X-Git-Url: https://git.openstreetmap.org./nominatim.git/blobdiff_plain/8a2c6067a2fc4fae579a5a9b5747b6985ca09b87..7205491b8495e48c62b28373d1746e77d475582b:/nominatim/api/search/db_search_builder.py?ds=sidebyside diff --git a/nominatim/api/search/db_search_builder.py b/nominatim/api/search/db_search_builder.py index c755f2a7..f2b653f2 100644 --- a/nominatim/api/search/db_search_builder.py +++ b/nominatim/api/search/db_search_builder.py @@ -15,6 +15,7 @@ from nominatim.api.search.query import QueryStruct, Token, TokenType, TokenRange from nominatim.api.search.token_assignment import TokenAssignment import nominatim.api.search.db_search_fields as dbf import nominatim.api.search.db_searches as dbs +import nominatim.api.search.db_search_lookups as lookups def wrap_near_search(categories: List[Tuple[str, str]], @@ -152,7 +153,7 @@ class SearchBuilder: sdata.lookups = [dbf.FieldLookup('nameaddress_vector', [t.token for r in address for t in self.query.get_partials_list(r)], - 'restrict')] + lookups.Restrict)] penalty += 0.2 yield dbs.PostcodeSearch(penalty, sdata) @@ -162,24 +163,25 @@ class SearchBuilder: """ Build a simple address search for special entries where the housenumber is the main name token. """ - sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], 'lookup_any')] + sdata.lookups = [dbf.FieldLookup('name_vector', [t.token for t in hnrs], lookups.LookupAny)] expected_count = sum(t.count for t in hnrs) - partials = [t for trange in address - for t in self.query.get_partials_list(trange)] + partials = {t.token: t.count for trange in address + for t in self.query.get_partials_list(trange)} if expected_count < 8000: sdata.lookups.append(dbf.FieldLookup('nameaddress_vector', - [t.token for t in partials], 'restrict')) - elif len(partials) != 1 or partials[0].count < 10000: + list(partials), lookups.Restrict)) + elif len(partials) != 1 or list(partials.values())[0] < 10000: sdata.lookups.append(dbf.FieldLookup('nameaddress_vector', - [t.token for t in partials], 'lookup_all')) + list(partials), lookups.LookupAll)) else: + addr_fulls = [t.token for t + in self.query.get_tokens(address[0], TokenType.WORD)] + if len(addr_fulls) > 5: + return sdata.lookups.append( - dbf.FieldLookup('nameaddress_vector', - [t.token for t - in self.query.get_tokens(address[0], TokenType.WORD)], - 'lookup_any')) + dbf.FieldLookup('nameaddress_vector', addr_fulls, lookups.LookupAny)) sdata.housenumbers = dbf.WeightedStrings([], []) yield dbs.PlaceSearch(0.05, sdata, expected_count) @@ -207,18 +209,17 @@ class SearchBuilder: are and tries to find a lookup that optimizes index use. """ penalty = 0.0 # extra penalty - name_partials = self.query.get_partials_list(name) - name_tokens = [t.token for t in name_partials] + name_partials = {t.token: t for t in self.query.get_partials_list(name)} addr_partials = [t for r in address for t in self.query.get_partials_list(r)] - addr_tokens = [t.token for t in addr_partials] + addr_tokens = list({t.token for t in addr_partials}) - partials_indexed = all(t.is_indexed for t in name_partials) \ + partials_indexed = all(t.is_indexed for t in name_partials.values()) \ and all(t.is_indexed for t in addr_partials) - exp_count = min(t.count for t in name_partials) / (2**(len(name_partials) - 1)) + exp_count = min(t.count for t in name_partials.values()) / (2**(len(name_partials) - 1)) if (len(name_partials) > 3 or exp_count < 8000) and partials_indexed: - yield penalty, exp_count, dbf.lookup_by_names(name_tokens, addr_tokens) + yield penalty, exp_count, dbf.lookup_by_names(list(name_partials.keys()), addr_tokens) return # Partial term to frequent. Try looking up by rare full names first. @@ -231,22 +232,25 @@ class SearchBuilder: addr_tokens = [t.token for t in addr_partials if t.is_indexed] penalty += 1.2 * sum(t.penalty for t in addr_partials if not t.is_indexed) # Any of the full names applies with all of the partials from the address - yield penalty, fulls_count / (2**len(addr_partials)),\ - dbf.lookup_by_any_name([t.token for t in name_fulls], addr_tokens, - 'restrict' if fulls_count < 10000 else 'lookup_all') + yield penalty, fulls_count / (2**len(addr_tokens)),\ + dbf.lookup_by_any_name([t.token for t in name_fulls], + addr_tokens, + fulls_count > 30000 / max(1, len(addr_tokens))) # To catch remaining results, lookup by name and address # We only do this if there is a reasonable number of results expected. - exp_count = exp_count / (2**len(addr_partials)) if addr_partials else exp_count - if exp_count < 10000 and all(t.is_indexed for t in name_partials): - lookup = [dbf.FieldLookup('name_vector', name_tokens, 'lookup_all')] + exp_count = exp_count / (2**len(addr_tokens)) if addr_tokens else exp_count + if exp_count < 10000 and all(t.is_indexed for t in name_partials.values()): + lookup = [dbf.FieldLookup('name_vector', list(name_partials.keys()), lookups.LookupAll)] if addr_tokens: - lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, 'lookup_all')) - penalty += 0.35 * max(0, 5 - len(name_partials) - len(addr_tokens)) + lookup.append(dbf.FieldLookup('nameaddress_vector', addr_tokens, lookups.LookupAll)) + penalty += 0.35 * max(1 if name_fulls else 0.1, + 5 - len(name_partials) - len(addr_tokens)) yield penalty, exp_count, lookup - def get_name_ranking(self, trange: TokenRange) -> dbf.FieldRanking: + def get_name_ranking(self, trange: TokenRange, + db_field: str = 'name_vector') -> dbf.FieldRanking: """ Create a ranking expression for a name term in the given range. """ name_fulls = self.query.get_tokens(trange, TokenType.WORD) @@ -255,7 +259,7 @@ class SearchBuilder: # Fallback, sum of penalty for partials name_partials = self.query.get_partials_list(trange) default = sum(t.penalty for t in name_partials) + 0.2 - return dbf.FieldRanking('name_vector', default, ranks) + return dbf.FieldRanking(db_field, default, ranks) def get_addr_ranking(self, trange: TokenRange) -> dbf.FieldRanking: @@ -313,11 +317,9 @@ class SearchBuilder: sdata = dbf.SearchData() sdata.penalty = assignment.penalty if assignment.country: - tokens = self.query.get_tokens(assignment.country, TokenType.COUNTRY) - if self.details.countries: - tokens = [t for t in tokens if t.lookup_word in self.details.countries] - if not tokens: - return None + tokens = self.get_country_tokens(assignment.country) + if not tokens: + return None sdata.set_strings('countries', tokens) elif self.details.countries: sdata.countries = dbf.WeightedStrings(self.details.countries, @@ -331,24 +333,54 @@ class SearchBuilder: self.query.get_tokens(assignment.postcode, TokenType.POSTCODE)) if assignment.qualifier: - tokens = self.query.get_tokens(assignment.qualifier, TokenType.QUALIFIER) - if self.details.categories: - tokens = [t for t in tokens if t.get_category() in self.details.categories] - if not tokens: - return None + tokens = self.get_qualifier_tokens(assignment.qualifier) + if not tokens: + return None sdata.set_qualifiers(tokens) elif self.details.categories: sdata.qualifiers = dbf.WeightedCategories(self.details.categories, [0.0] * len(self.details.categories)) if assignment.address: - sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address]) + if not assignment.name and assignment.housenumber: + # housenumber search: the first item needs to be handled like + # a name in ranking or penalties are not comparable with + # normal searches. + sdata.set_ranking([self.get_name_ranking(assignment.address[0], + db_field='nameaddress_vector')] + + [self.get_addr_ranking(r) for r in assignment.address[1:]]) + else: + sdata.set_ranking([self.get_addr_ranking(r) for r in assignment.address]) else: sdata.rankings = [] return sdata + def get_country_tokens(self, trange: TokenRange) -> List[Token]: + """ Return the list of country tokens for the given range, + optionally filtered by the country list from the details + parameters. + """ + tokens = self.query.get_tokens(trange, TokenType.COUNTRY) + if self.details.countries: + tokens = [t for t in tokens if t.lookup_word in self.details.countries] + + return tokens + + + def get_qualifier_tokens(self, trange: TokenRange) -> List[Token]: + """ Return the list of qualifier tokens for the given range, + optionally filtered by the qualifier list from the details + parameters. + """ + tokens = self.query.get_tokens(trange, TokenType.QUALIFIER) + if self.details.categories: + tokens = [t for t in tokens if t.get_category() in self.details.categories] + + return tokens + + def get_near_items(self, assignment: TokenAssignment) -> Optional[dbf.WeightedCategories]: """ Collect tokens for near items search or use the categories requested per parameter.