50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
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from typing import List, Optional, Tuple
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from chromadb.base_types import SparseVector
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def normalize_sparse_vector(
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indices: List[int],
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values: List[float],
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labels: Optional[List[str]] = None
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) -> SparseVector:
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"""Normalize and create a SparseVector by sorting indices and values together.
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This function takes raw indices and values (which may be unsorted or have duplicates)
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and returns a properly constructed SparseVector with sorted indices.
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Args:
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indices: List of dimension indices (may be unsorted)
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values: List of values corresponding to each index
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labels: Optional list of string labels corresponding to each index
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Returns:
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SparseVector with indices sorted in ascending order
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Raises:
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ValueError: If indices and values have different lengths
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ValueError: If there are duplicate indices (after sorting)
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ValueError: If indices are negative
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ValueError: If values are not numeric
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ValueError: If labels is provided and has different length than indices
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"""
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if not indices:
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return SparseVector(indices=[], values=[], labels=None)
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# Sort indices, values, and labels together by index
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if labels is not None:
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sorted_triples = sorted(zip(indices, values, labels), key=lambda x: x[0])
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sorted_indices, sorted_values, sorted_labels = zip(*sorted_triples)
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return SparseVector(
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indices=list(sorted_indices),
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values=list(sorted_values),
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labels=list(sorted_labels)
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)
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else:
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sorted_pairs = sorted(zip(indices, values), key=lambda x: x[0])
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sorted_indices, sorted_values = zip(*sorted_pairs)
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return SparseVector(
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indices=list(sorted_indices),
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values=list(sorted_values),
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labels=None
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)
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