"""
Public interface to the search code.
"""
-from typing import List, Any, Optional, Iterator, Tuple
+from typing import List, Any, Optional, Iterator, Tuple, Dict
import itertools
import re
import datetime as dt
from nominatim.api.connection import SearchConnection
from nominatim.api.types import SearchDetails
-from nominatim.api.results import SearchResults, add_result_details
+from nominatim.api.results import SearchResult, SearchResults, add_result_details
from nominatim.api.search.token_assignment import yield_token_assignments
from nominatim.api.search.db_search_builder import SearchBuilder, build_poi_search, wrap_near_search
from nominatim.api.search.db_searches import AbstractSearch
log().table_dump('Searches for assignment',
_dump_searches(searches, query, num_searches))
num_searches = len(searches)
- searches.sort(key=lambda s: s.penalty)
+ searches.sort(key=lambda s: (s.penalty, s.SEARCH_PRIO))
return query, searches
is found.
"""
log().section('Execute database searches')
- results = SearchResults()
+ results: Dict[Any, SearchResult] = {}
+
end_time = dt.datetime.now() + self.timeout
- num_results = 0
- min_ranking = 1000.0
+ min_ranking = searches[0].penalty + 2.0
prev_penalty = 0.0
for i, search in enumerate(searches):
if search.penalty > prev_penalty and (search.penalty > min_ranking or i > 20):
break
log().table_dump(f"{i + 1}. Search", _dump_searches([search], query))
- for result in await search.lookup(self.conn, self.params):
- results.append(result)
- min_ranking = min(min_ranking, result.ranking + 0.5, search.penalty + 0.3)
- log().result_dump('Results', ((r.accuracy, r) for r in results[num_results:]))
- num_results = len(results)
+ log().var_dump('Params', self.params)
+ lookup_results = await search.lookup(self.conn, self.params)
+ for result in lookup_results:
+ rhash = (result.source_table, result.place_id,
+ result.housenumber, result.country_code)
+ prevresult = results.get(rhash)
+ if prevresult:
+ prevresult.accuracy = min(prevresult.accuracy, result.accuracy)
+ else:
+ results[rhash] = result
+ min_ranking = min(min_ranking, result.accuracy * 1.2)
+ log().result_dump('Results', ((r.accuracy, r) for r in lookup_results))
prev_penalty = search.penalty
if dt.datetime.now() >= end_time:
break
- return results
+ return SearchResults(results.values())
def sort_and_cut_results(self, results: SearchResults) -> SearchResults:
return
for result in results:
- if not result.display_name:
+ # Negative importance indicates ordering by distance, which is
+ # more important than word matching.
+ if not result.display_name\
+ or (result.importance is not None and result.importance < 0):
continue
distance = 0.0
- norm = self.query_analyzer.normalize_text(result.display_name)
+ norm = self.query_analyzer.normalize_text(' '.join((result.display_name,
+ result.country_code or '')))
words = set((w for w in norm.split(' ') if w))
if not words:
continue
distance += len(qword)
else:
distance += (1.0 - wdist) * len(qword)
- result.accuracy += distance * 0.5 / sum(len(w) for w in qwords)
+ # Compensate for the fact that country names do not get a
+ # match penalty yet by the tokenizer.
+ # Temporary hack that needs to be removed!
+ if result.rank_address == 4:
+ distance *= 2
+ result.accuracy += distance * 0.4 / sum(len(w) for w in qwords)
async def lookup_pois(self, categories: List[Tuple[str, str]],