mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-08-04 09:01:05 -05:00
c92a0432f3
* deprecated recreate_collection from tests * checked if collection exists before deletion * reverted deletion of recreate_collection * added missing collection_exsits before deletion * nit if condition * fix type hint in local_collection * removed run mypy on async client generator * added checks for create_collection if exists * nit change collection name * add else to create_collection * nit * nit * refactoring: remove redundant checks * fix: do not test collection_exists through itself * refactoring: remove redundant collection exists checks * fix: remove redundant collection exists checks * refactoring: remove redundant collection checks * refactoring: remove redundant collection existence check in test sparse search * refactoring: remove redundant checks in test_updates * refactoring: remove redundant checks * fix: remove redundant checks * refactoring: remove redundant checks * refactoring: remove redundant imports * new: add recreate collection test --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
import pytest
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from qdrant_client import QdrantClient, models
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@pytest.fixture
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def qdrant() -> QdrantClient:
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return QdrantClient(":memory:")
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def test_dense_in_memory_key_filter_returns_results(qdrant: QdrantClient):
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qdrant.create_collection(
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collection_name="test_collection",
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vectors_config=models.VectorParams(size=4, distance=models.Distance.DOT),
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)
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operation_info = qdrant.upsert(
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collection_name="test_collection",
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wait=True,
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points=[
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models.PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={"city": "Berlin"}),
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models.PointStruct(
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id=2,
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vector=[0.19, 0.81, 0.75, 0.11],
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payload={"city": ["Berlin", "London"]},
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),
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models.PointStruct(
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id=3,
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vector=[0.36, 0.55, 0.47, 0.94],
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payload={"city": ["Berlin", "Moscow"]},
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),
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models.PointStruct(
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id=4,
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vector=[0.18, 0.01, 0.85, 0.80],
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payload={"city": ["London", "Moscow"]},
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),
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models.PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"count": [0]}),
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models.PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44]),
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],
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)
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assert operation_info.operation_id == 0
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assert operation_info.status == models.UpdateStatus.COMPLETED
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search_result = qdrant.search(
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collection_name="test_collection",
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query_vector=[0.2, 0.1, 0.9, 0.7],
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query_filter=models.Filter(
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must=[models.FieldCondition(key="city", match=models.MatchValue(value="London"))]
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),
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limit=3,
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)
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assert [r.id for r in search_result] == [4, 2]
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def test_sparse_in_memory_key_filter_returns_results(qdrant: QdrantClient):
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qdrant.create_collection(
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collection_name="test_collection",
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vectors_config={},
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sparse_vectors_config={"text": models.SparseVectorParams()},
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)
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operation_info = qdrant.upsert(
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collection_name="test_collection",
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wait=True,
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points=[
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models.PointStruct(
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id=1,
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vector={
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"text": models.SparseVector(
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indices=[0, 1, 2, 3], values=[0.05, 0.61, 0.76, 0.74]
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)
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},
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payload={"city": "Berlin"},
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),
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models.PointStruct(
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id=2,
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vector={
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"text": models.SparseVector(
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indices=[0, 1, 2, 3], values=[0.19, 0.81, 0.75, 0.11]
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)
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},
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payload={"city": ["Berlin", "London"]},
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),
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models.PointStruct(
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id=3,
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vector={
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"text": models.SparseVector(
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indices=[0, 1, 2, 3], values=[0.36, 0.55, 0.47, 0.94]
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)
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},
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payload={"city": ["Berlin", "Moscow"]},
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),
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models.PointStruct(
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id=4,
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vector={
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"text": models.SparseVector(
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indices=[0, 1, 2, 3], values=[0.18, 0.01, 0.85, 0.80]
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)
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},
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payload={"city": ["London", "Moscow"]},
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),
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],
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)
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assert operation_info.operation_id == 0
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assert operation_info.status == models.UpdateStatus.COMPLETED
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search_result = qdrant.search(
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collection_name="test_collection",
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query_vector=models.NamedSparseVector(
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name="text",
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vector=models.SparseVector(indices=[0, 1, 2, 3], values=[0.2, 0.1, 0.9, 0.7]),
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),
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query_filter=models.Filter(
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must=[models.FieldCondition(key="city", match=models.MatchValue(value="London"))]
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),
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limit=3,
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)
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assert [r.id for r in search_result] == [4, 2]
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