mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-08-03 08:30:58 -05:00
329b4150cf
* wip: interface for new APIs * upd docker version * remove duplicated comments + extent interface * start implementing remote client + remove duplicated comments there * implement remote methods * quantization converter * extend coverege * wip: saving and loading of optional vectors * update and delete vectors test * rm unused imports * nested filters * fix mypy * fix pyright * wip: add search groups and recommend groups (#174) * wip: add search groups and recommend groups * fix: fix signature * fix: fix mypy * simplify group-by and condition checks * fix tests --------- Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com> * add match except condition + improve group-by tests * fix: lock typing exchanges due to broken release * tests: add maybe and maybe_null checks to group tests * new: add new methods to type stubs --------- Co-authored-by: George <george.panchuk@qdrant.tech>
86 lines
2.0 KiB
Python
86 lines
2.0 KiB
Python
import numpy as np
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from qdrant_client.http.models import models
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from tests.congruence_tests.test_common import (
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COLLECTION_NAME,
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NUM_VECTORS,
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compare_client_results,
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generate_fixtures,
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image_vector_size,
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init_client,
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init_local,
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init_remote,
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)
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def test_simple_opt_vectors_search():
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fixture_records = generate_fixtures()
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local_client = init_local()
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init_client(local_client, fixture_records)
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remote_client = init_remote()
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init_client(remote_client, fixture_records)
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ids_to_delete = [x for x in range(NUM_VECTORS) if x % 5 == 0]
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vectors_to_retrieve = [x for x in range(20)]
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local_client.delete_vectors(
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collection_name=COLLECTION_NAME,
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vectors=["image"],
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points=ids_to_delete,
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)
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remote_client.delete_vectors(
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collection_name=COLLECTION_NAME,
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vectors=["image"],
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points=ids_to_delete,
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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: sorted(
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c.retrieve(
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COLLECTION_NAME,
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vectors_to_retrieve,
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with_payload=False,
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with_vectors=["image", "code"],
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),
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key=lambda x: x.id,
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),
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)
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new_vector = np.random.rand(image_vector_size).tolist()
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update_vectors = [
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models.PointVectors(
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id=i,
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vector={"image": new_vector},
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)
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for i in range(6)
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]
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local_client.update_vectors(
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collection_name=COLLECTION_NAME,
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vectors=update_vectors,
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)
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remote_client.update_vectors(
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collection_name=COLLECTION_NAME,
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vectors=update_vectors,
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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: sorted(
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c.retrieve(
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COLLECTION_NAME,
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vectors_to_retrieve,
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with_payload=False,
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with_vectors=["image", "code"],
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),
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key=lambda x: x.id,
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),
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)
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