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Evals
(
api_client_
:
google
.
genai
.
_api_client
.
BaseApiClient
)
API documentation for Evals
class.
Methods
batch_evaluate
batch_evaluate
(
*
,
dataset
:
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvaluationDataset
,
vertexai
.
_genai
.
types
.
EvaluationDatasetDict
,
],
metrics
:
list
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
Metric
,
vertexai
.
_genai
.
types
.
MetricDict
]
],
dest
:
str
,
config
:
typing
.
Optional
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvaluateDatasetConfig
,
vertexai
.
_genai
.
types
.
EvaluateDatasetConfigDict
,
]
]
=
None
)
-
> vertexai
.
_genai
.
types
.
EvaluateDatasetOperation
Evaluates a dataset based on a set of given metrics.
evaluate
evaluate
(
*
,
dataset
:
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvaluationDataset
,
vertexai
.
_genai
.
types
.
EvaluationDatasetDict
,
list
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvaluationDataset
,
vertexai
.
_genai
.
types
.
EvaluationDatasetDict
,
]
],
],
metrics
:
typing
.
Optional
[
list
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
Metric
,
vertexai
.
_genai
.
types
.
MetricDict
]
]
]
=
None
,
config
:
typing
.
Optional
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvaluateMethodConfig
,
vertexai
.
_genai
.
types
.
EvaluateMethodConfigDict
,
]
]
=
None
)
-
> vertexai
.
_genai
.
types
.
EvaluationResult
Evaluates candidate responses in the provided dataset(s) using the specified metrics.
evaluate_instances
evaluate_instances
(
*
,
metric_config
:
vertexai
.
_genai
.
types
.
_EvaluateInstancesRequestParameters
)
-
> vertexai
.
_genai
.
types
.
EvaluateInstancesResponse
Evaluates an instance of a model.
generate_rubrics
generate_rubrics
(
*
,
src
:
typing
.
Union
[
str
,
pd
.
DataFrame
,
vertexai
.
_genai
.
types
.
EvaluationDataset
],
rubric_group_name
:
str
,
prompt_template
:
typing
.
Optional
[
str
]
=
None
,
generator_model_config
:
typing
.
Optional
[
genai_types
.
AutoraterConfigOrDict
]
=
None
,
rubric_content_type
:
typing
.
Optional
[
types
.
RubricContentType
]
=
None
,
rubric_type_ontology
:
typing
.
Optional
[
list
[
str
]]
=
None
,
predefined_spec_name
:
typing
.
Optional
[
typing
.
Union
[
str
,
types
.
PrebuiltMetric
]
]
=
None
,
metric_spec_parameters
:
typing
.
Optional
[
dict
[
str
,
typing
.
Any
]]
=
None
,
config
:
typing
.
Optional
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
RubricGenerationConfig
,
vertexai
.
_genai
.
types
.
RubricGenerationConfigDict
,
]
]
=
None
)
-
> vertexai
.
_genai
.
types
.
EvaluationDataset
Generates rubrics for each prompt in the source and adds them as a new column structured as a dictionary.
You can generate rubrics by providing either:
- A
predefined_spec_nameto use a Vertex AI backend recipe. - A
prompt_templatealong with other configuration parameters (generator_model_config,rubric_content_type,rubric_type_ontology) for custom rubric generation.
These two modes are mutually exclusive.
run
run
()
-
> vertexai
.
_genai
.
types
.
EvaluateInstancesResponse
Evaluates an instance of a model.
This should eventually call _evaluate_instances()
run_inference
run_inference
(
*
,
model
:
typing
.
Union
[
str
,
typing
.
Callable
[[
typing
.
Any
],
typing
.
Any
]],
src
:
typing
.
Union
[
str
,
pandas
.
core
.
frame
.
DataFrame
,
vertexai
.
_genai
.
types
.
EvaluationDataset
],
config
:
typing
.
Optional
[
typing
.
Union
[
vertexai
.
_genai
.
types
.
EvalRunInferenceConfig
,
vertexai
.
_genai
.
types
.
EvalRunInferenceConfigDict
,
]
]
=
None
)
-
> vertexai
.
_genai
.
types
.
EvaluationDataset
Runs inference on a dataset for evaluation.
