mirror of
https://github.com/osm-search/Nominatim.git
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272 lines
11 KiB
Python
272 lines
11 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 legacy tokenizer.
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"""
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from typing import Tuple, Dict, List, Optional, 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 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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def yield_words(terms: List[str], 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]
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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]))
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yield word, qmod.TokenRange(first, last + 1)
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@dataclasses.dataclass
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class LegacyToken(qmod.Token):
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""" Specialised token for legacy tokenizer.
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"""
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word_token: str
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category: Optional[Tuple[str, str]]
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country: Optional[str]
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operator: Optional[str]
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@property
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def info(self) -> Dict[str, Any]:
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""" Dictionary of additional propoerties of the token.
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Should only be used for debugging purposes.
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"""
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return {'category': self.category,
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'country': self.country,
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'operator': self.operator}
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def get_category(self) -> Tuple[str, str]:
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assert self.category
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return self.category
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class LegacyQueryAnalyzer(AbstractQueryAnalyzer):
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""" Converter for query strings into a tokenized query
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using the tokens created by a legacy 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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self.max_word_freq = int(await self.conn.get_property('tokenizer_maxwordfreq'))
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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('word', sa.Text),
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sa.Column('class', sa.Text),
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sa.Column('type', sa.Text),
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sa.Column('country_code', sa.Text),
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sa.Column('search_name_count', sa.Integer),
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sa.Column('operator', sa.Text))
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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 Legacy tokenizer)')
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normalized = []
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if phrases:
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for row in await self.conn.execute(sa.select(*(sa.func.make_standard_name(p.text)
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for p in phrases))):
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normalized = [qmod.Phrase(p.ptype, r) for r, p in zip(row, phrases) if r]
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break
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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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lookup_words = list(words.keys())
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log().var_dump('Split query', parts)
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log().var_dump('Extracted words', lookup_words)
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for row in await self.lookup_in_db(lookup_words):
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for trange in words[row.word_token.strip()]:
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token, ttype = self.make_token(row)
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if ttype == qmod.TokenType.NEAR_ITEM:
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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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elif ttype == qmod.TokenType.QUALIFIER:
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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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elif ttype != qmod.TokenType.PARTIAL or trange.start + 1 == trange.end:
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query.add_token(trange, ttype, token)
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self.add_extra_tokens(query, parts)
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self.rerank_tokens(query)
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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.
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This only removes case, so some difference with the normalization
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in the phrase remains.
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"""
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return text.lower()
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def split_query(self, query: qmod.QueryStruct) -> Tuple[List[str],
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Dict[str, List[qmod.TokenRange]]]:
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""" Transliterate the phrases and split them into tokens.
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Returns a list of transliterated tokens and a dictionary
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of words for lookup together with their position.
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"""
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parts: List[str] = []
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phrase_start = 0
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words = defaultdict(list)
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for phrase in query.source:
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query.nodes[-1].ptype = phrase.ptype
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for trans in phrase.text.split(' '):
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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(trans)
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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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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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sql = t.select().where(t.c.word_token.in_(words + [' ' + w for w in words]))
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return await self.conn.execute(sql)
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def make_token(self, row: SaRow) -> Tuple[LegacyToken, qmod.TokenType]:
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""" Create a LegacyToken from the row of the word table.
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Also determines the type of token.
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"""
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penalty = 0.0
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is_indexed = True
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rowclass = getattr(row, 'class')
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if row.country_code is not None:
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ttype = qmod.TokenType.COUNTRY
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lookup_word = row.country_code
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elif rowclass is not None:
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if rowclass == 'place' and row.type == 'house':
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ttype = qmod.TokenType.HOUSENUMBER
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lookup_word = row.word_token[1:]
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elif rowclass == 'place' and row.type == 'postcode':
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ttype = qmod.TokenType.POSTCODE
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lookup_word = row.word_token[1:]
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else:
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ttype = qmod.TokenType.NEAR_ITEM if row.operator in ('in', 'near')\
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else qmod.TokenType.QUALIFIER
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lookup_word = row.word
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elif row.word_token.startswith(' '):
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ttype = qmod.TokenType.WORD
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lookup_word = row.word or row.word_token[1:]
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else:
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ttype = qmod.TokenType.PARTIAL
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lookup_word = row.word_token
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penalty = 0.21
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if row.search_name_count > self.max_word_freq:
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is_indexed = False
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return LegacyToken(penalty=penalty, token=row.word_id,
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count=row.search_name_count or 1,
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lookup_word=lookup_word,
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word_token=row.word_token.strip(),
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category=(rowclass, row.type) if rowclass is not None else None,
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country=row.country_code,
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operator=row.operator,
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is_indexed=is_indexed),\
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ttype
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def add_extra_tokens(self, query: qmod.QueryStruct, parts: List[str]) -> 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) <= 4 and part.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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LegacyToken(penalty=0.5, token=0, count=1,
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lookup_word=part, word_token=part,
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category=None, country=None,
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operator=None, is_indexed=True))
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def rerank_tokens(self, query: qmod.QueryStruct) -> 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 _, 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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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(LegacyToken, 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 = LegacyQueryAnalyzer(conn)
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await out.setup()
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return out
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