mirror of
https://github.com/qdrant/qdrant.git
synced 2026-09-30 01:47:56 -05:00
Andrey Vasnetsov
51b3a62977
* refactor: move RecordInternal into Shard crate * refactor: move retrieve_blocking into Shard crate * implement retrieve method + move some structures into dedicated files
75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
import os
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import shutil
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from qdrant_edge import *
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config = SegmentConfig(
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vector_data={
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"": VectorDataConfig(
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size=4,
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distance=Distance.COSINE,
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storage_type=VectorStorageType.CHUNKED_MMAP,
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index=Indexes.PLAIN,
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quantization_config=None,
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multivector_config=None,
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datatype=None,
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),
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},
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sparse_vector_data={},
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payload_storage_type=PayloadStorageType.IN_RAM_MMAP,
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)
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DATA_DIRECTORY = "./data"
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# Clear and recreate data directory
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if os.path.exists(DATA_DIRECTORY):
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shutil.rmtree(DATA_DIRECTORY)
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os.makedirs(DATA_DIRECTORY)
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shard = Shard(DATA_DIRECTORY, config)
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shard.update(UpdateOperation.upsert_points([
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Point(
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PointId.num(1),
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Vector.single([6.0, 9.0, 4.0, 2.0]),
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Payload({
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"null": None,
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"str": "string",
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"uint": 42,
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"int": -69,
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"float": 4.20,
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"bool": True,
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"obj": {
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"null": None,
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"str": "string",
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"uint": 42,
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"int": -69,
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"float": 4.20,
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"bool": True,
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"obj": {},
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"arr": [],
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},
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"arr": [None, "string", 42, -69, 4.20, True, {}, []],
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}),
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),
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]))
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points = shard.search(SearchRequest(
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query=Query.nearest(QueryVector.dense([1.0, 1.0, 1.0, 1.0]), None),
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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_vector=WithVector(True),
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with_payload=WithPayload(True),
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score_threshold=None,
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))
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for point in points:
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print(f"Point: {point.id}, vector: {point.vector}, payload: {point.payload}, score: {point.score}")
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retrieve = shard.retrieve(ids=[PointId.num(1)], with_vector=WithVector(True), with_payload=WithPayload(True))
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for point in retrieve:
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print(f"Point: {point.id}, vector: {point.vector}, payload: {point.payload}")
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