数据库课程设计基于Django框架开发的论坛系统源码.zip

# encoding: utf-8 import json import os import re import shutil import threading import warnings from django.conf import settings from django.core.exceptions import ImproperlyConfigured from django.utils.datetime_safe import date, datetime from django.utils.encoding import force_str from haystack.backends import ( BaseEngine, BaseSearchBackend, BaseSearchQuery, EmptyResults, log_query, ) from haystack.constants import ( DJANGO_CT, DJANGO_ID, FUZZY_WHOOSH_MAX_EDITS, FUZZY_WHOOSH_MIN_PREFIX, ID, ) from haystack.exceptions import MissingDependency, SearchBackendError, SkipDocument from haystack.inputs import Clean, Exact, PythonData, Raw from haystack.models import SearchResult from haystack.utils import get_identifier, get_model_ct from haystack.utils import log as logging from haystack.utils.app_loading import haystack_get_model from jieba.analyse import ChineseAnalyzer try: import whoosh except ImportError: raise MissingDependency( "The 'whoosh' backend requires the installation of 'Whoosh'. Please refer to the documentation." ) # Handle minimum requirement. if not hasattr(whoosh, "__version__") or whoosh.__version__ < (2, 5, 0): raise MissingDependency("The 'whoosh' backend requires version 2.5.0 or greater.") # Bubble up the correct error. from whoosh import index from whoosh.analysis import StemmingAnalyzer from whoosh.fields import BOOLEAN, DATETIME from whoosh.fields import ID as WHOOSH_ID from whoosh.fields import IDLIST, KEYWORD, NGRAM, NGRAMWORDS, NUMERIC, TEXT, Schema from whoosh.filedb.filestore import FileStorage, RamStorage from whoosh.highlight import ContextFragmenter, HtmlFormatter from whoosh.highlight import highlight as whoosh_highlight from whoosh.qparser import FuzzyTermPlugin, QueryParser from whoosh.searching import ResultsPage from whoosh.sorting import Count, DateRangeFacet, FieldFacet from whoosh.support.relativedelta import relativedelta as RelativeDelta from whoosh.writing import AsyncWriter DATETIME_REGEX = re.compile( r"^(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})T(?P<hour>\d{2}):(?P<minute>\d{2}):(?P<second>\d{2})(\.\d{3,6}Z?)?$" ) LOCALS = threading.local() LOCALS.RAM_STORE = None class WhooshHtmlFormatter(HtmlFormatter): """ This is a HtmlFormatter simpler than the whoosh.HtmlFormatter. We use it to have consistent results across backends. Specifically, Solr, Xapian and Elasticsearch are using this formatting. """ template = "<%(tag)s>%(t)s</%(tag)s>" class WhooshSearchBackend(BaseSearchBackend): # Word reserved by Whoosh for special use. RESERVED_WORDS = ("AND", "NOT", "OR", "TO") # Characters reserved by Whoosh for special use. # The '\\' must come first, so as not to overwrite the other slash replacements. RESERVED_CHARACTERS = ( "\\", "+", "-", "&&", "||", "!", "(", ")", "{", "}", "[", "]", "^", '"', "~", "*", "?", ":", ".", ) def __init__(self, connection_alias, **connection_options): super().__init__(connection_alias, **connection_options) self.setup_complete = False self.use_file_storage = True self.post_limit = getattr(connection_options, "POST_LIMIT", 128 * 1024 * 1024) self.path = connection_options.get("PATH") if connection_options.get("STORAGE", "file") != "file": self.use_file_storage = False if self.use_file_storage and not self.path: raise ImproperlyConfigured( "You must specify a 'PATH' in your settings for connection '%s'." % connection_alias ) self.log = logging.getLogger("haystack") def setup(self): """ Defers loading until needed. """ from haystack import connections new_index = False # Make sure the index is there. if self.use_file_storage and not os.path.exists(self.path): os.makedirs(self.path) new_index = True if self.use_file_storage and not os.access(self.path, os.W_OK): raise IOError( "The path to your Whoosh index '%s' is not writable for the current user/group." % self.path ) if self.use_file_storage: self.storage = FileStorage(self.path) else: global LOCALS if getattr(LOCALS, "RAM_STORE", None) is None: LOCALS.RAM_STORE = RamStorage() self.storage = LOCALS.RAM_STORE self.content_field_name, self.schema = self.build_schema( connections[self.connection_alias].get_unified_index().all_searchfields() ) self.parser = QueryParser(self.content_field_name, schema=self.schema) self.parser.add_plugins([FuzzyTermPlugin]) if new_index is True: self.index = self.storage.create_index(self.schema) else: try: self.index = self.storage.open_index(schema=self.schema) except index.EmptyIndexError: self.index = self.storage.create_index(self.schema) self.setup_complete = True def build_schema(self, fields): schema_fields = { ID: WHOOSH_ID(stored=True, unique=True), DJANGO_CT: WHOOSH_ID(stored=True), DJANGO_ID: WHOOSH_ID(stored=True), } # Grab the number of keys that are hard-coded into Haystack. # We'll use this to (possibly) fail slightly more gracefully later. initial_key_count = len(schema_fields) content_field_name = "" for _, field_class in fields.items(): if field_class.is_multivalued: if field_class.indexed is False: schema_fields[field_class.index_fieldname] = IDLIST( stored=True, analyzer=ChineseAnalyzer(), field_boost=field_class.boost, sortable=True ) else: schema_fields[field_class.index_fieldname] = KEYWORD( stored=True, commas=True, scorable=True, field_boost=field_class.boost, ) elif field_class.field_type in ["date", "datetime"]: schema_fields[field_class.index_fieldname] = DATETIME( stored=field_class.stored, sortable=True ) elif field_class.field_type == "integer": schema_fields[field_class.index_fieldname] = NUMERIC( stored=field_class.stored, numtype=int, field_boost=field_class.boost, ) elif field_class.field_type == "float": schema_fields[field_class.index_fieldname] = NUMERIC( stored=field_class.stored, numtype=float, field_boost=field_class.boost, ) elif field_class.field_type == "boolean": # Field boost isn't supported on BOOLEAN as of 1.8.2. schema_fields[field_class.index_fieldname] = BOOLEAN( stored=field_class.stored ) elif field_class.field_type == "ngram": schema_fields[field_class.index_fieldname] = NGRAM( minsize=3, maxsize=15, stored=field_class.stored, field_boost=field_class.boost, ) elif field_class.field_type == "edge_ngram": schema_fields[field_class.index_fieldname] = NGRAMWORDS( minsize=2, maxsize=15, at="start", stored=field_class.stored, field_boost=field_c