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docs: Update uptrain-haystack docstrings #530

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Mar 5, 2024
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docs: Update uptrain-haystack docstrings #530

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86 changes: 47 additions & 39 deletions integrations/uptrain/src/haystack_integrations/components/evaluators/uptrain/evaluator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,33 @@
@component
class UpTrainEvaluator:
"""
A component that uses the UpTrain framework to evaluate inputs against a specific metric.

The supported metrics are defined by :class:`UpTrainMetric`. The inputs of the component
metric-dependent. The output is a nested list of evaluation results where each inner list
contains the results for a single input.
A component that uses the [UpTrain framework](https://docs.uptrain.ai/getting-started/introduction)
to evaluate inputs against a specific metric. Supported metrics are defined by `UpTrainMetric`.

Usage example:
```python
from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric
from haystack.utils import Secret

evaluator = UpTrainEvaluator(
metric=UpTrainMetric.FACTUAL_ACCURACY,
api="openai",
api_key=Secret.from_env_var("OPENAI_API_KEY"),
)
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"])
```
"""

_backend_metric: Union[Evals, ParametricEval]
Expand All @@ -44,15 +66,15 @@ def __init__(
The metric to use for evaluation.
:param metric_params:
Parameters to pass to the metric's constructor.
Refer to the `UpTrainMetric` class for more details
on required parameters.
:param api:
The API to use for evaluation.

Supported APIs: "openai", "uptrain".
The API to use for evaluation. Supported APIs:
`openai`, `uptrain`.
:param api_key:
The API key to use.
:param api_params:
Additional parameters to pass to the API client.

Required parameters for the UpTrain API: `project_name`.
"""
self.metric = metric if isinstance(metric, UpTrainMetric) else UpTrainMetric.from_str(metric)
Expand All @@ -69,38 +91,20 @@ def __init__(
@component.output_types(results=List[List[Dict[str, Any]]])
def run(self, **inputs) -> Dict[str, Any]:
"""
Run the UpTrain evaluator.

Example:
```python
pipeline = Pipeline()
evaluator = UpTrainEvaluator(
metric=UpTrainMetric.FACTUAL_ACCURACY,
api="openai",
api_key=Secret.from_env_var("OPENAI_API_KEY"),
)
pipeline.add_component("evaluator", evaluator)

# Each metric expects a specific set of parameters as input. Refer to the
# UpTrainMetric class' documentation for more details.
output = pipeline.run({"evaluator": {
"questions": ["question],
"contexts": [["context", "another context"]],
"responses": ["response"]
}})
```
Run the UpTrain evaluator on the provided inputs.

:param inputs:
The inputs to evaluate. These are determined by the
metric being calculated. See `UpTrainMetric` for more
information.
:returns:
A nested list of metric results. Each input can have one or more
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.
- `name` - The name of the metric.
- `score` - The score of the metric.
- `explanation` - An optional explanation of the score.
"""
# The backend requires random access to the data, so we can't stream it.
InputConverters.validate_input_parameters(self.metric, self.descriptor.input_parameters, inputs)
Expand All @@ -125,7 +129,12 @@ def run(self, **inputs) -> Dict[str, Any]:

def to_dict(self) -> Dict[str, Any]:
"""
Serialize this component to a dictionary.
Serializes the component to a dictionary.

:returns:
Dictionary with serialized data.
:raises DeserializationError:
If the component cannot be serialized.
"""

def check_serializable(obj: Any):
Expand All @@ -151,18 +160,17 @@ def check_serializable(obj: Any):
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "UpTrainEvaluator":
"""
Deserialize a component from a dictionary.
Deserializes the component from a dictionary.

:param data:
The dictionary to deserialize from.
Dictionary to deserialize from.
:returns:
Deserialized component.
"""
deserialize_secrets_inplace(data["init_parameters"], ["api_key"])
return default_from_dict(cls, data)

def _init_backend(self):
"""
Initialize the UpTrain backend.
"""
if isinstance(self.descriptor.backend, Evals):
if self.metric_params is not None:
msg = (
Expand Down
31 changes: 17 additions & 14 deletions integrations/uptrain/src/haystack_integrations/components/evaluators/uptrain/metrics.py
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Expand Up @@ -14,48 +14,51 @@ class UpTrainMetric(Enum):
Metrics supported by UpTrain.
"""

#: Context relevance.
#: Context relevance.\
#: Inputs - `questions: List[str], contexts: List[List[str]]`
CONTEXT_RELEVANCE = "context_relevance"

#: Factual accuracy.
#: Factual accuracy.\
#: Inputs - `questions: List[str], contexts: List[List[str]], responses: List[str]`
FACTUAL_ACCURACY = "factual_accuracy"

#: Response relevance.
#: Response relevance.\
#: Inputs - `questions: List[str], responses: List[str]`
RESPONSE_RELEVANCE = "response_relevance"

#: Response completeness.
#: Response completeness.\
#: Inputs - `questions: List[str], responses: List[str]`
RESPONSE_COMPLETENESS = "response_completeness"

#: Response completeness with respect to context.
#: Response completeness with respect to context.\
#: Inputs - `questions: List[str], contexts: List[List[str]], responses: List[str]`
RESPONSE_COMPLETENESS_WRT_CONTEXT = "response_completeness_wrt_context"

#: Response consistency.
#: Response consistency.\
#: Inputs - `questions: List[str], contexts: List[List[str]], responses: List[str]`
RESPONSE_CONSISTENCY = "response_consistency"

#: Response conciseness.
#: Response conciseness.\
#: Inputs - `questions: List[str], responses: List[str]`
RESPONSE_CONCISENESS = "response_conciseness"

#: Language critique.
#: Language critique.\
#: Inputs - `responses: List[str]`
CRITIQUE_LANGUAGE = "critique_language"

#: Tone critique.
#: Inputs - `responses: List[str]`
#: Tone critique.\
#: Inputs - `responses: List[str]`\
#: Parameters - `llm_persona: str`
CRITIQUE_TONE = "critique_tone"

#: Guideline adherence.
#: Inputs - `questions: List[str], responses: List[str]`
#: Guideline adherence.\
#: Inputs - `questions: List[str], responses: List[str]`\
#: Parameters - `guideline: str`, `guideline_name: str`, `response_schema: Optional[str]`
GUIDELINE_ADHERENCE = "guideline_adherence"

#: Response matching.
#: Inputs - `responses: List[str], ground_truths: List[str]`
#: Response matching.\
#: Inputs - `responses: List[str], ground_truths: List[str]`\
#: Parameters - `method: str`
RESPONSE_MATCHING = "response_matching"

def __str__(self):
Expand Down