Additional keyword arguments to pass to the RagasContextRecall metric.	{}

RagasContextRecall(lm_model, lm_model_credentials=None, lm_model_config=None, **kwargs)

Bases: RAGASMetric

RAGAS Context Recall metric.

Required Fields: - query (str): The query to recall the context for. - generated_response (str): The generated response to recall the context for. - expected_response (str): The expected response to recall the context for. - retrieved_contexts (list[str]): The retrieved contexts to recall the context for.

Initialize the RagasContextRecall metric.

Parameters:

Name	Type	Description	Default
lm_model	str | ModelId | BaseLMInvoker	The language model to use.	required
lm_model_credentials	str | None	The credentials to use for the language model. Default is None.	None
lm_model_config	dict[str, Any] | None	The configuration to use for the language model. Default is None.	None
**kwargs		Additional keyword arguments to pass to the RagasContextRecall metric.	{}

RagasFactualCorrectness(lm_model=DefaultValues.MODEL, lm_model_credentials=None, lm_model_config=None, **kwargs)

Bases: RAGASMetric

RAGAS Factual Correctness metric.

Required Fields: - query (str): The query to recall the context for. - generated_response (str): The generated response to recall the context for.

Initialize the RagasFactualCorrectness metric.

Parameters:

Name	Type	Description	Default
lm_model	str | ModelId | BaseLMInvoker	The language model to use.	MODEL
lm_model_credentials	str | None	The credentials to use for the language model. Default is None.	None
lm_model_config	dict[str, Any] | None	The configuration to use for the language model. Default is None.	None
**kwargs		Additional keyword arguments to pass to the RagasFactualCorrectness metric.	{}

RedundancyMetric(model=DefaultValues.MODEL, model_credentials=None, model_config=None, prompt_builder=None, response_schema=None)

Bases: LMBasedMetric

Redundancy metric.

Attributes:

Name	Type	Description
name	str	The name of the metric.
response_schema	ResponseSchema	The response schema to use for the metric.
prompt_builder	PromptBuilder	The prompt builder to use for the metric.
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.
model_credentials	str	The model credentials to use for the metric.

Initialize the RedundancyMetric class.

Default expected input: - query (str): The query to evaluate the redundancy of the model's output. - generated_response (str): The generated response to evaluate the redundancy of the model's output.

Parameters:

Name	Type	Description	Default
model	Union[str, ModelId, BaseLMInvoker]	The model to use for the metric.	MODEL
model_credentials	str | None	The model credentials to use for the metric. Defaults to None.	None
model_config	dict[str, Any] | None	The model config to use for the metric. Defaults to an empty dictionary.	None
prompt_builder	PromptBuilder | None	The prompt builder to use for the metric. Defaults to default prompt builder.	None
response_schema	ResponseSchema | None	The response schema to use for the metric. Defaults to RedundancyResponseSchema.	None

TopKAccuracy(k=20)

Bases: BaseMetric

Top K Accuracy metric.

Required fields: - retrieved_chunks: The retrieved chunk ids with their similarity score. - ground_truth_chunk_ids: The ground truth chunk ids.

Example:

data = RetrievalData(
    retrieved_chunks={
        "chunk1": 0.9,
        "chunk2": 0.8,
        "chunk3": 0.7,
    },
    ground_truth_chunk_ids=["chunk1", "chunk2", "chunk3"],
)
metric = TopKAccuracy()
await metric.evaluate(data)

Attributes:

Name	Type	Description
name	str	The name of the metric.
k_values	list[int]	The number of retrieved chunks to consider.

Initializes the TopKAccuracy.

Parameters:

Name	Type	Description	Default
k	list[int] | int	The number of retrieved chunks to consider. Defaults to 20.	20

top_k_accuracy(qrels, results)

Evaluates the top k accuracy.

Parameters:

Name	Type	Description	Default
qrels	dict[str, dict[str, int]]	The ground truth of the retrieved chunks. There are two possible values:	required
-	1	the chunk is relevant to the query.	required
-	0	the chunk is not relevant to the query.	required
results	dict[str, dict[str, float]]	The retrieved chunks with their similarity score.	required

Returns:

Type	Description
dict[str, float]	dict[str, float]: The top k accuracy.

Example:

qrels = {
    "q1": {"chunk1": 1, "chunk2": 1},
}
results = {"q1": {"chunk1": 0.9, "chunk2": 0.8, "chunk3": 0.7}}