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qdrant/tests/openapi/test_query_formula.py
2025-04-24 20:57:20 +02:00

110 lines
3.4 KiB
Python

import pytest
from math import isclose
from .helpers.collection_setup import basic_collection_setup, drop_collection
from .helpers.helpers import request_with_validation
@pytest.fixture(autouse=True, scope="module")
def setup(on_disk_vectors, collection_name):
basic_collection_setup(
collection_name=collection_name, on_disk_vectors=on_disk_vectors
)
response = request_with_validation(
api="/collections/{collection_name}/index",
method="PUT",
path_params={"collection_name": collection_name},
query_params={"wait": 'true'},
body={"field_name": "price", "field_schema": "float"},
)
assert response.ok
yield
drop_collection(collection_name=collection_name)
@pytest.mark.parametrize(
"formula,expecting",
[
(
{"sum": [{"mult": ["$score", 0.4]}, {"mult": ["price", 0.6]}]},
lambda score, price: 0.4 * score + 0.6 * price,
),
(
{
"sum": [
"$score",
# fast sigmoid formula
{
"div": {
"left": "price",
"right": {"sum": [1.0, {"abs": "price"}]},
}
},
],
},
lambda score, price: score + (price / (1.0 + abs(price))),
),
],
)
def test_formula(collection_name, formula, expecting):
point_id = 8
# Get original scores
response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={"query": point_id},
)
points = response.json()["result"]["points"]
orig_scores = {point.get("id"): point.get("score") for point in points}
query = {
"prefetch": {"query": point_id},
"query": {"formula": formula, "defaults": {"price": 0.0}},
"with_payload": True,
}
# Formula query
response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body=query,
)
assert response.ok, response.json()
# Assert that the response is in descending order
points = response.json()["result"]["points"]
scores = [point.get("score") for point in points]
assert all(scores[i] >= scores[i + 1] for i in range(len(scores) - 1)), (
"Results should be ordered by score descending"
)
# Sanity check that the evaluation was correct
for point in points:
orig_score = orig_scores[point.get("id")]
price_value = point.get("payload").get("price")
if price_value is list:
price = price_value[0]
else:
price = price_value
if price is None:
price = 0.0
# Calculate expected score according to formula
expected_score = expecting(orig_score, price)
point_score = point.get("score")
# Compare with actual score within floating point precision
assert isclose(point_score, expected_score, rel_tol=1e-5), (
f"Expected score {expected_score}, got {point_score}. Point: {point}"
)
# Assert that the response contains all points
assert len(points) == len(orig_scores), "Response should contain all points"