203 lines
9.6 KiB
Python
203 lines
9.6 KiB
Python
import logging
|
|
import re
|
|
|
|
from pandas import ExcelFile
|
|
from sqlalchemy import func, desc
|
|
from sqlalchemy.sql.functions import GenericFunction
|
|
|
|
from crc import db
|
|
from crc.api.common import ApiError
|
|
from crc.models.api_models import Task
|
|
from crc.models.file import FileDataModel, LookupFileModel, LookupDataModel
|
|
from crc.models.workflow import WorkflowModel, WorkflowSpecDependencyFile
|
|
from crc.services.file_service import FileService
|
|
from crc.services.ldap_service import LdapService
|
|
from crc.services.workflow_processor import WorkflowProcessor
|
|
|
|
|
|
class TSRank(GenericFunction):
|
|
package = 'full_text'
|
|
name = 'ts_rank'
|
|
|
|
class LookupService(object):
|
|
|
|
"""Provides tools for doing lookups for auto-complete fields.
|
|
This can currently take two forms:
|
|
1) Lookup from spreadsheet data associated with a workflow specification.
|
|
in which case we store the spreadsheet data in a lookup table with full
|
|
text indexing enabled, and run searches against that table.
|
|
2) Lookup from LDAP records. In which case we call out to an external service
|
|
to pull back detailed records and return them.
|
|
|
|
I could imagine this growing to include other external services as tools to handle
|
|
lookup fields. I could also imagine using some sort of local cache so we don't
|
|
unnecessarily pound on external services for repeat searches for the same records.
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_lookup_model(spiff_task, field):
|
|
workflow_id = spiff_task.workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY]
|
|
workflow = db.session.query(WorkflowModel).filter(WorkflowModel.id == workflow_id).first()
|
|
return LookupService.__get_lookup_model(workflow, field.id)
|
|
|
|
@staticmethod
|
|
def __get_lookup_model(workflow, field_id):
|
|
lookup_model = db.session.query(LookupFileModel) \
|
|
.filter(LookupFileModel.workflow_spec_id == workflow.workflow_spec_id) \
|
|
.filter(LookupFileModel.field_id == field_id).first()
|
|
|
|
# one more quick query, to see if the lookup file is still related to this workflow.
|
|
# if not, we need to rebuild the lookup table.
|
|
is_current = False
|
|
if lookup_model:
|
|
is_current = db.session.query(WorkflowSpecDependencyFile).\
|
|
filter(WorkflowSpecDependencyFile.file_data_id == lookup_model.file_data_model_id).count()
|
|
|
|
if not is_current:
|
|
if lookup_model:
|
|
db.session.delete(lookup_model)
|
|
# Very very very expensive, but we don't know need this till we do.
|
|
lookup_model = LookupService.create_lookup_model(workflow, field_id)
|
|
|
|
return lookup_model
|
|
|
|
@staticmethod
|
|
def lookup(workflow, field_id, query, limit):
|
|
|
|
lookup_model = LookupService.__get_lookup_model(workflow, field_id)
|
|
|
|
if lookup_model.is_ldap:
|
|
return LookupService._run_ldap_query(query, limit)
|
|
else:
|
|
return LookupService._run_lookup_query(lookup_model, query, limit)
|
|
|
|
|
|
|
|
@staticmethod
|
|
def create_lookup_model(workflow_model, field_id):
|
|
"""
|
|
This is all really expensive, but should happen just once (per file change).
|
|
Checks to see if the options are provided in a separate lookup table associated with the
|
|
workflow, and if so, assures that data exists in the database, and return a model than can be used
|
|
to locate that data.
|
|
Returns: an array of LookupData, suitable for returning to the api.
|
|
"""
|
|
processor = WorkflowProcessor(workflow_model) # VERY expensive, Ludicrous for lookup / type ahead
|
|
spiff_task, field = processor.find_task_and_field_by_field_id(field_id)
|
|
|
|
if field.has_property(Task.PROP_OPTIONS_FILE):
|
|
if not field.has_property(Task.PROP_OPTIONS_VALUE_COLUMN) or \
|
|
not field.has_property(Task.PROP_OPTIONS_LABEL_COL):
|
|
raise ApiError.from_task("invalid_emum",
|
|
"For enumerations based on an xls file, you must include 3 properties: %s, "
|
|
"%s, and %s" % (Task.PROP_OPTIONS_FILE,
|
|
Task.PROP_OPTIONS_VALUE_COLUMN,
|
|
Task.PROP_OPTIONS_LABEL_COL),
|
|
task=spiff_task)
|
|
|
|
# Get the file data from the File Service
|
|
file_name = field.get_property(Task.PROP_OPTIONS_FILE)
|
|
value_column = field.get_property(Task.PROP_OPTIONS_VALUE_COLUMN)
|
|
label_column = field.get_property(Task.PROP_OPTIONS_LABEL_COL)
|
|
latest_files = FileService.get_spec_data_files(workflow_spec_id=workflow_model.workflow_spec_id,
|
|
workflow_id=workflow_model.id,
|
|
name=file_name)
|
|
if len(latest_files) < 1:
|
|
raise ApiError("invalid_enum", "Unable to locate the lookup data file '%s'" % file_name)
|
|
else:
|
|
data_model = latest_files[0]
|
|
|
|
lookup_model = LookupService.build_lookup_table(data_model, value_column, label_column,
|
|
workflow_model.workflow_spec_id, field_id)
|
|
|
|
elif field.has_property(Task.PROP_LDAP_LOOKUP):
|
|
lookup_model = LookupFileModel(workflow_spec_id=workflow_model.workflow_spec_id,
|
|
field_id=field_id,
|
|
is_ldap=True)
|
|
else:
|
|
raise ApiError("unknown_lookup_option",
|
|
"Lookup supports using spreadsheet options or ldap options, and neither "
|
|
"was provided.")
|
|
db.session.add(lookup_model)
|
|
db.session.commit()
|
|
return lookup_model
|
|
|
|
@staticmethod
|
|
def build_lookup_table(data_model: FileDataModel, value_column, label_column, workflow_spec_id, field_id):
|
|
""" In some cases the lookup table can be very large. This method will add all values to the database
|
|
in a way that can be searched and returned via an api call - rather than sending the full set of
|
|
options along with the form. It will only open the file and process the options if something has
|
|
changed. """
|
|
xls = ExcelFile(data_model.data)
|
|
df = xls.parse(xls.sheet_names[0]) # Currently we only look at the fist sheet.
|
|
if value_column not in df:
|
|
raise ApiError("invalid_emum",
|
|
"The file %s does not contain a column named % s" % (data_model.file_model.name,
|
|
value_column))
|
|
if label_column not in df:
|
|
raise ApiError("invalid_emum",
|
|
"The file %s does not contain a column named % s" % (data_model.file_model.name,
|
|
label_column))
|
|
|
|
lookup_model = LookupFileModel(workflow_spec_id=workflow_spec_id,
|
|
field_id=field_id,
|
|
file_data_model_id=data_model.id,
|
|
is_ldap=False)
|
|
|
|
db.session.add(lookup_model)
|
|
for index, row in df.iterrows():
|
|
lookup_data = LookupDataModel(lookup_file_model=lookup_model,
|
|
value=row[value_column],
|
|
label=row[label_column],
|
|
data=row.to_json())
|
|
db.session.add(lookup_data)
|
|
db.session.commit()
|
|
return lookup_model
|
|
|
|
@staticmethod
|
|
def _run_lookup_query(lookup_file_model, query, limit):
|
|
db_query = LookupDataModel.query.filter(LookupDataModel.lookup_file_model == lookup_file_model)
|
|
|
|
query = re.sub('[^A-Za-z0-9 ]+', '', query)
|
|
print("Query: " + query)
|
|
query = query.strip()
|
|
if len(query) > 0:
|
|
if ' ' in query:
|
|
terms = query.split(' ')
|
|
new_terms = ["'%s'" % query]
|
|
for t in terms:
|
|
new_terms.append("%s:*" % t)
|
|
new_query = ' | '.join(new_terms)
|
|
else:
|
|
new_query = "%s:*" % query
|
|
|
|
# Run the full text query
|
|
db_query = db_query.filter(LookupDataModel.label.match(new_query))
|
|
# But hackishly order by like, which does a good job of
|
|
# pulling more relevant matches to the top.
|
|
db_query = db_query.order_by(desc(LookupDataModel.label.like("%" + query + "%")))
|
|
#ORDER BY name LIKE concat('%', ticker, '%') desc, rank DESC
|
|
|
|
# db_query = db_query.order_by(desc(func.full_text.ts_rank(
|
|
# func.to_tsvector(LookupDataModel.label),
|
|
# func.to_tsquery(query))))
|
|
from sqlalchemy.dialects import postgresql
|
|
logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)
|
|
result = db_query.limit(limit).all()
|
|
logging.getLogger('sqlalchemy.engine').setLevel(logging.ERROR)
|
|
return result
|
|
|
|
@staticmethod
|
|
def _run_ldap_query(query, limit):
|
|
users = LdapService().search_users(query, limit)
|
|
|
|
"""Converts the user models into something akin to the
|
|
LookupModel in models/file.py, so this can be returned in the same way
|
|
we return a lookup data model."""
|
|
user_list = []
|
|
for user in users:
|
|
user_list.append( {"value": user['uid'],
|
|
"label": user['display_name'] + " (" + user['uid'] + ")",
|
|
"data": user
|
|
})
|
|
return user_list |