elasticsearch_serverless/_async/client/__init__.py [3089:3198]:
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self,
*,
index: t.Optional[str] = None,
docs: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None,
error_trace: t.Optional[bool] = None,
field_statistics: t.Optional[bool] = None,
fields: t.Optional[t.Union[str, t.Sequence[str]]] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
human: t.Optional[bool] = None,
ids: t.Optional[t.Sequence[str]] = None,
offsets: t.Optional[bool] = None,
payloads: t.Optional[bool] = None,
positions: t.Optional[bool] = None,
preference: t.Optional[str] = None,
pretty: t.Optional[bool] = None,
realtime: t.Optional[bool] = None,
routing: t.Optional[str] = None,
term_statistics: t.Optional[bool] = None,
version: t.Optional[int] = None,
version_type: t.Optional[
t.Union[str, t.Literal["external", "external_gte", "force", "internal"]]
] = None,
body: t.Optional[t.Dict[str, t.Any]] = None,
) -> ObjectApiResponse[t.Any]:
"""
.. raw:: html
Get multiple term vectors.
Get multiple term vectors with a single request.
You can specify existing documents by index and ID or provide artificial documents in the body of the request.
You can specify the index in the request body or request URI.
The response contains a docs array with all the fetched termvectors.
Each element has the structure provided by the termvectors API.
Artificial documents
You can also use mtermvectors to generate term vectors for artificial documents provided in the body of the request.
The mapping used is determined by the specified _index.
``_
:param index: The name of the index that contains the documents.
:param docs: An array of existing or artificial documents.
:param field_statistics: If `true`, the response includes the document count,
sum of document frequencies, and sum of total term frequencies.
:param fields: A comma-separated list or wildcard expressions of fields to include
in the statistics. It is used as the default list unless a specific field
list is provided in the `completion_fields` or `fielddata_fields` parameters.
:param ids: A simplified syntax to specify documents by their ID if they're in
the same index.
:param offsets: If `true`, the response includes term offsets.
:param payloads: If `true`, the response includes term payloads.
:param positions: If `true`, the response includes term positions.
:param preference: The node or shard the operation should be performed on. It
is random by default.
:param realtime: If true, the request is real-time as opposed to near-real-time.
:param routing: A custom value used to route operations to a specific shard.
:param term_statistics: If true, the response includes term frequency and document
frequency.
:param version: If `true`, returns the document version as part of a hit.
:param version_type: The version type.
"""
__path_parts: t.Dict[str, str]
if index not in SKIP_IN_PATH:
__path_parts = {"index": _quote(index)}
__path = f'/{__path_parts["index"]}/_mtermvectors'
else:
__path_parts = {}
__path = "/_mtermvectors"
__query: t.Dict[str, t.Any] = {}
__body: t.Dict[str, t.Any] = body if body is not None else {}
if error_trace is not None:
__query["error_trace"] = error_trace
if field_statistics is not None:
__query["field_statistics"] = field_statistics
if fields is not None:
__query["fields"] = fields
if filter_path is not None:
__query["filter_path"] = filter_path
if human is not None:
__query["human"] = human
if offsets is not None:
__query["offsets"] = offsets
if payloads is not None:
__query["payloads"] = payloads
if positions is not None:
__query["positions"] = positions
if preference is not None:
__query["preference"] = preference
if pretty is not None:
__query["pretty"] = pretty
if realtime is not None:
__query["realtime"] = realtime
if routing is not None:
__query["routing"] = routing
if term_statistics is not None:
__query["term_statistics"] = term_statistics
if version is not None:
__query["version"] = version
if version_type is not None:
__query["version_type"] = version_type
if not __body:
if docs is not None:
__body["docs"] = docs
if ids is not None:
__body["ids"] = ids
if not __body:
__body = None # type: ignore[assignment]
__headers = {"accept": "application/json"}
if __body is not None:
__headers["content-type"] = "application/json"
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elasticsearch_serverless/_sync/client/__init__.py [3087:3196]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
self,
*,
index: t.Optional[str] = None,
docs: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None,
error_trace: t.Optional[bool] = None,
field_statistics: t.Optional[bool] = None,
fields: t.Optional[t.Union[str, t.Sequence[str]]] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
human: t.Optional[bool] = None,
ids: t.Optional[t.Sequence[str]] = None,
offsets: t.Optional[bool] = None,
payloads: t.Optional[bool] = None,
positions: t.Optional[bool] = None,
preference: t.Optional[str] = None,
pretty: t.Optional[bool] = None,
realtime: t.Optional[bool] = None,
routing: t.Optional[str] = None,
term_statistics: t.Optional[bool] = None,
version: t.Optional[int] = None,
version_type: t.Optional[
t.Union[str, t.Literal["external", "external_gte", "force", "internal"]]
] = None,
body: t.Optional[t.Dict[str, t.Any]] = None,
) -> ObjectApiResponse[t.Any]:
"""
.. raw:: html
Get multiple term vectors.
Get multiple term vectors with a single request.
You can specify existing documents by index and ID or provide artificial documents in the body of the request.
You can specify the index in the request body or request URI.
The response contains a docs array with all the fetched termvectors.
Each element has the structure provided by the termvectors API.
Artificial documents
You can also use mtermvectors to generate term vectors for artificial documents provided in the body of the request.
The mapping used is determined by the specified _index.
``_
:param index: The name of the index that contains the documents.
:param docs: An array of existing or artificial documents.
:param field_statistics: If `true`, the response includes the document count,
sum of document frequencies, and sum of total term frequencies.
:param fields: A comma-separated list or wildcard expressions of fields to include
in the statistics. It is used as the default list unless a specific field
list is provided in the `completion_fields` or `fielddata_fields` parameters.
:param ids: A simplified syntax to specify documents by their ID if they're in
the same index.
:param offsets: If `true`, the response includes term offsets.
:param payloads: If `true`, the response includes term payloads.
:param positions: If `true`, the response includes term positions.
:param preference: The node or shard the operation should be performed on. It
is random by default.
:param realtime: If true, the request is real-time as opposed to near-real-time.
:param routing: A custom value used to route operations to a specific shard.
:param term_statistics: If true, the response includes term frequency and document
frequency.
:param version: If `true`, returns the document version as part of a hit.
:param version_type: The version type.
"""
__path_parts: t.Dict[str, str]
if index not in SKIP_IN_PATH:
__path_parts = {"index": _quote(index)}
__path = f'/{__path_parts["index"]}/_mtermvectors'
else:
__path_parts = {}
__path = "/_mtermvectors"
__query: t.Dict[str, t.Any] = {}
__body: t.Dict[str, t.Any] = body if body is not None else {}
if error_trace is not None:
__query["error_trace"] = error_trace
if field_statistics is not None:
__query["field_statistics"] = field_statistics
if fields is not None:
__query["fields"] = fields
if filter_path is not None:
__query["filter_path"] = filter_path
if human is not None:
__query["human"] = human
if offsets is not None:
__query["offsets"] = offsets
if payloads is not None:
__query["payloads"] = payloads
if positions is not None:
__query["positions"] = positions
if preference is not None:
__query["preference"] = preference
if pretty is not None:
__query["pretty"] = pretty
if realtime is not None:
__query["realtime"] = realtime
if routing is not None:
__query["routing"] = routing
if term_statistics is not None:
__query["term_statistics"] = term_statistics
if version is not None:
__query["version"] = version
if version_type is not None:
__query["version_type"] = version_type
if not __body:
if docs is not None:
__body["docs"] = docs
if ids is not None:
__body["ids"] = ids
if not __body:
__body = None # type: ignore[assignment]
__headers = {"accept": "application/json"}
if __body is not None:
__headers["content-type"] = "application/json"
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