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https://github.com/qdrant/qdrant.git
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1de85b956e
* Use anonymous lifetime in `FromPyObject` implementations * Use `PyResult` in `IntoPyObject` implementations * Cleanup imports and derives * Cleanup `filter` conversions * Add `PointVectors` getters * Move `config` module into sub-directory * Split `config` into sub-modules * Simplify enum bindings * Add zero-cost conversions for `PyVectorDataConfig` and `PySparseVectorDataConfig` * Add getters to config structures * fixup! Add getters to config structures More zero-cost conversions for `PyVector*DataConfig` * Implement `PyHnswIndexConfig` * Implement `PyQuantizationConfig` * fixup! Simplify enum bindings * fixup! Implement `PyHnswIndexConfig` * fixup! Implement `PyHnswIndexConfig` * fixup! Implement `PyHnswIndexConfig` * Implement `PySparseVectorDataConfig` * fixup! Implement `PySparseVectorDataConfig` * fixup! Implement `PySparseVectorDataConfig`
51 lines
1.9 KiB
Python
51 lines
1.9 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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def load_new_shard():
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print("---- Load shard ----")
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DATA_DIRECTORY = os.path.join(os.path.dirname(__file__), "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.ChunkedMmap,
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index=PlainIndexConfig(),
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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.InRamMmap,
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)
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return Shard(DATA_DIRECTORY, config)
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def fill_dummy_data(shard: Shard):
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shard.update(UpdateOperation.upsert_points([
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Point(1, [0.05, 0.61, 0.76, 0.74], {"color": "red", "city": ["Moscow", "Berlin"]}),
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Point(2, [0.19, 0.81, 0.75, 0.11], {"color": "red", "city": "Mexico"}),
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Point(3, [0.36, 0.55, 0.47, 0.94], {"color": "blue", "city": ["Berlin", "Barcelona"]}),
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Point(4, [0.12, 0.34, 0.56, 0.78], {"color": "green", "city": "Lisbon", "rating": 4.5}),
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Point(5, [0.88, 0.12, 0.33, 0.44], {"color": "yellow", "city": ["Paris"], "active": True}),
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Point(6, [0.21, 0.22, 0.23, 0.24], {"color": "blue", "city": "Tokyo", "tags": ["night", "food"]}),
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Point(7, [0.99, 0.01, 0.50, 0.50], {"color": "red", "city": ["New York", "Boston"], "visits": 7}),
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Point(8, [0.10, 0.20, 0.30, 0.40], {"color": "blue", "city": "Seoul", "meta": {"source": "import"}}),
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Point(9, [0.45, 0.55, 0.65, 0.75], {"color": "green", "city": ["Berlin"], "score": 0.92}),
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Point(10, [0.01, 0.02, 0.03, 0.04], {"color": "yellow", "city": None, "featured": False}),
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]))
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