mirror of
https://github.com/osm-search/Nominatim.git
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315 lines
12 KiB
Python
315 lines
12 KiB
Python
# SPDX-License-Identifier: GPL-3.0-or-later
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#
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# This file is part of Nominatim. (https://nominatim.org)
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#
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# Copyright (C) 2023 by the Nominatim developer community.
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# For a full list of authors see the git log.
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"""
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Implementation of query analysis for the ICU tokenizer.
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"""
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from typing import Tuple, Dict, List, Optional, NamedTuple, Iterator, Any, cast
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from copy import copy
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from collections import defaultdict
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import dataclasses
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import difflib
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from icu import Transliterator
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import sqlalchemy as sa
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from nominatim.typing import SaRow
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from nominatim.api.connection import SearchConnection
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from nominatim.api.logging import log
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from nominatim.api.search import query as qmod
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from nominatim.api.search.query_analyzer_factory import AbstractQueryAnalyzer
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from nominatim.db.sqlalchemy_types import Json
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DB_TO_TOKEN_TYPE = {
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'W': qmod.TokenType.WORD,
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'w': qmod.TokenType.PARTIAL,
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'H': qmod.TokenType.HOUSENUMBER,
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'P': qmod.TokenType.POSTCODE,
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'C': qmod.TokenType.COUNTRY
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}
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class QueryPart(NamedTuple):
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""" Normalized and transliterated form of a single term in the query.
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When the term came out of a split during the transliteration,
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the normalized string is the full word before transliteration.
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The word number keeps track of the word before transliteration
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and can be used to identify partial transliterated terms.
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"""
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token: str
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normalized: str
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word_number: int
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QueryParts = List[QueryPart]
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WordDict = Dict[str, List[qmod.TokenRange]]
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def yield_words(terms: List[QueryPart], start: int) -> Iterator[Tuple[str, qmod.TokenRange]]:
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""" Return all combinations of words in the terms list after the
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given position.
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"""
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total = len(terms)
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for first in range(start, total):
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word = terms[first].token
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yield word, qmod.TokenRange(first, first + 1)
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for last in range(first + 1, min(first + 20, total)):
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word = ' '.join((word, terms[last].token))
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yield word, qmod.TokenRange(first, last + 1)
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@dataclasses.dataclass
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class ICUToken(qmod.Token):
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""" Specialised token for ICU tokenizer.
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"""
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word_token: str
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info: Optional[Dict[str, Any]]
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def get_category(self) -> Tuple[str, str]:
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assert self.info
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return self.info.get('class', ''), self.info.get('type', '')
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def rematch(self, norm: str) -> None:
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""" Check how well the token matches the given normalized string
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and add a penalty, if necessary.
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"""
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if not self.lookup_word:
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return
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seq = difflib.SequenceMatcher(a=self.lookup_word, b=norm)
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distance = 0
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for tag, afrom, ato, bfrom, bto in seq.get_opcodes():
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if tag in ('delete', 'insert') and (afrom == 0 or ato == len(self.lookup_word)):
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distance += 1
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elif tag == 'replace':
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distance += max((ato-afrom), (bto-bfrom))
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elif tag != 'equal':
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distance += abs((ato-afrom) - (bto-bfrom))
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self.penalty += (distance/len(self.lookup_word))
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@staticmethod
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def from_db_row(row: SaRow) -> 'ICUToken':
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""" Create a ICUToken from the row of the word table.
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"""
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count = 1 if row.info is None else row.info.get('count', 1)
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penalty = 0.0
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if row.type == 'w':
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penalty = 0.3
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elif row.type == 'W':
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if len(row.word_token) == 1 and row.word_token == row.word:
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penalty = 0.2 if row.word.isdigit() else 0.3
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elif row.type == 'H':
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penalty = sum(0.1 for c in row.word_token if c != ' ' and not c.isdigit())
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if all(not c.isdigit() for c in row.word_token):
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penalty += 0.2 * (len(row.word_token) - 1)
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elif row.type == 'C':
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if len(row.word_token) == 1:
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penalty = 0.3
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if row.info is None:
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lookup_word = row.word
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else:
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lookup_word = row.info.get('lookup', row.word)
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if lookup_word:
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lookup_word = lookup_word.split('@', 1)[0]
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else:
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lookup_word = row.word_token
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return ICUToken(penalty=penalty, token=row.word_id, count=count,
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lookup_word=lookup_word, is_indexed=True,
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word_token=row.word_token, info=row.info)
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class ICUQueryAnalyzer(AbstractQueryAnalyzer):
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""" Converter for query strings into a tokenized query
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using the tokens created by a ICU tokenizer.
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"""
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def __init__(self, conn: SearchConnection) -> None:
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self.conn = conn
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async def setup(self) -> None:
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""" Set up static data structures needed for the analysis.
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"""
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async def _make_normalizer() -> Any:
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rules = await self.conn.get_property('tokenizer_import_normalisation')
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return Transliterator.createFromRules("normalization", rules)
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self.normalizer = await self.conn.get_cached_value('ICUTOK', 'normalizer',
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_make_normalizer)
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async def _make_transliterator() -> Any:
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rules = await self.conn.get_property('tokenizer_import_transliteration')
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return Transliterator.createFromRules("transliteration", rules)
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self.transliterator = await self.conn.get_cached_value('ICUTOK', 'transliterator',
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_make_transliterator)
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if 'word' not in self.conn.t.meta.tables:
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sa.Table('word', self.conn.t.meta,
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sa.Column('word_id', sa.Integer),
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sa.Column('word_token', sa.Text, nullable=False),
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sa.Column('type', sa.Text, nullable=False),
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sa.Column('word', sa.Text),
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sa.Column('info', Json))
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async def analyze_query(self, phrases: List[qmod.Phrase]) -> qmod.QueryStruct:
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""" Analyze the given list of phrases and return the
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tokenized query.
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"""
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log().section('Analyze query (using ICU tokenizer)')
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normalized = list(filter(lambda p: p.text,
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(qmod.Phrase(p.ptype, self.normalize_text(p.text))
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for p in phrases)))
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query = qmod.QueryStruct(normalized)
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log().var_dump('Normalized query', query.source)
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if not query.source:
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return query
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parts, words = self.split_query(query)
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log().var_dump('Transliterated query', lambda: _dump_transliterated(query, parts))
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for row in await self.lookup_in_db(list(words.keys())):
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for trange in words[row.word_token]:
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token = ICUToken.from_db_row(row)
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if row.type == 'S':
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if row.info['op'] in ('in', 'near'):
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if trange.start == 0:
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query.add_token(trange, qmod.TokenType.NEAR_ITEM, token)
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else:
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query.add_token(trange, qmod.TokenType.QUALIFIER, token)
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if trange.start == 0 or trange.end == query.num_token_slots():
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token = copy(token)
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token.penalty += 0.1 * (query.num_token_slots())
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query.add_token(trange, qmod.TokenType.NEAR_ITEM, token)
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else:
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query.add_token(trange, DB_TO_TOKEN_TYPE[row.type], token)
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self.add_extra_tokens(query, parts)
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self.rerank_tokens(query, parts)
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log().table_dump('Word tokens', _dump_word_tokens(query))
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return query
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def normalize_text(self, text: str) -> str:
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""" Bring the given text into a normalized form. That is the
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standardized form search will work with. All information removed
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at this stage is inevitably lost.
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"""
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return cast(str, self.normalizer.transliterate(text))
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def split_query(self, query: qmod.QueryStruct) -> Tuple[QueryParts, WordDict]:
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""" Transliterate the phrases and split them into tokens.
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Returns the list of transliterated tokens together with their
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normalized form and a dictionary of words for lookup together
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with their position.
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"""
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parts: QueryParts = []
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phrase_start = 0
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words = defaultdict(list)
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wordnr = 0
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for phrase in query.source:
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query.nodes[-1].ptype = phrase.ptype
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for word in phrase.text.split(' '):
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trans = self.transliterator.transliterate(word)
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if trans:
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for term in trans.split(' '):
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if term:
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parts.append(QueryPart(term, word, wordnr))
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query.add_node(qmod.BreakType.TOKEN, phrase.ptype)
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query.nodes[-1].btype = qmod.BreakType.WORD
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wordnr += 1
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query.nodes[-1].btype = qmod.BreakType.PHRASE
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for word, wrange in yield_words(parts, phrase_start):
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words[word].append(wrange)
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phrase_start = len(parts)
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query.nodes[-1].btype = qmod.BreakType.END
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return parts, words
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async def lookup_in_db(self, words: List[str]) -> 'sa.Result[Any]':
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""" Return the token information from the database for the
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given word tokens.
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"""
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t = self.conn.t.meta.tables['word']
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return await self.conn.execute(t.select().where(t.c.word_token.in_(words)))
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def add_extra_tokens(self, query: qmod.QueryStruct, parts: QueryParts) -> None:
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""" Add tokens to query that are not saved in the database.
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"""
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for part, node, i in zip(parts, query.nodes, range(1000)):
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if len(part.token) <= 4 and part[0].isdigit()\
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and not node.has_tokens(i+1, qmod.TokenType.HOUSENUMBER):
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query.add_token(qmod.TokenRange(i, i+1), qmod.TokenType.HOUSENUMBER,
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ICUToken(0.5, 0, 1, part.token, True, part.token, None))
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def rerank_tokens(self, query: qmod.QueryStruct, parts: QueryParts) -> None:
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""" Add penalties to tokens that depend on presence of other token.
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"""
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for i, node, tlist in query.iter_token_lists():
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if tlist.ttype == qmod.TokenType.POSTCODE:
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for repl in node.starting:
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if repl.end == tlist.end and repl.ttype != qmod.TokenType.POSTCODE \
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and (repl.ttype != qmod.TokenType.HOUSENUMBER
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or len(tlist.tokens[0].lookup_word) > 4):
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repl.add_penalty(0.39)
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elif tlist.ttype == qmod.TokenType.HOUSENUMBER \
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and len(tlist.tokens[0].lookup_word) <= 3:
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if any(c.isdigit() for c in tlist.tokens[0].lookup_word):
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for repl in node.starting:
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if repl.end == tlist.end and repl.ttype != qmod.TokenType.HOUSENUMBER:
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repl.add_penalty(0.5 - tlist.tokens[0].penalty)
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elif tlist.ttype not in (qmod.TokenType.COUNTRY, qmod.TokenType.PARTIAL):
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norm = parts[i].normalized
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for j in range(i + 1, tlist.end):
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if parts[j - 1].word_number != parts[j].word_number:
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norm += ' ' + parts[j].normalized
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for token in tlist.tokens:
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cast(ICUToken, token).rematch(norm)
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def _dump_transliterated(query: qmod.QueryStruct, parts: QueryParts) -> str:
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out = query.nodes[0].btype.value
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for node, part in zip(query.nodes[1:], parts):
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out += part.token + node.btype.value
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return out
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def _dump_word_tokens(query: qmod.QueryStruct) -> Iterator[List[Any]]:
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yield ['type', 'token', 'word_token', 'lookup_word', 'penalty', 'count', 'info']
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for node in query.nodes:
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for tlist in node.starting:
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for token in tlist.tokens:
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t = cast(ICUToken, token)
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yield [tlist.ttype.name, t.token, t.word_token or '',
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t.lookup_word or '', t.penalty, t.count, t.info]
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async def create_query_analyzer(conn: SearchConnection) -> AbstractQueryAnalyzer:
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""" Create and set up a new query analyzer for a database based
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on the ICU tokenizer.
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"""
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out = ICUQueryAnalyzer(conn)
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await out.setup()
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return out
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