mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-07-29 22:21:10 -05:00
458646cb95
* fix: do not try to check compatibility in test_client_init * tests: remove sparse-code vectors (10_000 dim), remove euclid recommend methods as non-applicable for sparse * tests: add payload indexes to query group test * debug: add durations=0 to pytest * debug: test only test query group * debug: try adding payload indexes to query group test * rollback test launch
144 lines
4.2 KiB
Python
144 lines
4.2 KiB
Python
import random
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from qdrant_client.http.models import PayloadSelectorExclude, PayloadSelectorInclude
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from tests.congruence_tests.test_common import (
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COLLECTION_NAME,
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compare_client_results,
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generate_fixtures,
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generate_sparse_fixtures,
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init_client,
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init_local,
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init_remote,
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sparse_vectors_config,
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)
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def test_retrieve(local_client, remote_client) -> None:
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num_vectors = 1000
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fixture_points = generate_fixtures(num_vectors)
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keys = list(fixture_points[0].payload.keys())
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local_client.upload_points(COLLECTION_NAME, fixture_points)
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remote_client.upload_points(COLLECTION_NAME, fixture_points, wait=True)
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id_ = random.randint(0, num_vectors)
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compare_client_results(
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local_client, remote_client, lambda c: c.retrieve(COLLECTION_NAME, [id_])
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_payload=False),
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)
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# with_vectors is not tested with `True` because `text` vectors are used with Cosine distance,
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# and we do not normalize them in local version
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_vectors=["image", "code"]),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME, [id_], with_vectors=["image", "code"], with_payload=False
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),
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)
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sample_keys = random.sample(keys, 3)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_payload=sample_keys),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME,
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[id_],
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with_payload=PayloadSelectorInclude(include=sample_keys),
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),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME,
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[id_],
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with_payload=PayloadSelectorExclude(exclude=sample_keys),
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),
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)
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def test_sparse_retrieve() -> None:
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num_vectors = 1000
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fixture_points = generate_sparse_fixtures(num_vectors)
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local_client = init_local()
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init_client(local_client, fixture_points, sparse_vectors_config=sparse_vectors_config)
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remote_client = init_remote()
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init_client(remote_client, fixture_points, sparse_vectors_config=sparse_vectors_config)
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keys = list(fixture_points[0].payload.keys())
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local_client.upload_points(COLLECTION_NAME, fixture_points)
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remote_client.upload_points(COLLECTION_NAME, fixture_points, wait=True)
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id_ = random.randint(0, num_vectors)
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compare_client_results(
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local_client, remote_client, lambda c: c.retrieve(COLLECTION_NAME, [id_])
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_payload=False),
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)
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# with_vectors is not tested with `True` because `text` vectors are used with Cosine distance,
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# and we do not normalize them in local version
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_vectors=["sparse-image", "sparse-text"]),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME,
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[id_],
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with_vectors=["sparse-image", "sparse-text"],
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with_payload=False,
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),
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)
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sample_keys = random.sample(keys, 3)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(COLLECTION_NAME, [id_], with_payload=sample_keys),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME,
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[id_],
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with_payload=PayloadSelectorInclude(include=sample_keys),
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),
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)
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compare_client_results(
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local_client,
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remote_client,
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lambda c: c.retrieve(
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COLLECTION_NAME,
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[id_],
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with_payload=PayloadSelectorExclude(exclude=sample_keys),
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),
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)
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