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example: vectorsearch-capital #31 
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‎example/README.md‎

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@@ -4,6 +4,8 @@ The following examples are available from this repository.
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## Application Example: Tasks
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This is our classic Tasks application using a CLI.
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```
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cd example
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python -m tasks
@@ -28,3 +30,47 @@ Welcome to the ObjectBox tasks-list app example. Type help or ? for a list of co
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> exit
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```
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## Vector-Search Example: Capitals
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This example application starts with a pre-defined set of capitals and their geo coordinates.
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It allows to search for nearest neighbors by capital (`capital_neighbors`) or by coordinates (`neighbors`) as well as adding more locations (`add`).
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```
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python -m vectorsearch-capitals
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Welcome to the ObjectBox vectorsearch-capitals example. Type help or ? for a list of commands.
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> ls
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ID Name Latitude Longitude
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1 Abuja 9.08 7.40
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2 Accra 5.60 -0.19
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[..]
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212 Yerevan 40.19 44.52
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213 Zagreb 45.81 15.98
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> ls Ber
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ID Name Latitude Longitude
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28 Berlin 52.52 13.40
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29 Bern 46.95 7.45
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> city_neighbors Berlin
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> city_neighbors Berlin
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ID Name Latitude Longitude Score
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147 Prague 50.08 14.44 7.04
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49 Copenhagen 55.68 12.57 10.66
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200 Vienna 48.21 16.37 27.41
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34 Bratislava 48.15 17.11 32.82
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89 Ljubljana 46.06 14.51 42.98
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> neighbors 6,52.52,13.405
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ID Name Latitude Longitude Score
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28 Berlin 52.52 13.40 0.00
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147 Prague 50.08 14.44 7.04
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49 Copenhagen 55.68 12.57 10.66
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200 Vienna 48.21 16.37 27.41
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34 Bratislava 48.15 17.11 32.82
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89 Ljubljana 46.06 14.51 42.98
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> add Area51, 37.23, -115.81
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> city_neighbors Area51
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ID Name Latitude Longitude Score
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107 Mexico City 19.43 -99.13 594.86
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27 Belmopan 17.25 -88.76 1130.92
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64 Guatemala City 14.63 -90.51 1150.79
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164 San Salvador 13.69 -89.22 1261.12
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67 Havana 23.11 -82.37 1317.73
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```

‎example/vectorsearch-capitals/__init__.py‎

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‎example/vectorsearch-capitals/__main__.py‎

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from cmd import Cmd
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import objectbox
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import time
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from .model import *
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import csv
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import os
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def list_cities(cities):
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print("{:3s} {:25s} {:>9s} {:>9s}".format("ID", "Name", "Latitude", "Longitude"))
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for city in cities:
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print("{:3d} {:25s} {:>9.2f} {:>9.2f}".format(
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city.id, city.name, city.location[0], city.location[1]))
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def list_cities_with_scores(city_score_tuples):
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print("{:3s} {:25s} {:>9s} {:>9s} {:>5s}".format("ID", "Name", "Latitude", "Longitude", "Score"))
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for (city,score) in city_score_tuples:
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print("{:3d} {:25s} {:>9.2f} {:>9.2f} {:>5.2f}".format(
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city.id, city.name, city.location[0], city.location[1], score))
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class VectorSearchCitysCmd(Cmd):
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prompt = "> "
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def __init__(self, *args):
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Cmd.__init__(self, *args)
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dbdir = "cities-db"
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new_db = not os.path.exists(dbdir)
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self._ob = objectbox.Builder().model(get_objectbox_model()).directory(dbdir).build()
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self._box = objectbox.Box(self._ob, City)
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self._name_prop: Property = City.get_property("name")
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self._location_prop: Property = City.get_property("location")
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if new_db:
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with open(os.path.join(os.path.dirname(__file__), 'cities.csv')) as f:
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r = csv.reader(f)
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cities = []
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for row in r:
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city = City()
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city.name = row[0]
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city.location = [ row[1], row[2] ]
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cities.append(city)
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self._box.put(*cities)
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def do_ls(self, name: str = ""):
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"""list all cities or starting with <prefix>\nusage: ls [<prefix>]"""
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qb = self._box.query()
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qb.starts_with_string(self._name_prop, name)
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query = qb.build()
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list_cities(query.find())
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def do_city_neighbors(self, city: str):
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"""find five next neighbors to city <name>\nusage: city_neighbors <name>"""
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qb = self._box.query()
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qb.equals_string(self._name_prop, city)
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query = qb.build()
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cities = query.find()
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if len(cities) == 1:
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location = cities[0].location
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qb = self._box.query()
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qb.nearest_neighbors_f32(self._location_prop, location, 6)
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qb.not_equals_string(self._name_prop, city)
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neighbors = qb.build().find_with_scores()
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list_cities_with_scores(neighbors)
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else:
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print(f"no city found named '{city}'")
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def do_neighbors(self, args):
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"""find <count> neighbors to geo-coord <lat> <long>.\nusage: neighbors <count>,<latitude>,<longitude>"""
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try:
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args = args.split(',')
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if len(args) != 3:
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raise ValueError()
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count = int(args[0])
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geocoord = [ float(args[1]), float(args[2]) ]
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qb = self._box.query()
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qb.nearest_neighbors_f32(self._location_prop, geocoord, count)
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neighbors = qb.build().find_with_scores()
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list_cities_with_scores(neighbors)
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except ValueError:
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print("usage: neighbors <count>,<latitude>,<longitude>")
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def do_add(self, args: str):
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"""add new location\nusage: add <name>,<lat>,<long>"""
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try:
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args = args.split(',')
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if len(args) != 3:
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raise ValueError()
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name = str(args[0])
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lat = float(args[1])
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long = float(args[2])
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city = City()
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city.name = name
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city.location = [lat,long]
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self._box.put(city)
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except ValueError:
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print("usage: add <name>,<latitude>,<longitude>")
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def do_exit(self, _):
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"""close the program"""
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raise SystemExit()
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if __name__ == '__main__':
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app = VectorSearchCitysCmd()
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app.cmdloop('Welcome to the ObjectBox vectorsearch-cities example. Type help or ? for a list of commands.')

‎example/vectorsearch-capitals/cities.csv‎

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Abuja, 9.0765, 7.3986
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Accra, 5.6037, -0.1870
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Addis Ababa, 9.0084, 38.7813
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Algiers, 36.7529, 3.0420
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Amman, 31.9632, 35.9306
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Amsterdam, 52.3667, 4.8945
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Ankara, 39.9334, 32.8597
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Antananarivo, -18.8792, 47.5079
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Apia, -13.8330, -171.7667
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Ashgabat, 37.9601, 58.3261
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Asmara, 15.3229, 38.9251
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Astana, 51.1796, 71.4475
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Asunción, -25.2637, -57.5759
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Athens, 37.9795, 23.7162
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Avarua, -21.2079, -159.7750
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Baghdad, 33.3152, 44.3661
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Baku, 40.4093, 49.8671
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Bamako, 12.6530, -7.9864
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Bandar Seri Begawan, 4.9031, 114.9398
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Bangkok, 13.7563, 100.5018
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Bangui, 4.3947, 18.5582
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Banjul, 13.4549, -16.5790
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Basseterre, 17.3026, -62.7177
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Beijing, 39.9042, 116.4074
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Beirut, 33.8889, 35.4944
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Belgrade, 44.7866, 20.4489
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Belmopan, 17.2510, -88.7590
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Berlin, 52.5200, 13.4050
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Bern, 46.9480, 7.4474
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Bishkek, 42.8746, 74.5698
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Bissau, 11.8636, -15.5842
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Bogotá, 4.7109, -74.0721
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Brasília, -15.8267, -47.9218
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Bratislava, 48.1486, 17.1077
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Brazzaville, -4.2634, 15.2429
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Bridgetown, 13.1132, -59.5988
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Brussels, 50.8503, 4.3517
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Bucharest, 44.4268, 26.1025
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Budapest, 47.4979, 19.0402
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Buenos Aires, -34.6037, -58.3816
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Bujumbura, -3.3818, 29.3622
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Cairo, 30.0444, 31.2357
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Canberra, -35.2809, 149.1300
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Caracas, 10.4806, -66.9036
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Castries, 14.0101, -60.9874
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Chisinau, 47.0105, 28.8638
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Colombo, 6.9271, 79.8612
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Conakry, 9.6412, -13.5784
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Copenhagen, 55.6761, 12.5683
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Dakar, 14.7167, -17.4677
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Damascus, 33.5131, 36.2919
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Dhaka, 23.8103, 90.4125
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Dili, -8.5569, 125.5603
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Djibouti, 11.5890, 43.1456
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Dodoma, -6.1748, 35.7469
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Doha, 25.2854, 51.5310
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Dublin, 53.3498, -6.2603
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Dushanbe, 38.5868, 68.7841
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Freetown, 8.4840, -13.2299
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Funafuti, -8.5210, 179.1962
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Gaborone, -24.6282, 25.9231
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Georgetown, 6.8013, -58.1550
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Gibraltar, 36.1408, -5.3536
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Guatemala City, 14.6349, -90.5069
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Hanoi, 21.0278, 105.8342
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Harare, -17.8252, 31.0335
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Havana, 23.1136, -82.3666
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Helsinki, 60.1699, 24.9384
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Honiara, -9.4376, 159.9720
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Islamabad, 33.6844, 73.0479
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Jakarta, -6.2088, 106.8456
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Juba, 4.8594, 31.5713
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Kabul, 34.5553, 69.2075
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Kampala, 0.3476, 32.5825
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Kathmandu, 27.7172, 85.3240
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Khartoum, 15.5007, 32.5599
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Kiev, 50.4501, 30.5234
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Kigali, -1.9441, 30.0619
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Kingston, 17.9710, -76.7924
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Kingstown, 13.1467, -61.2121
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Kinshasa, -4.4419, 15.2663
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Kuala Lumpur, 3.1390, 101.6869
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Kuwait City, 29.3759, 47.9774
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La Paz, -16.4897, -68.1193
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Libreville, 0.4162, 9.4673
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Lilongwe, -13.9626, 33.7741
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Lima, -12.0464, -77.0428
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Lisbon, 38.7223, -9.1393
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Ljubljana, 46.0569, 14.5058
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Lomé, 6.1319, 1.2228
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London, 51.5072, -0.1276
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Luanda, -8.8399, 13.2894
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Lusaka, -15.3875, 28.3228
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Luxembourg City, 49.6116, 6.1319
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Madrid, 40.4168, -3.7038
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Majuro, 7.1164, 171.1859
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Malabo, 3.7508, 8.7839
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Male, 4.1755, 73.5093
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Mamoudzou, -12.7871, 45.2750
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Managua, 12.1364, -86.2514
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Manama, 26.2285, 50.5860
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Manila, 14.5995, 120.9842
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Maputo, -25.8918, 32.6051
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Maseru, -29.2976, 27.4854
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Mbabane, -26.3054, 31.1367
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Melekeok, 7.4874, 134.6265
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Mexico City, 19.4326, -99.1332
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Minsk, 53.9045, 27.5615
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Mogadishu, 2.0469, 45.3182
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Monaco, 43.7325, 7.4189
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Monrovia, 6.3005, -10.7974
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Montevideo, -34.9011, -56.1645
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Moroni, -11.7022, 43.2551
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Moscow, 55.7558, 37.6173
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Muscat, 23.5859, 58.4059
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Nairobi, -1.2921, 36.8219
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Nassau, 25.0478, -77.3554
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Naypyidaw, 19.7633, 96.0785
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New Delhi, 28.6139, 77.2090
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Ngerulmud, 7.5004, 134.6249
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Niamey, 13.5122, 2.1254
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Nicosia, 35.1725, 33.365
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Nicosia Northern Cyprus, 35.19, 33.363611
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Nouakchott, 18.0735, -15.9582
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Nuku'alofa, -21.1393, -175.2049
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Nuuk, 64.1836, -51.7214
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Oranjestad, 12.5092, -70.0086
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Oslo, 59.9139, 10.7522
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Ottawa, 45.4215, -75.6972
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Ouagadougou, 12.3714, -1.5197
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Pago Pago, -14.2794, -170.7004
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Palikir, 6.9248, 158.1614
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Panama City, 8.9824, -79.5199
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Papeete, -17.5350, -149.5699
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Paramaribo, 5.8520, -55.2038
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Paris, 48.8566, 2.3522
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Philipsburg, 18.0255, -63.0450
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Phnom Penh, 11.5564, 104.9282
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Plymouth, 16.7056, -62.2126
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Podgorica, 42.4304, 19.2594
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Port Louis, -20.1619, 57.4989
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Port Moresby, -9.4438, 147.1803
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Port Vila, -17.7416, 168.3213
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Port-au-Prince, 18.5944, -72.3074
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Port of Spain, 10.6596, -61.4789
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Porto-Novo, 6.4968, 2.6283
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Prague, 50.0755, 14.4378
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Praia, 14.9195, -23.5087
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Pretoria, -25.7463, 28.1876
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Pristina, 42.6629, 21.1655
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Pyongyang, 39.0392, 125.7625
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Quito, -0.1807, -78.4678
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Rabat, 33.9693, -6.9275
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Reykjavik, 64.1466, -21.9426
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Riga, 56.9496, 24.1052
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Riyadh, 24.7136, 46.6753
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Road Town, 18.4207, -64.6399
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Rome, 41.9028, 12.4964
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Roseau, 15.3092, -61.3794
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Saipan, 15.1833, 145.7500
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San José, 9.9281, -84.0907
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San Juan, 18.4655, -66.1057
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San Marino, 43.9424, 12.4578
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San Salvador, 13.6929, -89.2182
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Sana'a, 15.3694, 44.1910
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Santiago, -33.4489, -70.6693
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Santo Domingo, 18.4861, -69.9312
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Sarajevo, 43.8564, 18.4131
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Seoul, 37.5665, 126.9780
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Singapore, 1.3521, 103.8198
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Skopje, 41.9973, 21.4279
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Sofia, 42.6975, 23.3241
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Sri Jayawardenepura Kotte, 6.8928, 79.9277
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St. George's, 12.0561, -61.7485
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St. Helier, 49.1839, -2.1064
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St. John's, 17.1171, -61.8456
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St. Peter Port, 49.4599, -2.5352
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Stanley, -51.7020, -57.8517
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Stockholm, 59.3293, 18.0686
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Sucre, -19.0421, -65.2559
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Sukhumi, 43.0004, 41.0234
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Suva, -18.1416, 178.4419
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Taipei, 25.0330, 121.5654
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Tallinn, 59.4370, 24.7536
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Tarawa, 1.4170, 173.0000
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Tashkent, 41.2995, 69.2401
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Tbilisi, 41.7151, 44.8271
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Tegucigalpa, 14.0818, -87.2068
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Tehran, 35.6892, 51.3890
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Thimphu, 27.4728, 89.6390
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Tirana, 41.3275, 19.8187
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Tokyo, 35.6762, 139.6503
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Tripoli, 32.8867, 13.1910
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Tunis, 36.8065, 10.1815
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Ulaanbaatar, 47.8864, 106.9057
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Vaduz, 47.1410, 9.5215
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Valletta, 35.9042, 14.5189
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Vatican City, 41.9029, 12.4534
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Victoria, -4.6182, 55.4515
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Vienna, 48.2082, 16.3738
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Vientiane, 17.9757, 102.6331
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Vilnius, 54.6872, 25.2797
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Warsaw, 52.2297, 21.0122
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Washington D.C., 38.9072, -77.0369
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Wellington, -41.2865, 174.7762
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West Island, -12.1880, 96.8292
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Willemstad, 12.1091, -68.9319
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Windhoek, -22.5749, 17.0805
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Yamoussoukro, 6.8276, -5.2893
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Yaoundé, 3.8480, 11.5021
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Yaren, -0.5467, 166.9209
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Yerevan, 40.1872, 44.5152
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Zagreb, 45.8150, 15.9819

‎example/vectorsearch-capitals/model.py‎

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from objectbox.model import *
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from objectbox.model.properties import *
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import objectbox
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import numpy as np
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@Entity(id=1, uid=1)
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class City:
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id = Id(id=1, uid=1001)
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name = Property(str, id=2, uid=1002)
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location = Property(np.ndarray, type=PropertyType.floatVector, id=3, uid=1003, index=HnswIndex(
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id=3, uid=10001,
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dimensions=2,
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distance_type=HnswDistanceType.EUCLIDEAN
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))
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def get_objectbox_model():
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m = Model()
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m.entity(City, last_property_id=IdUid(3, 1003))
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m.last_entity_id = IdUid(1, 1)
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m.last_index_id = IdUid(3,10001)
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return m

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