mirror of
https://github.com/qdrant/qdrant.git
synced 2026-07-28 13:41:08 -05:00
56a9610e32
* initial filter conversion structure * implement match conditions * implement range condition * implement filter condition * implement geo conditions * fmt * finish field conditions * implement more conditions * implement nested + expose python exports * fix spelling
147 lines
3.2 KiB
Python
147 lines
3.2 KiB
Python
import os
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import shutil
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import uuid
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from qdrant_edge import *
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print("---- Load shard ----")
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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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# Load Qdrant Edge shard
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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.DOT,
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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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shard = Shard(DATA_DIRECTORY, config)
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print("---- Upsert ----")
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shard.update(UpdateOperation.upsert_points([
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Point(
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1,
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[6.0, 9.0, 4.0, 2.0],
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{
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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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Point(
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"e9408f2b-b917-4af1-ab75-d97ac6b2c047",
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[6.0, 9.0, 3.0, -2.0],
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{
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"hello": "world",
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"price": 199.99,
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},
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),
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Point(
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uuid.uuid4(),
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[1.0, 6.0, 4.0, 2.0],
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{
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"hello": "world",
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"price": 999.99,
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},
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),
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]))
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print("---- Some other points ----")
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some_other_points = [
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Point(10, [[1,2,3], [3, 4, 5]], {}),
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Point(11, {
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"sparse": SparseVector(indices=[0, 2], values=[1.0, 3.0])
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}, {}),
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]
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# Test points conversion into internal representation and back
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for point in some_other_points:
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print(f"Point: {point.id}, vector: {point.vector}, payload: {point.payload}")
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print("---- Search ----")
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points = shard.search(SearchRequest(
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query=[1.0, 1.0, 1.0, 1.0],
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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=True,
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with_payload=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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print("---- Search Filter ----")
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search_filter = Filter(
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must=[
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FieldCondition(
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key="hello",
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match=MatchTextAny(text_any="world"),
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),
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FieldCondition(
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key="price",
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range=RangeFloat(gte=500.0),
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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=[1.0, 1.0, 1.0, 1.0],
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filter=search_filter,
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params=None,
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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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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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print("---- Retrieve ----")
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points = shard.retrieve(point_ids=[1], with_vector=True, with_payload=True)
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for point in points:
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print(f"Point: {point.id}, vector: {point.vector}, payload: {point.payload}")
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