def read_dataset( self, dataset: str, owner: Optional[str], query: Optional[TokenClassificationQuery] = None, ) -> Iterable[TokenClassificationRecord]: """ Scan a dataset records Parameters ---------- dataset: The dataset name owner: The dataset owner. Optional query: If provided, scan will retrieve only records matching the provided query filters. Optional """ dataset = self.__datasets__.find_by_name(dataset, owner=owner) for db_record in self.__dao__.scan_dataset( dataset, search=RecordSearch(query=as_elasticsearch(query)) ): yield TokenClassificationRecord.parse_obj(db_record)
def test_too_long_metadata(): text = "On one ones o no" record = TokenClassificationRecord.parse_obj({ "text": text, "tokens": text.split(), "metadata": { "too_long": "a" * 1000 }, }) assert len(record.metadata["too_long"]) == MAX_KEYWORD_LENGTH
def search( self, dataset: str, owner: Optional[str], search: TokenClassificationQuery, record_from: int = 0, size: int = 100, ) -> TokenClassificationSearchResults: """ Run a search in a dataset Parameters ---------- dataset: The dataset name owner: The dataset owner search: The search parameters record_from: The record from return results size: The max number of records to return Returns ------- The matched records with aggregation info for specified task_meta.py """ dataset = self.__datasets__.find_by_name(dataset, owner=owner) results = self.__dao__.search_records( dataset, search=RecordSearch(query=as_elasticsearch(search)), size=size, record_from=record_from, ) return TokenClassificationSearchResults( total=results.total, records=[ TokenClassificationRecord.parse_obj(r) for r in results.records ], aggregations=TokenClassificationAggregations( **results.aggregations, words=results.words, metadata=results.metadata or {}, ) if results.aggregations else None, )
def add_records( self, dataset: str, owner: Optional[str], records: List[CreationTokenClassificationRecord], ): dataset = self.__datasets__.find_by_name(dataset, owner=owner) db_records = [] now = datetime.datetime.now() for record in records: db_record = TokenClassificationRecord.parse_obj(record) db_record.last_updated = now db_records.append(db_record.dict(exclude_none=True)) failed = self.__dao__.add_records( dataset=dataset, records=db_records, ) return BulkResponse(dataset=dataset.name, processed=len(records), failed=failed)
def search( self, dataset: str, owner: Optional[str], search: TokenClassificationQuery, record_from: int = 0, size: int = 100, ) -> TokenClassificationSearchResults: """ Run a search in a dataset Parameters ---------- dataset: The dataset name owner: The dataset owner search: The search parameters record_from: The record from return results size: The max number of records to return Returns ------- The matched records with aggregation info for specified task_meta.py """ dataset = self.__datasets__.find_by_name(dataset, owner=owner) results = self.__dao__.search_records( dataset, search=RecordSearch( query=as_elasticsearch(search), aggregations={ **aggregations.nested_aggregation( name=PREDICTED_MENTIONS_ES_FIELD_NAME, nested_path=PREDICTED_MENTIONS_ES_FIELD_NAME, inner_aggregation=aggregations.bidimentional_terms_aggregations( name=PREDICTED_MENTIONS_ES_FIELD_NAME, field_name_x=PREDICTED_MENTIONS_ES_FIELD_NAME + ".entity", field_name_y=PREDICTED_MENTIONS_ES_FIELD_NAME + ".mention", ), ), **aggregations.nested_aggregation( name=MENTIONS_ES_FIELD_NAME, nested_path=MENTIONS_ES_FIELD_NAME, inner_aggregation=aggregations.bidimentional_terms_aggregations( name=MENTIONS_ES_FIELD_NAME, field_name_x=MENTIONS_ES_FIELD_NAME + ".entity", field_name_y=MENTIONS_ES_FIELD_NAME + ".mention", ), ), }, ), size=size, record_from=record_from, ) return TokenClassificationSearchResults( total=results.total, records=[TokenClassificationRecord.parse_obj(r) for r in results.records], aggregations=TokenClassificationAggregations( **results.aggregations, words=results.words, metadata=results.metadata or {}, ) if results.aggregations else None, )