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https://github.com/qdrant/qdrant-client.git
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Kunpeng XieandGeorge Panchuk
f2f8640679
* fix(local): isolate sparse vectors at storage boundaries * fix: replace deepcopy with a cheap copy --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import numpy as np
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from qdrant_client._pydantic_compat import construct
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from qdrant_client.http.models import SparseVector
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def empty_sparse_vector() -> SparseVector:
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return SparseVector(
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indices=[],
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values=[],
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)
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def copy_sparse_vector(vector: SparseVector) -> SparseVector:
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"""Return a sparse vector sharing no lists with `vector`.
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Two flat lists, so `deepcopy` is an order of magnitude slower for the same result, and
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this runs per sparse vector per point on every read.
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"""
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return construct(SparseVector, indices=list(vector.indices), values=list(vector.values))
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def validate_sparse_vector(vector: SparseVector) -> None:
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# these validate user input, so they must not be `assert`s: python -O strips those,
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# which would let a malformed vector into the collection
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if len(vector.indices) != len(vector.values):
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raise ValueError("Indices and values must have the same length")
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if np.isnan(vector.values).any():
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raise ValueError("Values must not contain NaN")
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if len(vector.indices) != len(set(vector.indices)):
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raise ValueError("Indices must be unique")
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def is_sorted(vector: SparseVector) -> bool:
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for i in range(1, len(vector.indices)):
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if vector.indices[i] < vector.indices[i - 1]:
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return False
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return True
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def sort_sparse_vector(vector: SparseVector) -> SparseVector:
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if is_sorted(vector):
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return vector
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sorted_indices = np.argsort(vector.indices)
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return SparseVector(
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indices=[vector.indices[i] for i in sorted_indices],
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values=[vector.values[i] for i in sorted_indices],
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)
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