mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-08-03 00:20:58 -05:00
303de4423e
* fix: fix mypy and pyright * fix: fix nested key case, add tests * fix: fix mypy * fix: fix mypy again * new: extend jsonpath support, update set and get value by key in local mode * fix: uncomment tests, fix set payload call, fix corner case * refactor: split payload_value extractor into several files * docs: update docstring * refactor: refactor local mode payload setter (#544) * refactor: refactor local mode payload setter * fix: fix mypy * fix: return docstring * fix: address review issues * fix: fix extraction from by invalid keys * fix: move payload tests to a separate folder to be recognized by pytest * tests: add nested array filters * review fixes * fix: fix async * fix: set payload by key handle escaped quotes --------- Co-authored-by: generall <andrey@vasnetsov.com>
283 lines
7.8 KiB
Python
283 lines
7.8 KiB
Python
import numpy as np
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from qdrant_client import QdrantClient
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from qdrant_client import models as rest_models
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from qdrant_client.conversions import common_types as types
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from qdrant_client.models import (
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CompressionRatio,
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ProductQuantizationConfig,
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ScalarQuantizationConfig,
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ScalarType,
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)
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qdrant_client = QdrantClient(timeout=30)
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qdrant_client.clear_payload("collection", [123])
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qdrant_client.count("collection", rest_models.Filter())
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qdrant_client.close(grpc_grace=1.5)
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qdrant_client.create_full_snapshot()
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qdrant_client.create_payload_index("collection", "asd", 3)
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qdrant_client.delete("collection", [123])
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qdrant_client.delete_collection("collection")
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qdrant_client.delete_payload("collection", ["key"], [1])
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qdrant_client.delete_payload_index("collection", "field_name")
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qdrant_client.delete_snapshot("collection", "sn_name")
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qdrant_client.delete_full_snapshot("collection")
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qdrant_client.get_collection_aliases("collection")
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qdrant_client.get_aliases()
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qdrant_client.get_collection("collection")
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qdrant_client.collection_exists("collection")
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qdrant_client.get_collections()
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qdrant_client.get_locks()
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qdrant_client.list_full_snapshots()
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qdrant_client.list_snapshots("collection")
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qdrant_client.lock_storage("reason")
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qdrant_client.overwrite_payload("collection", {}, [])
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qdrant_client.recommend(
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"collection",
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[],
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[],
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rest_models.Filter(),
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rest_models.SearchParams(),
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10,
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0,
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True,
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True,
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1.0,
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"using",
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rest_models.LookupLocation(collection=""),
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rest_models.RecommendStrategy.AVERAGE_VECTOR,
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1,
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)
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qdrant_client.recommend_batch(
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"collection",
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[
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rest_models.RecommendRequest(
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positive=[],
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negative=[],
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filter=None,
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params=None,
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limit=10,
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offset=0,
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with_payload=True,
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with_vector=True,
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score_threshold=0.5,
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using=None,
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lookup_from=None,
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)
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],
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)
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qdrant_client.discover(
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"collection",
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None,
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[],
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rest_models.Filter(),
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rest_models.SearchParams(),
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10,
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0,
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True,
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True,
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"using",
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rest_models.LookupLocation(collection=""),
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1,
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)
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qdrant_client.discover_batch(
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"collection",
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[
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rest_models.DiscoverRequest(
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target=None,
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context=[],
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filter=rest_models.Filter(),
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params=rest_models.SearchParams(),
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limit=10,
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offset=0,
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with_vector=True,
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with_payload=True,
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using="using",
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lookup_from=rest_models.LookupLocation(collection=""),
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),
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],
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)
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qdrant_client.recover_snapshot("collection", "location", rest_models.SnapshotPriority.REPLICA)
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qdrant_client.create_collection(
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"collection",
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types.VectorParams(size=128, distance=rest_models.Distance.COSINE),
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{
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"field": rest_models.SparseVectorParams(
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index=rest_models.SparseIndexParams(
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full_scan_threshold=1000,
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on_disk=False,
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)
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)
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},
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2,
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rest_models.ShardingMethod.AUTO,
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2,
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True,
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True,
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rest_models.HnswConfigDiff(),
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rest_models.OptimizersConfigDiff(),
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rest_models.WalConfigDiff(),
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rest_models.ScalarQuantization(scalar=ScalarQuantizationConfig(type=ScalarType.INT8)),
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None,
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5,
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)
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qdrant_client.recreate_collection(
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"collection",
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types.VectorParams(size=128, distance=rest_models.Distance.COSINE),
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{
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"field": rest_models.SparseVectorParams(
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index=rest_models.SparseIndexParams(
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full_scan_threshold=1000,
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on_disk=False,
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)
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)
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},
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2,
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rest_models.ShardingMethod.AUTO,
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2,
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True,
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True,
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rest_models.HnswConfigDiff(),
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rest_models.OptimizersConfigDiff(),
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rest_models.WalConfigDiff(),
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rest_models.ScalarQuantization(scalar=ScalarQuantizationConfig(type=ScalarType.INT8)),
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None,
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5,
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)
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qdrant_client.recreate_collection(
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"collection",
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types.VectorParams(size=128, distance=rest_models.Distance.COSINE),
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{
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"field": rest_models.SparseVectorParams(
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index=rest_models.SparseIndexParams(
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full_scan_threshold=1000,
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on_disk=False,
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)
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)
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},
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2,
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rest_models.ShardingMethod.AUTO,
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2,
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True,
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True,
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rest_models.HnswConfigDiff(),
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rest_models.OptimizersConfigDiff(),
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rest_models.WalConfigDiff(),
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rest_models.ProductQuantization(
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product=ProductQuantizationConfig(compression=CompressionRatio.X32)
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),
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None,
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5,
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)
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qdrant_client.retrieve("collection", [])
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qdrant_client.scroll("collection")
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qdrant_client.search_batch(
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"collection",
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[
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rest_models.SearchRequest(
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vector=[1.0, 0.0, 3.0],
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limit=10,
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)
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],
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)
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qdrant_client.set_payload("collection", {}, [], key=None, wait=True)
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qdrant_client.unlock_storage()
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qdrant_client.update_collection(
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"collection",
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rest_models.OptimizersConfigDiff(
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deleted_threshold=0.5,
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vacuum_min_vector_number=1000,
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default_segment_number=3,
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max_segment_size=2,
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memmap_threshold=3,
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indexing_threshold=5,
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flush_interval_sec=3000,
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max_optimization_threads=1,
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),
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)
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qdrant_client.update_collection_aliases(
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[
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rest_models.CreateAliasOperation(
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create_alias=rest_models.CreateAlias(collection_name="heh", alias_name="hah"),
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)
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]
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)
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qdrant_client.upload_records("collection", [])
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qdrant_client.upload_points("collection", [])
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qdrant_client.upsert("collection", [])
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qdrant_client.search("collection", [123], with_payload=["str", "another one", "and another one"])
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# pyright currently is not happy with np.array and treating it as a "partially unknown type"
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qdrant_client.search(
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"collection",
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np.array([123]), # type: ignore
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with_payload=["str", "another one", "and another one"],
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)
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qdrant_client.upload_collection("collection", [[123]])
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qdrant_client.update_vectors("collection", [rest_models.PointVectors(id=1, vector=[123])], False)
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qdrant_client.delete_vectors("collection", [], [123, 32, 44])
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qdrant_client.search_groups(
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"collection",
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[123],
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"rand_field",
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rest_models.Filter(
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must=[rest_models.FieldCondition(key="field", match=rest_models.MatchValue(value="123"))]
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),
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rest_models.SearchParams(hnsw_ef=182),
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2,
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3,
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True,
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True,
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0.2,
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)
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qdrant_client.recommend_groups(
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"collection",
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"rand_field",
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[14],
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[],
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rest_models.Filter(
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must=[rest_models.FieldCondition(key="field", match=rest_models.MatchValue(value="123"))]
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),
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rest_models.SearchParams(hnsw_ef=182),
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2,
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3,
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3.0,
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True,
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True,
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"using",
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rest_models.LookupLocation(collection="start"),
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None,
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)
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qdrant_client.batch_update_points(
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collection_name="batchcollection",
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update_operations=[
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rest_models.UpsertOperation(
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upsert=rest_models.PointsList(
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points=[rest_models.PointStruct(vector=[0.1, 0.2], id=3, payload={})]
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)
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)
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],
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)
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qdrant_client.create_snapshot(collection_name="createsnapshot", wait=False)
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qdrant_client.list_shard_snapshots(collection_name="listshardsnapshots", shard_id=3)
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qdrant_client.create_shard_snapshot(collection_name="createshardsnapshot", shard_id=3)
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qdrant_client.delete_shard_snapshot(
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collection_name="deleteshardsnapshot", shard_id=3, snapshot_name="snapshot_id", wait=False
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)
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qdrant_client.recover_shard_snapshot(
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collection_name="recovershardsnapshot",
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shard_id=3,
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location="nowhere",
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priority=rest_models.SnapshotPriority.NO_SYNC,
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)
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qdrant_client.create_shard_key(
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collection_name="qwerty",
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shard_key="new_key",
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shards_number=3,
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replication_factor=2,
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placement=[23],
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)
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qdrant_client.delete_shard_key(collection_name="zcxzc", shard_key="broken_key")
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