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# Modifications Copyright OpenSearch Contributors. See
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# http://www.apache.org/licenses/LICENSE-2.0
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import collections.abc as collections_abc
from typing import Any, Optional
from .utils import DslBase
def SF(name_or_sf: Any, **params: Any) -> Any: # pylint: disable=invalid-name
# {"script_score": {"script": "_score"}, "filter": {}}
if isinstance(name_or_sf, collections_abc.Mapping):
if params:
raise ValueError("SF() cannot accept parameters when passing in a dict.")
kwargs = {}
sf = name_or_sf.copy() # type: ignore
for k in ScoreFunction._param_defs:
if k in name_or_sf:
kwargs[k] = sf.pop(k)
# not sf, so just filter+weight, which used to be boost factor
if not sf:
name = "boost_factor"
# {'FUNCTION': {...}}
elif len(sf) == 1:
name, params = sf.popitem()
else:
raise ValueError("SF() got an unexpected fields in the dictionary: %r" % sf)
# boost factor special case, see https://github.com/elastic/elasticsearch/issues/6343
if not isinstance(params, collections_abc.Mapping):
params = {"value": params}
# mix known params (from _param_defs) and from inside the function
kwargs.update(params)
return ScoreFunction.get_dsl_class(name)(**kwargs)
# ScriptScore(script="_score", filter=Q())
if isinstance(name_or_sf, ScoreFunction):
if params:
raise ValueError(
"SF() cannot accept parameters when passing in a ScoreFunction object."
)
return name_or_sf
# "script_score", script="_score", filter=Q()
return ScoreFunction.get_dsl_class(name_or_sf)(**params)
[docs]class ScoreFunction(DslBase):
_type_name: str = "score_function"
_type_shortcut = staticmethod(SF)
_param_defs = {
"query": {"type": "query"},
"filter": {"type": "query"},
"weight": {},
}
name: Optional[str] = None
[docs] def to_dict(self) -> Any:
d = super(ScoreFunction, self).to_dict()
# filter and query dicts should be at the same level as us
for k in self._param_defs:
if k in d[self.name]:
d[k] = d[self.name].pop(k)
return d
class ScriptScore(ScoreFunction):
name = "script_score"
class BoostFactor(ScoreFunction):
name = "boost_factor"
def to_dict(self) -> Any:
d = super(BoostFactor, self).to_dict()
if "value" in d[self.name]:
d[self.name] = d[self.name].pop("value")
else:
del d[self.name]
return d
class RandomScore(ScoreFunction):
name = "random_score"
class FieldValueFactor(ScoreFunction):
name = "field_value_factor"
class Linear(ScoreFunction):
name = "linear"
class Gauss(ScoreFunction):
name = "gauss"
class Exp(ScoreFunction):
name = "exp"