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https://github.com/qdrant/qdrant-client.git
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71d2cb1471
* new: add sparse vectors support in discovery and recommend in local mode * chore: remove redundant file * chore: remove another redundant file * fix: fix type hint for python3.8 * fix: remove redundant types from check * refactoring: remove redundant union * refactoring: remove redundant import * fix: fix type hint * fix: fix type hint * refactoring: improve exception description Co-authored-by: Luis Cossío <luis.cossio@outlook.com> * refactoring: improve exception description Co-authored-by: Luis Cossío <luis.cossio@outlook.com> * refactoring: address review comments --------- Co-authored-by: Luis Cossío <luis.cossio@outlook.com>
37 lines
1020 B
Python
37 lines
1020 B
Python
import numpy as np
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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 validate_sparse_vector(vector: SparseVector) -> None:
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assert len(vector.indices) == len(
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vector.values
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), "Indices and values must have the same length"
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assert not np.isnan(vector.values).any(), "Values must not contain NaN"
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assert len(vector.indices) == len(set(vector.indices)), "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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