Merge remote-tracking branch 'upstream/master'

[nominatim.git] / nominatim / api / search / db_search_builder.py

diff --git a/nominatim/api/search/db_search_builder.py b/nominatim/api/search/db_search_builder.py
index 3d5796c526c18494242efa442c46813aa6b02b14..97e7ac0282a79b40e7015bcd1069c8edbadeb09e 100644 (file)
--- a/nominatim/api/search/db_search_builder.py
+++ b/nominatim/api/search/db_search_builder.py
@@ -5,7 +5,7 @@
 # Copyright (C) 2023 by the Nominatim developer community.
 # For a full list of authors see the git log.
 """
 # Copyright (C) 2023 by the Nominatim developer community.
 # For a full list of authors see the git log.
 """

-Convertion from token assignment to an abstract DB search.
+Conversion from token assignment to an abstract DB search.

 """
 from typing import Optional, List, Tuple, Iterator, Dict
 import heapq
 """
 from typing import Optional, List, Tuple, Iterator, Dict
 import heapq


@@ -176,11 +176,12 @@ class SearchBuilder:
             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
                                                  list(partials), lookups.LookupAll))
         else:
             sdata.lookups.append(dbf.FieldLookup('nameaddress_vector',
                                                  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(
             sdata.lookups.append(

-                dbf.FieldLookup('nameaddress_vector',
-                                [t.token for t
-                                 in self.query.get_tokens(address[0], TokenType.WORD)],
-                                lookups.LookupAny))
+                dbf.FieldLookup('nameaddress_vector', addr_fulls, lookups.LookupAny))

 
         sdata.housenumbers = dbf.WeightedStrings([], [])
         yield dbs.PlaceSearch(0.05, sdata, expected_count)
 
         sdata.housenumbers = dbf.WeightedStrings([], [])
         yield dbs.PlaceSearch(0.05, sdata, expected_count)


@@ -225,27 +226,74 @@ class SearchBuilder:
         name_fulls = self.query.get_tokens(name, TokenType.WORD)
         if name_fulls:
             fulls_count = sum(t.count for t in name_fulls)
         name_fulls = self.query.get_tokens(name, TokenType.WORD)
         if name_fulls:
             fulls_count = sum(t.count for t in name_fulls)

-            # At this point drop unindexed partials from the address.
-            # This might yield wrong results, nothing we can do about that.
-            if not partials_indexed:
-                addr_tokens = [t.token for t in addr_partials if t.is_indexed]
+            if len(name_partials) == 1:
+                penalty += min(0.5, max(0, (exp_count - 50 * fulls_count) / (2000 * fulls_count)))
+            if partials_indexed:

                 penalty += 1.2 * sum(t.penalty for t in addr_partials if not 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_tokens)),\
-                  dbf.lookup_by_any_name([t.token for t in name_fulls],
-                                         addr_tokens,
-                                         fulls_count > 30000 / max(1, len(addr_tokens)))
+
+            yield penalty,fulls_count / (2**len(addr_tokens)), \
+                  self.get_full_name_ranking(name_fulls, addr_partials,
+                                             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_tokens)) if addr_tokens else exp_count
         if exp_count < 10000 and all(t.is_indexed for t in name_partials.values()):
 
         # 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_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, lookups.LookupAll))

             penalty += 0.35 * max(1 if name_fulls else 0.1,
                                   5 - len(name_partials) - len(addr_tokens))
             penalty += 0.35 * max(1 if name_fulls else 0.1,
                                   5 - len(name_partials) - len(addr_tokens))

-            yield penalty, exp_count, lookup
+            yield penalty, exp_count,\
+                  self.get_name_address_ranking(list(name_partials.keys()), addr_partials)
+
+
+    def get_name_address_ranking(self, name_tokens: List[int],
+                                 addr_partials: List[Token]) -> List[dbf.FieldLookup]:
+        """ Create a ranking expression looking up by name and address.
+        """
+        lookup = [dbf.FieldLookup('name_vector', name_tokens, lookups.LookupAll)]
+
+        addr_restrict_tokens = []
+        addr_lookup_tokens = []
+        for t in addr_partials:
+            if t.is_indexed:
+                if t.addr_count > 20000:
+                    addr_restrict_tokens.append(t.token)
+                else:
+                    addr_lookup_tokens.append(t.token)
+
+        if addr_restrict_tokens:
+            lookup.append(dbf.FieldLookup('nameaddress_vector',
+                                          addr_restrict_tokens, lookups.Restrict))
+        if addr_lookup_tokens:
+            lookup.append(dbf.FieldLookup('nameaddress_vector',
+                                          addr_lookup_tokens, lookups.LookupAll))
+
+        return lookup
+
+
+    def get_full_name_ranking(self, name_fulls: List[Token], addr_partials: List[Token],
+                              use_lookup: bool) -> List[dbf.FieldLookup]:
+        """ Create a ranking expression with full name terms and
+            additional address lookup. When 'use_lookup' is true, then
+            address lookups will use the index, when the occurences are not
+            too many.
+        """
+        # At this point drop unindexed partials from the address.
+        # This might yield wrong results, nothing we can do about that.
+        if use_lookup:
+            addr_restrict_tokens = []
+            addr_lookup_tokens = []
+            for t in addr_partials:
+                if t.is_indexed:
+                    if t.addr_count > 20000:
+                        addr_restrict_tokens.append(t.token)
+                    else:
+                        addr_lookup_tokens.append(t.token)
+        else:
+            addr_restrict_tokens = [t.token for t in addr_partials if t.is_indexed]
+            addr_lookup_tokens = []
+
+        return dbf.lookup_by_any_name([t.token for t in name_fulls],
+                                      addr_restrict_tokens, addr_lookup_tokens)

 
 
     def get_name_ranking(self, trange: TokenRange,
 
 
     def get_name_ranking(self, trange: TokenRange,