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import math
import numpy as np
import random
from common import *
from objectbox.query_builder import QueryBuilder
from typing import *
def _find_expected_nn(points: np.ndarray, query: np.ndarray, n: int):
""" Given a set of points of shape (N, P) and a query of shape (P), finds the n points nearest to query. """
assert points.ndim == 2 and query.ndim == 1
assert points.shape[1] == query.shape[0]
d = np.linalg.norm(points - query, axis=1) # Euclidean distance
return np.argsort(d)[:n]
def _test_random_points(
num_points: int,
num_query_points: int,
seed: Optional[int] = None,
distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN,
min_score: float = 0.5):
""" Generates random points in a 2d plane; checks the queried NN against the expected. """
vector_field_name = "vector_"+distance_type.name.lower()
print(f"Test random points; Points: {num_points}, Query points: {num_query_points}, Seed: {seed}")
k = 10
if seed is not None:
np.random.seed(seed)
points = np.random.rand(num_points, 2).astype(np.float32)
test_store = create_test_store()
# Init and seed DB
box = test_store.box(VectorEntity)
print(f"Seeding DB with {num_points} points...")
objects = []
for i in range(points.shape[0]):
object_ = VectorEntity()
object_.name = f"point_{i}"
setattr(object_, vector_field_name, points[i])
objects.append(object_)
box.put(*objects)
print(f"DB seeded with {box.count()} random points!")
assert box.count() == num_points
# Generate a random list of query points
query_points = np.random.rand(num_query_points, 2).astype(np.float32)
# Iterate query points, and compare expected result with OBX result
print(f"Running {num_query_points} searches...")
for i in range(query_points.shape[0]):
query_point = query_points[i]
# Find the ground truth (brute force)
expected_result = _find_expected_nn(points, query_point, k) + 1 # + 1 because OBX IDs start from 1
assert len(expected_result) == k
# Run ANN with OBX
qb = box.query()
qb.nearest_neighbors_f32(vector_field_name, query_point, k)
query = qb.build()
obx_result = [id_ for id_, score in query.find_ids_with_scores()] # Ignore score
assert len(obx_result) == k
# We would like at least half of the expected results, to be returned by the search (in any order)
# Remember: it's an approximate search!
search_score = len(np.intersect1d(expected_result, obx_result)) / k
assert search_score >= min_score # TODO likely could be increased
print(f"Done!")
test_store.close()
def test_random_points():
min_score = 0.5
distance_type = VectorDistanceType.EUCLIDEAN
_test_random_points(num_points=100, num_query_points=10, seed=10, distance_type=distance_type, min_score=min_score)
_test_random_points(num_points=100, num_query_points=10, seed=11, distance_type=distance_type, min_score=min_score)
_test_random_points(num_points=100, num_query_points=10, seed=12, distance_type=distance_type, min_score=min_score)
_test_random_points(num_points=100, num_query_points=10, seed=13, distance_type=distance_type, min_score=min_score)
_test_random_points(num_points=100, num_query_points=10, seed=14, distance_type=distance_type, min_score=min_score)
_test_random_points(num_points=100, num_query_points=10, seed=15, distance_type=distance_type, min_score=min_score)
def _test_combined_nn_search(test_store: Store, distance_type: VectorDistanceType = VectorDistanceType.EUCLIDEAN):
box = test_store.box(VectorEntity)
vector_field_name = "vector_"+distance_type.name.lower()
values = [
("Power of red", [1, 1]),
("Blueberry", [2, 2]),
("Red", [3, 3]),
("Blue sea", [4, 4]),
("Lightblue", [5, 5]),
("Red apple", [6, 6]),
("Hundred", [7, 7]),
("Tired", [8, 8]),
("Power of blue", [9, 9])
]
for value in values:
entity = VectorEntity()
setattr(entity, "name", value[0])
setattr(entity, vector_field_name, value[1])
box.put(entity)
assert box.count() == 9
# Test condition + NN search
qb = box.query()
qb.nearest_neighbors_f32(vector_field_name, [4.1, 4.2], 6)
qb.contains_string("name", "red", case_sensitive=False)
query = qb.build()
# 4, 5, 3, 6, 2, 7
# Filtered: 3, 6, 7
search_results = query.find_with_scores()
assert len(search_results) == 3
assert search_results[0][0].name == "Red"
assert search_results[1][0].name == "Red apple"
assert search_results[2][0].name == "Hundred"
# Test offset/limit on find_with_scores (result is ordered by score desc)
query.offset(1)
query.limit(1)
search_results = query.find_with_scores()
assert len(search_results) == 1
assert search_results[0][0].name == "Red apple"
# Regular condition + NN search
qb = box.query()
qb.nearest_neighbors_f32(vector_field_name, [9.2, 8.9], 7)
qb.starts_with_string("name", "Blue", case_sensitive=True)
query = qb.build()
search_results = query.find_with_scores()
assert len(search_results) == 1
assert search_results[0][0].name == "Blue sea"
# Regular condition + NN search
qb = box.query()
qb.nearest_neighbors_f32(vector_field_name, [7.7, 7.7], 8)
qb.contains_string("name", "blue", case_sensitive=False)
query = qb.build()
# 8, 7, 9, 6, 5, 4, 3, 2
# Filtered: 9, 5, 4, 2
search_results = query.find_ids_with_scores()
assert len(search_results) == 4
assert search_results[0][0] == 9
assert search_results[1][0] == 5
assert search_results[2][0] == 4
assert search_results[3][0] == 2
search_results = query.find_ids_by_score()
assert len(search_results) == 4
assert search_results[0] == 9
assert search_results[1] == 5
assert search_results[2] == 4
assert search_results[3] == 2
search_results = query.find_ids_by_score_numpy()
assert search_results.size == 4
assert search_results[0] == 9
assert search_results[1] == 5
assert search_results[2] == 4
assert search_results[3] == 2
search_results = query.find_ids()
assert len(search_results) == 4
assert search_results[0] == 2
assert search_results[1] == 4
assert search_results[2] == 5
assert search_results[3] == 9
# Test offset/limit on find_ids (result is ordered by ID asc)
query.offset(1)
query.limit(2)
search_results = query.find_ids()
assert len(search_results) == 2
assert search_results[0] == 4
assert search_results[1] == 5
# Test empty result
query.offset(999)
assert len(query.find_ids()) == 0
assert len(query.find_ids_with_scores()) == 0
assert len(query.find_ids_by_score()) == 0
numpy_result = query.find_ids_by_score_numpy()
assert numpy_result.size == 0
assert str(numpy_result.dtype) == "uint64"
assert len(numpy_result) == 0
def test_combined_nn_search(test_store):
""" Tests NN search combined with regular query conditions, offset and limit. """
distance_type = VectorDistanceType.EUCLIDEAN
_test_combined_nn_search(test_store, distance_type)
# TODO: Cosine, DotProduct diverges see below
def test_vector_cache_hint_size_kb(tmp_path):
""" vector_cache_hint_size_kb is an int (KB), like in Java and Dart; other types raise right away (a float failed
with a ctypes.ArgumentError when the store was opened). """
@Entity(model="hnsw-cache-hint")
class Doc:
id = Id
vector = Float32Vector(index=HnswIndex(dimensions=2, vector_cache_hint_size_kb=1024))
model = Model()
model.entity(Doc)
with Store(model=model, directory=str(tmp_path / "db"), model_json_file=str(tmp_path / "m.json")) as store:
box = store.box(Doc)
box.put(Doc(vector=[1.0, 1.0]))
assert box.query(Doc.vector.nearest_neighbors([1.0, 1.0], 1)).build().count() == 1
for invalid in (1024.0, "1024", True):
with pytest.raises(TypeError, match="vector_cache_hint_size_kb must be an int"):
HnswIndex(dimensions=2, vector_cache_hint_size_kb=invalid)