242 lines
12 KiB
Python
242 lines
12 KiB
Python
import logging
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import re
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from collections import OrderedDict
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import pandas as pd
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import numpy
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from pandas import ExcelFile
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from pandas._libs.missing import NA
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from sqlalchemy import desc
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from sqlalchemy.sql.functions import GenericFunction
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from crc import db
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from crc.api.common import ApiError
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from crc.models.api_models import Task
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from crc.models.file import FileModel, FileDataModel, LookupFileModel, LookupDataModel
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from crc.models.ldap import LdapSchema
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from crc.models.workflow import WorkflowModel, WorkflowSpecDependencyFile
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from crc.services.file_service import FileService
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from crc.services.ldap_service import LdapService
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from crc.services.workflow_processor import WorkflowProcessor
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class TSRank(GenericFunction):
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package = 'full_text'
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name = 'ts_rank'
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class LookupService(object):
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"""Provides tools for doing lookups for auto-complete fields, and rapid access to any
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uploaded spreadsheets.
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This can currently take three forms:
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1) Lookup from spreadsheet data associated with a workflow specification.
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in which case we store the spreadsheet data in a lookup table with full
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text indexing enabled, and run searches against that table.
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2) Lookup from spreadsheet data associated with a specific file. This allows us
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to get a lookup model for a specific file object, such as a reference file.
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2) Lookup from LDAP records. In which case we call out to an external service
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to pull back detailed records and return them.
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I could imagine this growing to include other external services as tools to handle
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lookup fields. I could also imagine using some sort of local cache so we don't
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unnecessarily pound on external services for repeat searches for the same records.
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"""
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@staticmethod
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def get_lookup_model(spiff_task, field):
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workflow_id = spiff_task.workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY]
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workflow = db.session.query(WorkflowModel).filter(WorkflowModel.id == workflow_id).first()
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return LookupService.__get_lookup_model(workflow, spiff_task.task_spec.name, field.id)
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@staticmethod
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def get_lookup_model_for_file_data(file_data: FileDataModel, value_column, label_column):
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lookup_model = db.session.query(LookupFileModel).filter(LookupFileModel.file_data_model_id == file_data.id).first()
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if not lookup_model:
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logging.warning("!!!! Making a very expensive call to update the lookup model.")
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lookup_model = LookupService.build_lookup_table(file_data, value_column, label_column)
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return lookup_model
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@staticmethod
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def __get_lookup_model(workflow, task_spec_id, field_id):
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lookup_model = db.session.query(LookupFileModel) \
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.filter(LookupFileModel.workflow_spec_id == workflow.workflow_spec_id) \
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.filter(LookupFileModel.field_id == field_id) \
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.filter(LookupFileModel.task_spec_id == task_spec_id) \
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.order_by(desc(LookupFileModel.id)).first()
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# one more quick query, to see if the lookup file is still related to this workflow.
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# if not, we need to rebuild the lookup table.
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is_current = False
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if lookup_model:
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if lookup_model.is_ldap: # LDAP is always current
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is_current = True
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else:
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is_current = db.session.query(WorkflowSpecDependencyFile). \
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filter(WorkflowSpecDependencyFile.file_data_id == lookup_model.file_data_model_id).\
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filter(WorkflowSpecDependencyFile.workflow_id == workflow.id).count()
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if not is_current:
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# Very very very expensive, but we don't know need this till we do.
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logging.warning("!!!! Making a very expensive call to update the lookup models.")
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lookup_model = LookupService.create_lookup_model(workflow, task_spec_id, field_id)
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return lookup_model
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@staticmethod
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def lookup(workflow, task_spec_id, field_id, query, value=None, limit=10):
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# Returns a list of dictionaries
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lookup_model = LookupService.__get_lookup_model(workflow, task_spec_id, field_id)
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if lookup_model.is_ldap:
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return LookupService._run_ldap_query(query, value, limit)
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else:
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return LookupService._run_lookup_query(lookup_model, query, value, limit)
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@staticmethod
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def create_lookup_model(workflow_model, task_spec_id, field_id):
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"""
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This is all really expensive, but should happen just once (per file change).
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Checks to see if the options are provided in a separate lookup table associated with the workflow, and if so,
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assures that data exists in the database, and return a model than can be used to locate that data.
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Returns: an array of LookupData, suitable for returning to the API.
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"""
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processor = WorkflowProcessor(workflow_model) # VERY expensive, Ludicrous for lookup / type ahead
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spec, field = processor.find_spec_and_field(task_spec_id, field_id)
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# Clear out all existing lookup models for this workflow and field.
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existing_models = db.session.query(LookupFileModel) \
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.filter(LookupFileModel.workflow_spec_id == workflow_model.workflow_spec_id) \
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.filter(LookupFileModel.task_spec_id == task_spec_id) \
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.filter(LookupFileModel.field_id == field_id).all()
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for model in existing_models: # Do it one at a time to cause the required cascade of deletes.
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db.session.delete(model)
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# Use the contents of a file to populate enum field options
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if field.has_property(Task.FIELD_PROP_SPREADSHEET_NAME):
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if not (field.has_property(Task.FIELD_PROP_VALUE_COLUMN) or
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field.has_property(Task.FIELD_PROP_LABEL_COLUMN)):
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raise ApiError.from_task_spec("invalid_enum",
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"For enumerations based on an xls file, you must include 3 properties: %s, "
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"%s, and %s" % (Task.FIELD_PROP_SPREADSHEET_NAME,
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Task.FIELD_PROP_VALUE_COLUMN,
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Task.FIELD_PROP_LABEL_COLUMN),
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task_spec=spec)
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# Get the file data from the File Service
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file_name = field.get_property(Task.FIELD_PROP_SPREADSHEET_NAME)
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value_column = field.get_property(Task.FIELD_PROP_VALUE_COLUMN)
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label_column = field.get_property(Task.FIELD_PROP_LABEL_COLUMN)
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latest_files = FileService.get_spec_data_files(workflow_spec_id=workflow_model.workflow_spec_id,
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workflow_id=workflow_model.id,
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name=file_name)
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if len(latest_files) < 1:
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raise ApiError("invalid_enum", "Unable to locate the lookup data file '%s'" % file_name)
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else:
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data_model = latest_files[0]
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lookup_model = LookupService.build_lookup_table(data_model, value_column, label_column,
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workflow_model.workflow_spec_id, task_spec_id, field_id)
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# Use the results of an LDAP request to populate enum field options
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elif field.has_property(Task.FIELD_PROP_LDAP_LOOKUP):
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lookup_model = LookupFileModel(workflow_spec_id=workflow_model.workflow_spec_id,
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task_spec_id=task_spec_id,
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field_id=field_id,
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is_ldap=True)
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else:
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raise ApiError.from_task_spec("unknown_lookup_option",
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"Lookup supports using spreadsheet or LDAP options, "
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"and neither of those was provided.", spec)
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db.session.add(lookup_model)
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db.session.commit()
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return lookup_model
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@staticmethod
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def build_lookup_table(data_model: FileDataModel, value_column, label_column,
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workflow_spec_id=None, task_spec_id=None, field_id=None):
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""" In some cases the lookup table can be very large. This method will add all values to the database
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in a way that can be searched and returned via an api call - rather than sending the full set of
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options along with the form. It will only open the file and process the options if something has
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changed. """
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xls = ExcelFile(data_model.data, engine='openpyxl')
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df = xls.parse(xls.sheet_names[0]) # Currently we only look at the fist sheet.
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df = df.convert_dtypes()
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df = df.loc[:, ~df.columns.str.contains('^Unnamed')] # Drop unnamed columns.
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df = pd.DataFrame(df).dropna(how='all') # Drop null rows
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df = pd.DataFrame(df).replace({NA: ''})
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if value_column not in df:
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raise ApiError("invalid_enum",
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"The file %s does not contain a column named % s" % (data_model.file_model.name,
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value_column))
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if label_column not in df:
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raise ApiError("invalid_enum",
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"The file %s does not contain a column named % s" % (data_model.file_model.name,
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label_column))
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lookup_model = LookupFileModel(workflow_spec_id=workflow_spec_id,
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field_id=field_id,
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task_spec_id=task_spec_id,
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file_data_model_id=data_model.id,
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is_ldap=False)
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db.session.add(lookup_model)
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for index, row in df.iterrows():
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lookup_data = LookupDataModel(lookup_file_model=lookup_model,
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value=row[value_column],
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label=row[label_column],
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data=row.to_dict(OrderedDict))
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db.session.add(lookup_data)
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db.session.commit()
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return lookup_model
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@staticmethod
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def _run_lookup_query(lookup_file_model, query, value, limit):
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db_query = LookupDataModel.query.filter(LookupDataModel.lookup_file_model == lookup_file_model)
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if value is not None: # Then just find the model with that value
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db_query = db_query.filter(LookupDataModel.value == str(value))
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else:
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# Build a full text query that takes all the terms provided and executes each term as a prefix query, and
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# OR's those queries together. The order of the results is handled as a standard "Like" on the original
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# string which seems to work intuitively for most entries.
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query = re.sub('[^A-Za-z0-9 ]+', '', query) # Strip out non ascii characters.
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query = re.sub(r'\s+', ' ', query) # Convert multiple space like characters to just one space, as we split on spaces.
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print("Query: " + query)
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query = query.strip()
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if len(query) > 0:
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if ' ' in query:
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terms = query.split(' ')
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new_terms = []
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for t in terms:
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new_terms.append("%s:*" % t)
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new_query = ' & '.join(new_terms)
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new_query = "'%s' | %s" % (query, new_query)
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else:
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new_query = "%s:*" % query
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db_query = db_query.filter(
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LookupDataModel.__ts_vector__.match(new_query, postgresql_regconfig='simple'))
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# Hackishly order by like, which does a good job of pulling more relevant matches to the top.
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db_query = db_query.order_by(desc(LookupDataModel.label.like("%" + query + "%")))
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logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)
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logging.info(db_query)
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result = db_query.limit(limit).all()
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logging.getLogger('sqlalchemy.engine').setLevel(logging.ERROR)
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result_data = list(map(lambda lookup_item: lookup_item.data, result))
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return result_data
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@staticmethod
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def _run_ldap_query(query, value, limit):
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if value:
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return [LdapSchema().dump(LdapService.user_info(value))]
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else:
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users = LdapService.search_users(query, limit)
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return users
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