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https://github.com/qdrant/qdrant-client.git
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e704e4fe02
* gen grpc * generate rest client * use float for formula evaluation, match multivalue behavior * support datetime expressions * populate inspection cache * review remarks * support datetime_key * fix: address AI comments * fix: regen inspection cache * regen inspection cache with pydantic 2.10 --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
import random
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import qdrant_client.models as models
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from tests.fixtures.filters import one_random_condition_please
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from tests.fixtures.payload import random_datetime
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def one_random_expression_please(current_depth: int = 0, max_depth: int = 5) -> models.Expression:
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"""Generate a random expression for testing purposes.
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Args:
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current_depth: Current depth of expression nesting
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max_depth: Maximum allowed depth for nested expressions
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Returns:
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A random Expression object
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"""
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def rand_decay_params():
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return models.DecayParamsExpression(
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# limit nesting to avoid precision errors
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x=one_random_expression_please(max_depth, max_depth),
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target=one_random_expression_please(max_depth, max_depth),
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midpoint=round(random.uniform(0.00001, 0.99999), 5),
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scale=round(random.uniform(0.000_000_1, 1_000_000.0), 5),
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)
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# Choose a random expression type with possible nesting
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expression_choices = [
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# Terminal expressions (no nesting) with higher probability at deeper levels
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lambda: round(random.uniform(-10.0, 10.0), 5), # Constant
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lambda: random.choice( # Variable
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[
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"rand_number",
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"rand_digit",
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"rand_signed_int",
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"nested.rand_digit",
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"$score",
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"$score[0]",
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"mixed_type",
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]
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),
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lambda: one_random_condition_please(), # Condition
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lambda: models.GeoDistance(
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geo_distance=models.GeoDistanceParams(
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origin=models.GeoPoint(
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lon=round(random.uniform(-180, 180), 5),
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lat=round(random.uniform(-90, 90), 5),
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),
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to="city.geo", # Using a field that would contain geo coordinates
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)
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),
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lambda: models.DatetimeExpression(datetime=str(random_datetime())),
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lambda: models.DatetimeKeyExpression(datetime_key="rand_datetime"),
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# Nested expressions
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lambda: models.MultExpression(
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mult=[
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one_random_expression_please(current_depth + 1, max_depth)
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for _ in range(random.randint(2, 3))
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]
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),
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lambda: models.SumExpression(
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sum=[
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one_random_expression_please(current_depth + 1, max_depth)
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for _ in range(random.randint(2, 3))
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]
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),
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lambda: models.NegExpression(
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neg=one_random_expression_please(current_depth + 1, max_depth)
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),
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lambda: models.AbsExpression(
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abs=one_random_expression_please(current_depth + 1, max_depth)
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),
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lambda: models.DivExpression(
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div=models.DivParams(
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left=one_random_expression_please(current_depth + 1, max_depth),
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right=one_random_expression_please(current_depth + 1, max_depth),
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by_zero_default=round(random.uniform(0, 1), 5) if random.random() < 0.5 else None,
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)
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),
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lambda: models.SqrtExpression(
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sqrt=one_random_expression_please(current_depth + 1, max_depth)
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),
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lambda: models.PowExpression(
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pow=models.PowParams(
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base=one_random_expression_please(current_depth + 1, max_depth),
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exponent=one_random_expression_please(current_depth + 1, max_depth),
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)
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),
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lambda: models.ExpExpression(
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exp=one_random_expression_please(current_depth + 1, max_depth)
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),
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lambda: models.Log10Expression(
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log10=one_random_expression_please(current_depth + 1, max_depth)
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),
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lambda: models.LnExpression(ln=one_random_expression_please(current_depth + 1, max_depth)),
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lambda: models.LinDecayExpression(lin_decay=rand_decay_params()),
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lambda: models.ExpDecayExpression(exp_decay=rand_decay_params()),
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lambda: models.GaussDecayExpression(gauss_decay=rand_decay_params()),
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]
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# If we've reached max depth, return a terminal expression (no nesting)
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if current_depth >= max_depth: # Limit nesting depth
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# Return a simple expression at max depth
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return random.choice(expression_choices[:6])()
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# Give higher weight to terminal expressions at deeper levels
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if current_depth > 2:
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# Add more terminal expressions to increase their probability
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expression_choices = expression_choices[:6] * 3 + expression_choices[6:]
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return random.choice(expression_choices)()
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