mirror of
https://github.com/sartography/cr-connect-workflow.git
synced 2025-02-23 21:28:32 +00:00
b544334f45
2. Disabling the token timeout for now, to see if this corrects the issues Alex is having with lost work. 3. Raising more thoughtful error messages for unknown lookup options. 4. Providing better validation of default values and injecting the correct value for defaults related to enum lists of all types. 5. Bumping Spiffworkflow library which contains some better error messages and checks.
220 lines
11 KiB
Python
220 lines
11 KiB
Python
import logging
|
|
import re
|
|
from collections import OrderedDict
|
|
|
|
import pandas as pd
|
|
from pandas import ExcelFile, np
|
|
from sqlalchemy import 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) \
|
|
.order_by(desc(LookupFileModel.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).\
|
|
filter(WorkflowSpecDependencyFile.workflow_id == workflow.id).count()
|
|
|
|
if not is_current:
|
|
# 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, value=None, limit=10):
|
|
|
|
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, value, 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
|
|
spec, field = processor.find_spec_and_field_by_field_id(field_id)
|
|
|
|
# Clear out all existing lookup models for this workflow and field.
|
|
existing_models = db.session.query(LookupFileModel) \
|
|
.filter(LookupFileModel.workflow_spec_id == workflow_model.workflow_spec_id) \
|
|
.filter(LookupFileModel.field_id == field_id).all()
|
|
for model in existing_models: # Do it one at a time to cause the required cascade of deletes.
|
|
db.session.delete(model)
|
|
|
|
# Use the contents of a file to populate enum field options
|
|
if field.has_property(Task.FIELD_PROP_SPREADSHEET_NAME):
|
|
if not (field.has_property(Task.FIELD_PROP_SPREADSHEET_VALUE_COLUMN) or
|
|
field.has_property(Task.FIELD_PROP_SPREADSHEET_LABEL_COLUMN)):
|
|
raise ApiError.from_task_spec("invalid_enum",
|
|
"For enumerations based on an xls file, you must include 3 properties: %s, "
|
|
"%s, and %s" % (Task.FIELD_PROP_SPREADSHEET_NAME,
|
|
Task.FIELD_PROP_SPREADSHEET_VALUE_COLUMN,
|
|
Task.FIELD_PROP_SPREADSHEET_LABEL_COLUMN),
|
|
task_spec=spec)
|
|
|
|
# Get the file data from the File Service
|
|
file_name = field.get_property(Task.FIELD_PROP_SPREADSHEET_NAME)
|
|
value_column = field.get_property(Task.FIELD_PROP_SPREADSHEET_VALUE_COLUMN)
|
|
label_column = field.get_property(Task.FIELD_PROP_SPREADSHEET_LABEL_COLUMN)
|
|
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)
|
|
|
|
# Use the results of an LDAP request to populate enum field options
|
|
elif field.has_property(Task.FIELD_PROP_LDAP_LOOKUP):
|
|
lookup_model = LookupFileModel(workflow_spec_id=workflow_model.workflow_spec_id,
|
|
field_id=field_id,
|
|
is_ldap=True)
|
|
|
|
else:
|
|
raise ApiError.from_task_spec("unknown_lookup_option",
|
|
"Lookup supports using spreadsheet or LDAP options, "
|
|
"and neither of those was provided.", spec)
|
|
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.
|
|
df = pd.DataFrame(df).replace({np.nan: None})
|
|
if value_column not in df:
|
|
raise ApiError("invalid_enum",
|
|
"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_enum",
|
|
"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_dict(OrderedDict))
|
|
db.session.add(lookup_data)
|
|
db.session.commit()
|
|
return lookup_model
|
|
|
|
@staticmethod
|
|
def _run_lookup_query(lookup_file_model, query, value, limit):
|
|
db_query = LookupDataModel.query.filter(LookupDataModel.lookup_file_model == lookup_file_model)
|
|
if value is not None: # Then just find the model with that value
|
|
db_query = db_query.filter(LookupDataModel.value == value)
|
|
else:
|
|
# Build a full text query that takes all the terms provided and executes each term as a prefix query, and
|
|
# OR's those queries together. The order of the results is handled as a standard "Like" on the original
|
|
# string which seems to work intuitively for most entries.
|
|
query = re.sub('[^A-Za-z0-9 ]+', '', query) # Strip out non ascii characters.
|
|
query = re.sub(r'\s+', ' ', query) # Convert multiple space like characters to just one space, as we split on spaces.
|
|
print("Query: " + query)
|
|
query = query.strip()
|
|
if len(query) > 0:
|
|
if ' ' in query:
|
|
terms = query.split(' ')
|
|
new_terms = []
|
|
for t in terms:
|
|
new_terms.append("%s:*" % t)
|
|
new_query = ' & '.join(new_terms)
|
|
new_query = "'%s' | %s" % (query, new_query)
|
|
else:
|
|
new_query = "%s:*" % query
|
|
|
|
db_query = db_query.filter(
|
|
LookupDataModel.__ts_vector__.match(new_query, postgresql_regconfig='simple'))
|
|
|
|
# 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 + "%")))
|
|
|
|
logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)
|
|
logging.info(db_query)
|
|
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
|
|
|