# Copyright 2007 Matt Chaput. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY MATT CHAPUT ``AS IS'' AND ANY EXPRESS OR # IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO # EVENT SHALL MATT CHAPUT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, # OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, # EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # The views and conclusions contained in the software and documentation are # those of the authors and should not be interpreted as representing official # policies, either expressed or implied, of Matt Chaput. """ Contains functions and classes related to fields. """ import datetime, fnmatch, re, struct, sys from array import array from decimal import Decimal from whoosh import analysis, columns, formats from whoosh.compat import with_metaclass from whoosh.compat import itervalues, xrange from whoosh.compat import bytes_type, string_type, text_type from whoosh.system import emptybytes from whoosh.system import pack_byte, unpack_byte from whoosh.util.numeric import to_sortable, from_sortable from whoosh.util.numeric import typecode_max, NaN from whoosh.util.text import utf8encode, utf8decode from whoosh.util.times import datetime_to_long, long_to_datetime # Exceptions class FieldConfigurationError(Exception): pass class UnknownFieldError(Exception): pass # Field Types class FieldType(object): """ Represents a field configuration. The FieldType object supports the following attributes: * format (formats.Format): the storage format for posting blocks. * analyzer (analysis.Analyzer): the analyzer to use to turn text into terms. * scorable (boolean): whether searches against this field may be scored. This controls whether the index stores per-document field lengths for this field. * stored (boolean): whether the content of this field is stored for each document. For example, in addition to indexing the title of a document, you usually want to store the title so it can be presented as part of the search results. * unique (boolean): whether this field's value is unique to each document. For example, 'path' or 'ID'. IndexWriter.update_document() will use fields marked as 'unique' to find the previous version of a document being updated. * multitoken_query is a string indicating what kind of query to use when a "word" in a user query parses into multiple tokens. The string is interpreted by the query parser. The strings understood by the default query parser are "first" (use first token only), "and" (join the tokens with an AND query), "or" (join the tokens with OR), "phrase" (join the tokens with a phrase query), and "default" (use the query parser's default join type). * vector (formats.Format or boolean): the format to use to store term vectors. If not a ``Format`` object, any true value means to use the index format as the term vector format. Any flase value means don't store term vectors for this field. The constructor for the base field type simply lets you supply your own attribute values. Subclasses may configure some or all of this for you. """ analyzer = format = scorable = stored = unique = vector = None indexed = True multitoken_query = "default" sortable_typecode = None column_type = None def __init__(self, format, analyzer, scorable=False, stored=False, unique=False, multitoken_query="default", sortable=False, vector=None): self.format = format self.analyzer = analyzer self.scorable = scorable self.stored = stored self.unique = unique self.multitoken_query = multitoken_query self.set_sortable(sortable) if isinstance(vector, formats.Format): self.vector = vector elif vector: self.vector = self.format else: self.vector = None def __repr__(self): return ("%s(format=%r, scorable=%s, stored=%s, unique=%s)" % (self.__class__.__name__, self.format, self.scorable, self.stored, self.unique)) def __eq__(self, other): return all((isinstance(other, FieldType), (self.format == other.format), (self.scorable == other.scorable), (self.stored == other.stored), (self.unique == other.unique), (self.column_type == other.column_type))) def __ne__(self, other): return not(self.__eq__(other)) # Text def index(self, value, **kwargs): """Returns an iterator of (btext, frequency, weight, encoded_value) tuples for each unique word in the input value. The default implementation uses the ``analyzer`` attribute to tokenize the value into strings, then encodes them into bytes using UTF-8. """ if not self.format: raise Exception("%s field %r cannot index without a format" % (self.__class__.__name__, self)) if not isinstance(value, (text_type, list, tuple)): raise ValueError("%r is not unicode or sequence" % value) assert isinstance(self.format, formats.Format) if "mode" not in kwargs: kwargs["mode"] = "index" word_values = self.format.word_values ana = self.analyzer for tstring, freq, wt, vbytes in word_values(value, ana, **kwargs): yield (utf8encode(tstring)[0], freq, wt, vbytes) def tokenize(self, value, **kwargs): """ Analyzes the given string and returns an iterator of Token objects (note: for performance reasons, actually the same token yielded over and over with different attributes). """ if not self.analyzer: raise Exception("%s field has no analyzer" % self.__class__) return self.analyzer(value, **kwargs) def process_text(self, qstring, mode='', **kwargs): """ Analyzes the given string and returns an iterator of token texts. >>> field = fields.TEXT() >>> list(field.process_text("The ides of March")) ["ides", "march"] """ if not self.format: raise Exception("%s field has no format" % self) return (t.text for t in self.tokenize(qstring, mode=mode, **kwargs)) # Conversion def to_bytes(self, value): """ Returns a bytes representation of the given value, appropriate to be written to disk. The default implementation assumes a unicode value and encodes it using UTF-8. """ if isinstance(value, (list, tuple)): value = value[0] if not isinstance(value, bytes_type): value = utf8encode(value)[0] return value def to_column_value(self, value): """ Returns an object suitable to be inserted into the document values column for this field. The default implementation simply calls ``self.to_bytes(value)``. """ return self.to_bytes(value) def from_bytes(self, bs): return utf8decode(bs)[0] def from_column_value(self, value): return self.from_bytes(value) # Columns/sorting def set_sortable(self, sortable): if sortable: if isinstance(sortable, columns.Column): self.column_type = sortable else: self.column_type = self.default_column() else: self.column_type = None def sortable_terms(self, ixreader, fieldname): """ Returns an iterator of the "sortable" tokens in the given reader and field. These values can be used for sorting. The default implementation simply returns all tokens in the field. This can be overridden by field types such as NUMERIC where some values in a field are not useful for sorting. """ return ixreader.lexicon(fieldname) def default_column(self): return columns.VarBytesColumn() # Parsing def self_parsing(self): """ Subclasses should override this method to return True if they want the query parser to call the field's ``parse_query()`` method instead of running the analyzer on text in this field. This is useful where the field needs full control over how queries are interpreted, such as in the numeric field type. """ return False def parse_query(self, fieldname, qstring, boost=1.0): """ When ``self_parsing()`` returns True, the query parser will call this method to parse basic query text. """ raise NotImplementedError(self.__class__.__name__) def parse_range(self, fieldname, start, end, startexcl, endexcl, boost=1.0): """ When ``self_parsing()`` returns True, the query parser will call this method to parse range query text. If this method returns None instead of a query object, the parser will fall back to parsing the start and end terms using process_text(). """ return None # Spelling def separate_spelling(self): """ Returns True if the field stores unstemmed words in a separate field for spelling suggestions. """ return False def spelling_fieldname(self, fieldname): """ Returns the name of a field to use for spelling suggestions instead of this field. :param fieldname: the name of this field. """ return fieldname def spellable_words(self, value): """Returns an iterator of each unique word (in sorted order) in the input value, suitable for inclusion in the field's word graph. The default behavior is to call the field analyzer with the keyword argument ``no_morph=True``, which should make the analyzer skip any morphological transformation filters (e.g. stemming) to preserve the original form of the words. Exotic field types may need to override this behavior. """ if isinstance(value, (list, tuple)): words = value else: words = [token.text for token in self.analyzer(value, no_morph=True)] return iter(sorted(set(words))) # Utility def subfields(self): """ Returns an iterator of ``(name_prefix, fieldobject)`` pairs for the fields that need to be indexed when content is put in this field. The default implementation simply yields ``("", self)``. """ yield "", self def supports(self, name): """ Returns True if the underlying format supports the given posting value type. >>> field = TEXT() >>> field.supports("positions") True >>> field.supports("chars") False """ return self.format.supports(name) def clean(self): """ Clears any cached information in the field and any child objects. """ if self.format and hasattr(self.format, "clean"): self.format.clean() # Events def on_add(self, schema, fieldname): pass def on_remove(self, schema, fieldname): pass # Wrapper base class class FieldWrapper(FieldType): def __init__(self, subfield, prefix): if isinstance(subfield, type): subfield = subfield() self.subfield = subfield self.name_prefix = prefix # By default we'll copy all the subfield's attributes -- override these # in subclass constructor for things you want to change self.analyzer = subfield.analyzer self.format = subfield.format self.column_type = subfield.column_type self.scorable = subfield.scorable self.stored = subfield.stored self.unique = subfield.unique self.indexed = subfield.indexed self.vector = subfield.vector def __eq__(self, other): return self.subfield.__eq__(other) def __ne__(self, other): return self.subfield.__ne__(other) # Text # def index(self, value, boost=1.0, **kwargs): # return self.subfield.index(value, boost, **kwargs) # # def tokenize(self, value, **kwargs): # return self.subfield.tokenize(value, **kwargs) # # def process_text(self, qstring, mode='', **kwargs): # return self.subfield.process_text(qstring, mode, **kwargs) # Conversion def to_bytes(self, value): return self.subfield.to_bytes(value) def to_column_value(self, value): return self.subfield.to_column_value(value) def from_bytes(self, bs): return self.subfield.from_bytes(bs) def from_column_value(self, value): return self.subfield.from_column_value(value) # Sorting/columns def set_sortable(self, sortable): self.subfield.set_sortable(sortable) def sortable_terms(self, ixreader, fieldname): return self.subfield.sortable_terms(ixreader, fieldname) def default_column(self): return self.subfield.default_column() # Parsing def self_parsing(self): return self.subfield.self_parsing() def parse_query(self, fieldname, qstring, boost=1.0): return self.subfield.parse_query(fieldname, qstring, boost) def parse_range(self, fieldname, start, end, startexcl, endexcl, boost=1.0): self.subfield.parse_range(fieldname, start, end, startexcl, endexcl, boost) # Utility def subfields(self): # The default FieldWrapper.subfields() implementation DOES NOT split # out the subfield here -- you need to override if that's what you want yield "", self def supports(self, name): return self.subfield.supports(name) def clean(self): self.subfield.clean() # Events def on_add(self, schema, fieldname): self.subfield.on_add(schema, fieldname) def on_remove(self, schema, fieldname): self.subfield.on_remove(schema, fieldname) # Pre-configured field types class ID(FieldType): """ Configured field type that indexes the entire value of the field as one token. This is useful for data you don't want to tokenize, such as the path of a file. """ def __init__(self, stored=False, unique=False, field_boost=1.0, sortable=False, analyzer=None): """ :param stored: Whether the value of this field is stored with the document. """ self.analyzer = analyzer or analysis.IDAnalyzer() # Don't store any information other than the doc ID self.format = formats.Existence(field_boost=field_boost) self.stored = stored self.unique = unique self.set_sortable(sortable) class IDLIST(FieldType): """ Configured field type for fields containing IDs separated by whitespace and/or punctuation (or anything else, using the expression param). """ def __init__(self, stored=False, unique=False, expression=None, field_boost=1.0): """ :param stored: Whether the value of this field is stored with the document. :param unique: Whether the value of this field is unique per-document. :param expression: The regular expression object to use to extract tokens. The default expression breaks tokens on CRs, LFs, tabs, spaces, commas, and semicolons. """ expression = expression or re.compile(r"[^\r\n\t ,;]+") self.analyzer = analysis.RegexAnalyzer(expression=expression) # Don't store any information other than the doc ID self.format = formats.Existence(field_boost=field_boost) self.stored = stored self.unique = unique class NUMERIC(FieldType): """ Special field type that lets you index integer or floating point numbers in relatively short fixed-width terms. The field converts numbers to sortable bytes for you before indexing. You specify the numeric type of the field (``int`` or ``float``) when you create the ``NUMERIC`` object. The default is ``int``. For ``int``, you can specify a size in bits (``32`` or ``64``). For both ``int`` and ``float`` you can specify a ``signed`` keyword argument (default is ``True``). >>> schema = Schema(path=STORED, position=NUMERIC(int, 64, signed=False)) >>> ix = storage.create_index(schema) >>> with ix.writer() as w: ... w.add_document(path="/a", position=5820402204) ... You can also use the NUMERIC field to store Decimal instances by specifying a type of ``int`` or ``long`` and the ``decimal_places`` keyword argument. This simply multiplies each number by ``(10 ** decimal_places)`` before storing it as an integer. Of course this may throw away decimal prcesision (by truncating, not rounding) and imposes the same maximum value limits as ``int``/``long``, but these may be acceptable for certain applications. >>> from decimal import Decimal >>> schema = Schema(path=STORED, position=NUMERIC(int, decimal_places=4)) >>> ix = storage.create_index(schema) >>> with ix.writer() as w: ... w.add_document(path="/a", position=Decimal("123.45") ... """ def __init__(self, numtype=int, bits=32, stored=False, unique=False, field_boost=1.0, decimal_places=0, shift_step=4, signed=True, sortable=False, default=None): """ :param numtype: the type of numbers that can be stored in this field, either ``int``, ``float``. If you use ``Decimal``, use the ``decimal_places`` argument to control how many decimal places the field will store. :param bits: When ``numtype`` is ``int``, the number of bits to use to store the number: 8, 16, 32, or 64. :param stored: Whether the value of this field is stored with the document. :param unique: Whether the value of this field is unique per-document. :param decimal_places: specifies the number of decimal places to save when storing Decimal instances. If you set this, you will always get Decimal instances back from the field. :param shift_steps: The number of bits of precision to shift away at each tiered indexing level. Values should generally be 1-8. Lower values yield faster searches but take up more space. A value of `0` means no tiered indexing. :param signed: Whether the numbers stored in this field may be negative. """ # Allow users to specify strings instead of Python types in case # docstring isn't clear if numtype == "int": numtype = int if numtype == "float": numtype = float # Raise an error if the user tries to use a type other than int or # float if numtype is Decimal: numtype = int if not decimal_places: raise TypeError("To store Decimal instances, you must set the " "decimal_places argument") elif numtype not in (int, float): raise TypeError("Can't use %r as a type, use int or float" % numtype) # Sanity check if numtype is float and decimal_places: raise Exception("A float type and decimal_places argument %r are " "incompatible" % decimal_places) intsizes = [8, 16, 32, 64] intcodes = ["B", "H", "I", "Q"] # Set up field configuration based on type and size if numtype is float: bits = 64 # Floats are converted to 64 bit ints else: if bits not in intsizes: raise Exception("Invalid bits %r, use 8, 16, 32, or 64" % bits) # Type code for the *sortable* representation self.sortable_typecode = intcodes[intsizes.index(bits)] self._struct = struct.Struct(">" + str(self.sortable_typecode)) self.numtype = numtype self.bits = bits self.stored = stored self.unique = unique self.decimal_places = decimal_places self.shift_step = shift_step self.signed = signed self.analyzer = analysis.IDAnalyzer() # Don't store any information other than the doc ID self.format = formats.Existence(field_boost=field_boost) self.min_value, self.max_value = self._min_max() # Column configuration if default is None: if numtype is int: default = typecode_max[self.sortable_typecode] else: default = NaN elif not self.is_valid(default): raise Exception("The default %r is not a valid number for this " "field" % default) self.default = default self.set_sortable(sortable) def __getstate__(self): d = self.__dict__.copy() if "_struct" in d: del d["_struct"] return d def __setstate__(self, d): self.__dict__.update(d) self._struct = struct.Struct(">" + str(self.sortable_typecode)) if "min_value" not in d: d["min_value"], d["max_value"] = self._min_max() def _min_max(self): numtype = self.numtype bits = self.bits signed = self.signed # Calculate the minimum and maximum possible values for error checking min_value = from_sortable(numtype, bits, signed, 0) max_value = from_sortable(numtype, bits, signed, 2 ** bits - 1) return min_value, max_value def default_column(self): return columns.NumericColumn(self.sortable_typecode, default=self.default) def is_valid(self, x): try: x = self.to_bytes(x) except ValueError: return False except OverflowError: return False return True def index(self, num, **kwargs): # If the user gave us a list of numbers, recurse on the list if isinstance(num, (list, tuple)): for n in num: for item in self.index(n): yield item return # word, freq, weight, valuestring if self.shift_step: for shift in xrange(0, self.bits, self.shift_step): yield (self.to_bytes(num, shift), 1, 1.0, emptybytes) else: yield (self.to_bytes(num), 1, 1.0, emptybytes) def prepare_number(self, x): if x == emptybytes or x is None: return x dc = self.decimal_places if dc and isinstance(x, (string_type, Decimal)): x = Decimal(x) * (10 ** dc) elif isinstance(x, Decimal): raise TypeError("Can't index a Decimal object unless you specified " "decimal_places on the field") try: x = self.numtype(x) except OverflowError: raise ValueError("Value %r overflowed number type %r" % (x, self.numtype)) if x < self.min_value or x > self.max_value: raise ValueError("Numeric field value %s out of range [%s, %s]" % (x, self.min_value, self.max_value)) return x def unprepare_number(self, x): dc = self.decimal_places if dc: s = str(x) x = Decimal(s[:-dc] + "." + s[-dc:]) return x def to_column_value(self, x): if isinstance(x, (list, tuple, array)): x = x[0] x = self.prepare_number(x) return to_sortable(self.numtype, self.bits, self.signed, x) def from_column_value(self, x): x = from_sortable(self.numtype, self.bits, self.signed, x) return self.unprepare_number(x) def to_bytes(self, x, shift=0): # Try to avoid re-encoding; this sucks because on Python 2 we can't # tell the difference between a string and encoded bytes, so we have # to require the user use unicode when they mean string if isinstance(x, bytes_type): return x if x == emptybytes or x is None: return self.sortable_to_bytes(0) x = self.prepare_number(x) x = to_sortable(self.numtype, self.bits, self.signed, x) return self.sortable_to_bytes(x, shift) def sortable_to_bytes(self, x, shift=0): if shift: x >>= shift return pack_byte(shift) + self._struct.pack(x) def from_bytes(self, bs): x = self._struct.unpack(bs[1:])[0] x = from_sortable(self.numtype, self.bits, self.signed, x) x = self.unprepare_number(x) return x def process_text(self, text, **kwargs): return (self.to_bytes(text),) def self_parsing(self): return True def parse_query(self, fieldname, qstring, boost=1.0): from whoosh import query from whoosh.qparser.common import QueryParserError if qstring == "*": return query.Every(fieldname, boost=boost) if not self.is_valid(qstring): raise QueryParserError("%r is not a valid number" % qstring) token = self.to_bytes(qstring) return query.Term(fieldname, token, boost=boost) def parse_range(self, fieldname, start, end, startexcl, endexcl, boost=1.0): from whoosh import query from whoosh.qparser.common import QueryParserError if start is not None: if not self.is_valid(start): raise QueryParserError("Range start %r is not a valid number" % start) start = self.prepare_number(start) if end is not None: if not self.is_valid(end): raise QueryParserError("Range end %r is not a valid number" % end) end = self.prepare_number(end) return query.NumericRange(fieldname, start, end, startexcl, endexcl, boost=boost) def sortable_terms(self, ixreader, fieldname): zero = b"\x00" for token in ixreader.lexicon(fieldname): if token[0:1] != zero: # Only yield the full-precision values break yield token class DATETIME(NUMERIC): """ Special field type that lets you index datetime objects. The field converts the datetime objects to sortable text for you before indexing. Since this field is based on Python's datetime module it shares all the limitations of that module, such as the inability to represent dates before year 1 in the proleptic Gregorian calendar. However, since this field stores datetimes as an integer number of microseconds, it could easily represent a much wider range of dates if the Python datetime implementation ever supports them. >>> schema = Schema(path=STORED, date=DATETIME) >>> ix = storage.create_index(schema) >>> w = ix.writer() >>> w.add_document(path="/a", date=datetime.now()) >>> w.commit() """ def __init__(self, stored=False, unique=False, sortable=False): """ :param stored: Whether the value of this field is stored with the document. :param unique: Whether the value of this field is unique per-document. """ super(DATETIME, self).__init__(int, 64, stored=stored, unique=unique, shift_step=8, sortable=sortable) def prepare_datetime(self, x): from whoosh.util.times import floor if isinstance(x, text_type): # For indexing, support same strings as for query parsing -- # convert unicode to datetime object x = self._parse_datestring(x) x = floor(x) # this makes most sense (unspecified = lowest) if isinstance(x, datetime.datetime): return datetime_to_long(x) elif isinstance(x, bytes_type): return x else: raise Exception("%r is not a datetime" % (x,)) def to_column_value(self, x): if isinstance(x, bytes_type): raise Exception("%r is not a datetime" % (x,)) if isinstance(x, (list, tuple)): x = x[0] return self.prepare_datetime(x) def from_column_value(self, x): return long_to_datetime(x) def to_bytes(self, x, shift=0): x = self.prepare_datetime(x) return NUMERIC.to_bytes(self, x, shift=shift) def from_bytes(self, bs): x = NUMERIC.from_bytes(self, bs) return long_to_datetime(x) def _parse_datestring(self, qstring): # This method parses a very simple datetime representation of the form # YYYY[MM[DD[hh[mm[ss[uuuuuu]]]]]] from whoosh.util.times import adatetime, fix, is_void qstring = qstring.replace(" ", "").replace("-", "").replace(".", "") year = month = day = hour = minute = second = microsecond = None if len(qstring) >= 4: year = int(qstring[:4]) if len(qstring) >= 6: month = int(qstring[4:6]) if len(qstring) >= 8: day = int(qstring[6:8]) if len(qstring) >= 10: hour = int(qstring[8:10]) if len(qstring) >= 12: minute = int(qstring[10:12]) if len(qstring) >= 14: second = int(qstring[12:14]) if len(qstring) == 20: microsecond = int(qstring[14:]) at = fix(adatetime(year, month, day, hour, minute, second, microsecond)) if is_void(at): raise Exception("%r is not a parseable date" % qstring) return at def parse_query(self, fieldname, qstring, boost=1.0): from whoosh import query from whoosh.util.times import is_ambiguous try: at = self._parse_datestring(qstring) except: e = sys.exc_info()[1] return query.error_query(e) if is_ambiguous(at): startnum = datetime_to_long(at.floor()) endnum = datetime_to_long(at.ceil()) return query.NumericRange(fieldname, startnum, endnum) else: return query.Term(fieldname, at, boost=boost) def parse_range(self, fieldname, start, end, startexcl, endexcl, boost=1.0): from whoosh import query if start is None and end is None: return query.Every(fieldname, boost=boost) if start is not None: startdt = self._parse_datestring(start).floor() start = datetime_to_long(startdt) if end is not None: enddt = self._parse_datestring(end).ceil() end = datetime_to_long(enddt) return query.NumericRange(fieldname, start, end, boost=boost) class BOOLEAN(FieldType): """ Special field type that lets you index boolean values (True and False). The field converts the boolean values to text for you before indexing. >>> schema = Schema(path=STORED, done=BOOLEAN) >>> ix = storage.create_index(schema) >>> w = ix.writer() >>> w.add_document(path="/a", done=False) >>> w.commit() """ bytestrings = (b"f", b"t") trues = frozenset(u"t true yes 1".split()) falses = frozenset(u"f false no 0".split()) def __init__(self, stored=False, field_boost=1.0): """ :param stored: Whether the value of this field is stored with the document. """ self.stored = stored # Don't store any information other than the doc ID self.format = formats.Existence(field_boost=field_boost) def _obj_to_bool(self, x): # We special case strings such as "true", "false", "yes", "no", but # otherwise call bool() on the query value. This lets you pass objects # as query values and do the right thing. if isinstance(x, string_type) and x.lower() in self.trues: x = True elif isinstance(x, string_type) and x.lower() in self.falses: x = False else: x = bool(x) return x def to_bytes(self, x): if isinstance(x, bytes_type): return x elif isinstance(x, string_type): x = x.lower() in self.trues else: x = bool(x) bs = self.bytestrings[int(x)] return bs def index(self, bit, **kwargs): if isinstance(bit, string_type): bit = bit.lower() in self.trues else: bit = bool(bit) # word, freq, weight, valuestring return [(self.bytestrings[int(bit)], 1, 1.0, emptybytes)] def self_parsing(self): return True def parse_query(self, fieldname, qstring, boost=1.0): from whoosh import query if qstring == "*": return query.Every(fieldname, boost=boost) return query.Term(fieldname, self._obj_to_bool(qstring), boost=boost) class STORED(FieldType): """ Configured field type for fields you want to store but not index. """ indexed = False stored = True def __init__(self): pass class COLUMN(FieldType): """ Configured field type for fields you want to store as a per-document value column but not index. """ indexed = False stored = False def __init__(self, columnobj=None): if columnobj is None: columnobj = columns.VarBytesColumn() if not isinstance(columnobj, columns.Column): raise TypeError("%r is not a column object" % (columnobj,)) self.column_type = columnobj def to_bytes(self, v): return v def from_bytes(self, b): return b class KEYWORD(FieldType): """ Configured field type for fields containing space-separated or comma-separated keyword-like data (such as tags). The default is to not store positional information (so phrase searching is not allowed in this field) and to not make the field scorable. """ def __init__(self, stored=False, lowercase=False, commas=False, scorable=False, unique=False, field_boost=1.0, sortable=False, vector=None, analyzer=None): """ :param stored: Whether to store the value of the field with the document. :param commas: Whether this is a comma-separated field. If this is False (the default), it is treated as a space-separated field. :param scorable: Whether this field is scorable. """ if not analyzer: analyzer = analysis.KeywordAnalyzer(lowercase=lowercase, commas=commas) self.analyzer = analyzer # Store field lengths and weights along with doc ID self.format = formats.Frequency(field_boost=field_boost) self.scorable = scorable self.stored = stored self.unique = unique if isinstance(vector, formats.Format): self.vector = vector elif vector: self.vector = self.format else: self.vector = None if sortable: self.column_type = self.default_column() class TEXT(FieldType): """ Configured field type for text fields (for example, the body text of an article). The default is to store positional information to allow phrase searching. This field type is always scorable. """ def __init__(self, analyzer=None, phrase=True, chars=False, stored=False, field_boost=1.0, multitoken_query="default", spelling=False, sortable=False, lang=None, vector=None, spelling_prefix="spell_"): """ :param analyzer: The analysis.Analyzer to use to index the field contents. See the analysis module for more information. If you omit this argument, the field uses analysis.StandardAnalyzer. :param phrase: Whether the store positional information to allow phrase searching. :param chars: Whether to store character ranges along with positions. If this is True, "phrase" is also implied. :param stored: Whether to store the value of this field with the document. Since this field type generally contains a lot of text, you should avoid storing it with the document unless you need to, for example to allow fast excerpts in the search results. :param spelling: if True, and if the field's analyzer changes the form of term text (such as a stemming analyzer), this field will store extra information in a separate field (named using the ``spelling_prefix`` keyword argument) to allow spelling suggestions to use the unchanged word forms as spelling suggestions. :param sortable: If True, make this field sortable using the default column type. If you pass a :class:`whoosh.columns.Column` instance instead of True, the field will use the given column type. :param lang: automaticaly configure a :class:`whoosh.analysis.LanguageAnalyzer` for the given language. This is ignored if you also specify an ``analyzer``. :param vector: if this value evaluates to true, store a list of the terms in this field in each document. If the value is an instance of :class:`whoosh.formats.Format`, the index will use the object to store the term vector. Any other true value (e.g. ``vector=True``) will use the field's index format to store the term vector as well. """ if analyzer: self.analyzer = analyzer elif lang: self.analyzer = analysis.LanguageAnalyzer(lang) else: self.analyzer = analysis.StandardAnalyzer() if chars: formatclass = formats.Characters elif phrase: formatclass = formats.Positions else: formatclass = formats.Frequency self.format = formatclass(field_boost=field_boost) if sortable: if isinstance(sortable, columns.Column): self.column_type = sortable else: self.column_type = columns.VarBytesColumn() else: self.column_type = None self.spelling = spelling self.spelling_prefix = spelling_prefix self.multitoken_query = multitoken_query self.scorable = True self.stored = stored if isinstance(vector, formats.Format): self.vector = vector elif vector: self.vector = self.format else: self.vector = None def subfields(self): yield "", self # If the user indicated this is a spellable field, and the analyzer # is morphic, then also index into a spelling-only field that stores # minimal information if self.separate_spelling(): yield self.spelling_prefix, SpellField(self.analyzer) def separate_spelling(self): return self.spelling and self.analyzer.has_morph() def spelling_fieldname(self, fieldname): if self.separate_spelling(): return self.spelling_prefix + fieldname else: return fieldname class SpellField(FieldType): """ This is a utility field type meant to be returned by ``TEXT.subfields()`` when it needs a minimal field to store the spellable words. """ def __init__(self, analyzer): self.format = formats.Frequency() self.analyzer = analyzer self.column_type = None self.scorabe = False self.stored = False self.unique = False self.indexed = True self.spelling = False # All the text analysis methods add "nomorph" to the keywords to get # unmorphed term texts def index(self, value, boost=1.0, **kwargs): kwargs["nomorph"] = True return FieldType.index(self, value, boost=boost, **kwargs) def tokenzie(self, value, **kwargs): kwargs["nomorph"] = True return FieldType.tokenize(self, value, **kwargs) def process_text(self, qstring, mode='', **kwargs): kwargs["nomorph"] = True return FieldType.process_text(self, qstring, mode=mode, **kwargs) class NGRAM(FieldType): """ Configured field that indexes text as N-grams. For example, with a field type NGRAM(3,4), the value "hello" will be indexed as tokens "hel", "hell", "ell", "ello", "llo". This field type chops the entire text into N-grams, including whitespace and punctuation. See :class:`NGRAMWORDS` for a field type that breaks the text into words first before chopping the words into N-grams. """ scorable = True def __init__(self, minsize=2, maxsize=4, stored=False, field_boost=1.0, queryor=False, phrase=False, sortable=False): """ :param minsize: The minimum length of the N-grams. :param maxsize: The maximum length of the N-grams. :param stored: Whether to store the value of this field with the document. Since this field type generally contains a lot of text, you should avoid storing it with the document unless you need to, for example to allow fast excerpts in the search results. :param queryor: if True, combine the N-grams with an Or query. The default is to combine N-grams with an And query. :param phrase: store positions on the N-grams to allow exact phrase searching. The default is off. """ formatclass = formats.Frequency if phrase: formatclass = formats.Positions self.analyzer = analysis.NgramAnalyzer(minsize, maxsize) self.format = formatclass(field_boost=field_boost) self.analyzer = analysis.NgramAnalyzer(minsize, maxsize) self.stored = stored self.queryor = queryor self.set_sortable(sortable) def self_parsing(self): return True def parse_query(self, fieldname, qstring, boost=1.0): from whoosh import query terms = [query.Term(fieldname, g) for g in self.process_text(qstring, mode='query')] cls = query.Or if self.queryor else query.And return cls(terms, boost=boost) class NGRAMWORDS(NGRAM): """ Configured field that chops text into words using a tokenizer, lowercases the words, and then chops the words into N-grams. """ scorable = True def __init__(self, minsize=2, maxsize=4, stored=False, field_boost=1.0, tokenizer=None, at=None, queryor=False, sortable=False): """ :param minsize: The minimum length of the N-grams. :param maxsize: The maximum length of the N-grams. :param stored: Whether to store the value of this field with the document. Since this field type generally contains a lot of text, you should avoid storing it with the document unless you need to, for example to allow fast excerpts in the search results. :param tokenizer: an instance of :class:`whoosh.analysis.Tokenizer` used to break the text into words. :param at: if 'start', only takes N-grams from the start of the word. If 'end', only takes N-grams from the end. Otherwise the default is to take all N-grams from each word. :param queryor: if True, combine the N-grams with an Or query. The default is to combine N-grams with an And query. """ self.analyzer = analysis.NgramWordAnalyzer(minsize, maxsize, tokenizer, at=at) self.format = formats.Frequency(field_boost=field_boost) self.stored = stored self.queryor = queryor self.set_sortable(sortable) # Other fields class ReverseField(FieldWrapper): def __init__(self, subfield, prefix="rev_"): FieldWrapper.__init__(self, subfield, prefix) self.analyzer = subfield.analyzer | analysis.ReverseTextFilter() self.format = BasicFormat(lengths=False, weights=False) self.scorable = False self.set_sortable(False) self.stored = False self.unique = False self.vector = False def subfields(self): yield "", self.subfield yield self.name_prefix, self # Schema class class MetaSchema(type): def __new__(cls, name, bases, attrs): super_new = super(MetaSchema, cls).__new__ if not any(b for b in bases if isinstance(b, MetaSchema)): # If this isn't a subclass of MetaSchema, don't do anything special return super_new(cls, name, bases, attrs) # Create the class special_attrs = {} for key in list(attrs.keys()): if key.startswith("__"): special_attrs[key] = attrs.pop(key) new_class = super_new(cls, name, bases, special_attrs) fields = {} for b in bases: if hasattr(b, "_clsfields"): fields.update(b._clsfields) fields.update(attrs) new_class._clsfields = fields return new_class def schema(self): return Schema(**self._clsfields) class Schema(object): """ Represents the collection of fields in an index. Maps field names to FieldType objects which define the behavior of each field. Low-level parts of the index use field numbers instead of field names for compactness. This class has several methods for converting between the field name, field number, and field object itself. """ def __init__(self, **fields): """ All keyword arguments to the constructor are treated as fieldname = fieldtype pairs. The fieldtype can be an instantiated FieldType object, or a FieldType sub-class (in which case the Schema will instantiate it with the default constructor before adding it). For example:: s = Schema(content = TEXT, title = TEXT(stored = True), tags = KEYWORD(stored = True)) """ self._fields = {} self._subfields = {} self._dyn_fields = {} for name in sorted(fields.keys()): self.add(name, fields[name]) def copy(self): """ Returns a shallow copy of the schema. The field instances are not deep copied, so they are shared between schema copies. """ return self.__class__(**self._fields) def __eq__(self, other): return (other.__class__ is self.__class__ and list(self.items()) == list(other.items())) def __ne__(self, other): return not(self.__eq__(other)) def __repr__(self): return "<%s: %r>" % (self.__class__.__name__, self.names()) def __iter__(self): """ Returns the field objects in this schema. """ return iter(self._fields.values()) def __getitem__(self, name): """ Returns the field associated with the given field name. """ # If the name is in the dictionary, just return it if name in self._fields: return self._fields[name] # Check if the name matches a dynamic field for expr, fieldtype in itervalues(self._dyn_fields): if expr.match(name): return fieldtype raise KeyError("No field named %r" % (name,)) def __len__(self): """ Returns the number of fields in this schema. """ return len(self._fields) def __contains__(self, fieldname): """ Returns True if a field by the given name is in this schema. """ # Defined in terms of __getitem__ so that there's only one method to # override to provide dynamic fields try: field = self[fieldname] return field is not None except KeyError: return False def __setstate__(self, state): if "_subfields" not in state: state["_subfields"] = {} self.__dict__.update(state) def to_bytes(self, fieldname, value): return self[fieldname].to_bytes(value) def items(self): """ Returns a list of ("fieldname", field_object) pairs for the fields in this schema. """ return sorted(self._fields.items()) def names(self, check_names=None): """ Returns a list of the names of the fields in this schema. :param check_names: (optional) sequence of field names to check whether the schema accepts them as (dynamic) field names - acceptable names will also be in the result list. Note: You may also have static field names in check_names, that won't create duplicates in the result list. Unsupported names will not be in the result list. """ fieldnames = set(self._fields.keys()) if check_names is not None: check_names = set(check_names) - fieldnames fieldnames.update(fieldname for fieldname in check_names if fieldname in self) return sorted(fieldnames) def clean(self): for field in self: field.clean() def add(self, name, fieldtype, glob=False): """ Adds a field to this schema. :param name: The name of the field. :param fieldtype: An instantiated fields.FieldType object, or a FieldType subclass. If you pass an instantiated object, the schema will use that as the field configuration for this field. If you pass a FieldType subclass, the schema will automatically instantiate it with the default constructor. """ # If the user passed a type rather than an instantiated field object, # instantiate it automatically if type(fieldtype) is type: try: fieldtype = fieldtype() except: e = sys.exc_info()[1] raise FieldConfigurationError("Error: %s instantiating field " "%r: %r" % (e, name, fieldtype)) if not isinstance(fieldtype, FieldType): raise FieldConfigurationError("%r is not a FieldType object" % fieldtype) self._subfields[name] = sublist = [] for prefix, subfield in fieldtype.subfields(): fname = prefix + name sublist.append(fname) # Check field name if fname.startswith("_"): raise FieldConfigurationError("Names cannot start with _") elif " " in fname: raise FieldConfigurationError("Names cannot contain spaces") elif fname in self._fields or (glob and fname in self._dyn_fields): raise FieldConfigurationError("%r already in schema" % fname) # Add the field if glob: expr = re.compile(fnmatch.translate(name)) self._dyn_fields[fname] = (expr, subfield) else: fieldtype.on_add(self, fname) self._fields[fname] = subfield def remove(self, fieldname): if fieldname in self._fields: self._fields[fieldname].on_remove(self, fieldname) del self._fields[fieldname] if fieldname in self._subfields: for subname in self._subfields[fieldname]: if subname in self._fields: del self._fields[subname] del self._subfields[fieldname] elif fieldname in self._dyn_fields: del self._dyn_fields[fieldname] else: raise KeyError("No field named %r" % fieldname) def indexable_fields(self, fieldname): if fieldname in self._subfields: for subname in self._subfields[fieldname]: yield subname, self._fields[subname] else: # Use __getitem__ here instead of getting it directly from _fields # because it might be a glob yield fieldname, self[fieldname] def has_scorable_fields(self): return any(ftype.scorable for ftype in self) def stored_names(self): """ Returns a list of the names of fields that are stored. """ return [name for name, field in self.items() if field.stored] def scorable_names(self): """ Returns a list of the names of fields that store field lengths. """ return [name for name, field in self.items() if field.scorable] class SchemaClass(with_metaclass(MetaSchema, Schema)): """ Allows you to define a schema using declarative syntax, similar to Django models:: class MySchema(SchemaClass): path = ID date = DATETIME content = TEXT You can use inheritance to share common fields between schemas:: class Parent(SchemaClass): path = ID(stored=True) date = DATETIME class Child1(Parent): content = TEXT(positions=False) class Child2(Parent): tags = KEYWORD This class overrides ``__new__`` so instantiating your sub-class always results in an instance of ``Schema``. >>> class MySchema(SchemaClass): ... title = TEXT(stored=True) ... content = TEXT ... >>> s = MySchema() >>> type(s) """ def __new__(cls, *args, **kwargs): obj = super(Schema, cls).__new__(Schema) kw = getattr(cls, "_clsfields", {}) kw.update(kwargs) obj.__init__(*args, **kw) return obj def ensure_schema(schema): if isinstance(schema, type) and issubclass(schema, Schema): schema = schema.schema() if not isinstance(schema, Schema): raise FieldConfigurationError("%r is not a Schema" % schema) return schema def merge_fielddict(d1, d2): keyset = set(d1.keys()) | set(d2.keys()) out = {} for name in keyset: field1 = d1.get(name) field2 = d2.get(name) if field1 and field2 and field1 != field2: raise Exception("Inconsistent field %r: %r != %r" % (name, field1, field2)) out[name] = field1 or field2 return out def merge_schema(s1, s2): schema = Schema() schema._fields = merge_fielddict(s1._fields, s2._fields) schema._dyn_fields = merge_fielddict(s1._dyn_fields, s2._dyn_fields) return schema def merge_schemas(schemas): schema = schemas[0] for i in xrange(1, len(schemas)): schema = merge_schema(schema, schemas[i]) return schema