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fix: update poetry lock (#1242)

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* fix: update poetry lock

* fix: add type annotations, update poetry.lock

* fix: fix local persistence tests

* fix: replace del client with client.close in local mode persistence tests
This commit is contained in:
George
2026-06-26 11:41:15 +07:00
committed by GitHub
parent 3ce30426e0
commit fe0415384c
5 changed files with 1052 additions and 1154 deletions
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2131
poetry.lock generated
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4
qdrant_client/local/distances.py
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@@ -315,7 +315,7 @@ def calculate_discovery_scores(
# Get distances to target
distances_to_target = calculate_distance_core(query.target, vectors, distance_type)
sigmoided_distances = np.fromiter(
sigmoided_distances: types.NumpyArray = np.fromiter(
(scaled_fast_sigmoid(xi) for xi in distances_to_target), np.float32
)
@@ -332,7 +332,7 @@ def calculate_context_scores(
neg = calculate_distance_core(pair.negative, vectors, distance_type)
difference = pos - neg - EPSILON
pair_scores = np.fromiter(
pair_scores: types.NumpyArray = np.fromiter(
(fast_sigmoid(xi) for xi in np.minimum(difference, 0.0)), np.float32
)
overall_scores += pair_scores

4
qdrant_client/local/multi_distances.py
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@@ -191,7 +191,7 @@ def calculate_multi_discovery_scores(
# Get distances to target
distances_to_target = calculate_multi_distance_core(query.target, matrices, distance_type)
sigmoided_distances = np.fromiter(
sigmoided_distances: types.NumpyArray = np.fromiter(
(scaled_fast_sigmoid(xi) for xi in distances_to_target), np.float32
)
@@ -208,7 +208,7 @@ def calculate_multi_context_scores(
neg = calculate_multi_distance_core(pair.negative, matrices, distance_type)
difference = pos - neg - EPSILON
pair_scores = np.fromiter(
pair_scores: types.NumpyArray = np.fromiter(
(fast_sigmoid(xi) for xi in np.minimum(difference, 0.0)), np.float32
)
overall_scores += pair_scores

4
qdrant_client/local/sparse_distances.py
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@@ -178,7 +178,7 @@ def calculate_sparse_discovery_scores(
# Get distances to target
distances_to_target = calculate_distance_sparse(query.target, vectors, empty_is_zero=True)
sigmoided_distances = np.fromiter(
sigmoided_distances: types.NumpyArray = np.fromiter(
(scaled_fast_sigmoid(xi) for xi in distances_to_target), np.float32
)
@@ -195,7 +195,7 @@ def calculate_sparse_context_scores(
neg = calculate_distance_sparse(pair.negative, vectors, empty_is_zero=True)
difference = pos - neg - EPSILON
pair_scores = np.fromiter(
pair_scores: types.NumpyArray = np.fromiter(
(fast_sigmoid(xi) for xi in np.minimum(difference, 0.0)), np.float32
)
overall_scores += pair_scores

63
tests/test_local_persistence.py
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@@ -4,7 +4,7 @@ import tempfile
import numpy as np
import pytest
import qdrant_client
from qdrant_client import QdrantClient
import qdrant_client.http.models as rest
from qdrant_client._pydantic_compat import construct
from tests.fixtures.points import generate_random_sparse_vector_list
@@ -20,7 +20,7 @@ def ingest_dense_vector_data(
lines = [x for x in range(10)]
embeddings = np.random.randn(len(lines), vector_size).tolist()
client = qdrant_client.QdrantClient(path=path)
client = QdrantClient(path=path)
if client.collection_exists(collection_name):
client.delete_collection(collection_name)
@@ -40,6 +40,7 @@ def ingest_dense_vector_data(
vectors=embeddings,
),
)
return client
def ingest_sparse_vector_data(
@@ -50,7 +51,7 @@ def ingest_sparse_vector_data(
add_dense_to_config: bool = False,
):
sparse_vectors = generate_random_sparse_vector_list(vector_count, max_vector_size, 0.2)
client = qdrant_client.QdrantClient(path=path)
client = QdrantClient(path=path)
if client.collection_exists(collection_name):
client.delete_collection(collection_name)
@@ -80,32 +81,33 @@ def ingest_sparse_vector_data(
def test_prevent_parallel_access():
with tempfile.TemporaryDirectory() as tmpdir:
_client = qdrant_client.QdrantClient(path=tmpdir)
_client = QdrantClient(path=tmpdir)
with pytest.raises(Exception) as e:
_client2 = qdrant_client.QdrantClient(path=tmpdir)
_client2 = QdrantClient(path=tmpdir)
assert "already accessed by another instance" in str(e)
def test_local_dense_persistence():
with tempfile.TemporaryDirectory() as tmpdir:
ingest_dense_vector_data(path=tmpdir)
client = qdrant_client.QdrantClient(path=tmpdir)
client = ingest_dense_vector_data(path=tmpdir)
assert client.count(default_collection_name).count == 10
del client
client.close()
ingest_dense_vector_data(path=tmpdir)
client = qdrant_client.QdrantClient(path=tmpdir)
client = ingest_dense_vector_data(path=tmpdir)
assert client.count(default_collection_name).count == 10
del client
client.close()
ingest_dense_vector_data(path=tmpdir)
ingest_dense_vector_data(path=tmpdir, collection_name="example_2")
client = qdrant_client.QdrantClient(path=tmpdir)
client = ingest_dense_vector_data(path=tmpdir)
client.close()
client = ingest_dense_vector_data(path=tmpdir, collection_name="example_2")
assert client.count(default_collection_name).count == 10
assert client.count("example_2").count == 10
client.close()
@pytest.mark.parametrize("add_dense_to_config", [True, False])
def test_local_sparse_persistence(add_dense_to_config):
@@ -118,10 +120,9 @@ def test_local_sparse_persistence(add_dense_to_config):
limit=10,
with_vectors=True,
)
client.close()
del client
client = qdrant_client.QdrantClient(path=tmpdir)
client = QdrantClient(path=tmpdir)
(pre_result, _) = client.scroll(
collection_name=default_collection_name,
@@ -136,25 +137,24 @@ def test_local_sparse_persistence(add_dense_to_config):
assert len(pre_result[i].vector["text"].indices) == len(
pre_result[i].vector["text"].values
)
client.close()
del client
ingest_sparse_vector_data(path=tmpdir)
client = qdrant_client.QdrantClient(path=tmpdir)
client = ingest_sparse_vector_data(path=tmpdir)
assert client.count(default_collection_name).count == 10
del client
client.close()
ingest_sparse_vector_data(path=tmpdir)
ingest_sparse_vector_data(path=tmpdir, collection_name="example_2")
client = qdrant_client.QdrantClient(path=tmpdir)
client = ingest_sparse_vector_data(path=tmpdir)
client.close()
client = ingest_sparse_vector_data(path=tmpdir, collection_name="example_2")
assert client.count(default_collection_name).count == 10
assert client.count("example_2").count == 10
client.close()
def test_update_persisence():
collection_name = "update_persisence"
def test_update_persistence():
collection_name = "update_persistence"
with tempfile.TemporaryDirectory() as tmpdir:
client = qdrant_client.QdrantClient(path=tmpdir)
client = QdrantClient(path=tmpdir)
if client.collection_exists(collection_name):
client.delete_collection(collection_name)
@@ -187,10 +187,10 @@ def test_update_persisence():
"important": "meta information",
"not_important": "missing",
}
client.close()
del client
client = qdrant_client.QdrantClient(path=tmpdir)
client.close()
client = QdrantClient(path=tmpdir)
persisted_collection_info = client.get_collection(collection_name)
assert (
persisted_collection_info.config.params.sparse_vectors["text"].modifier
@@ -200,3 +200,4 @@ def test_update_persisence():
"important": "meta information",
"not_important": "missing",
}
client.close()