mirror of
https://github.com/hwchase17/langchain
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b7b62e29fb
If you use an embedding dist function in an eval loop, you get warned every time. Would prefer to just check once and forget about it. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
"""Math utils."""
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import logging
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from typing import List, Optional, Tuple, Union
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import numpy as np
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logger = logging.getLogger(__name__)
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Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
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def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
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"""Row-wise cosine similarity between two equal-width matrices."""
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if len(X) == 0 or len(Y) == 0:
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return np.array([])
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X = np.array(X)
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Y = np.array(Y)
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if X.shape[1] != Y.shape[1]:
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raise ValueError(
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f"Number of columns in X and Y must be the same. X has shape {X.shape} "
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f"and Y has shape {Y.shape}."
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)
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try:
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import simsimd as simd
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X = np.array(X, dtype=np.float32)
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Y = np.array(Y, dtype=np.float32)
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Z = 1 - simd.cdist(X, Y, metric="cosine")
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if isinstance(Z, float):
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return np.array([Z])
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return np.array(Z)
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except ImportError:
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logger.debug(
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"Unable to import simsimd, defaulting to NumPy implementation. If you want "
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"to use simsimd please install with `pip install simsimd`."
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)
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X_norm = np.linalg.norm(X, axis=1)
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Y_norm = np.linalg.norm(Y, axis=1)
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# Ignore divide by zero errors run time warnings as those are handled below.
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with np.errstate(divide="ignore", invalid="ignore"):
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similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
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similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
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return similarity
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def cosine_similarity_top_k(
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X: Matrix,
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Y: Matrix,
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top_k: Optional[int] = 5,
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score_threshold: Optional[float] = None,
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) -> Tuple[List[Tuple[int, int]], List[float]]:
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"""Row-wise cosine similarity with optional top-k and score threshold filtering.
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Args:
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X: Matrix.
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Y: Matrix, same width as X.
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top_k: Max number of results to return.
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score_threshold: Minimum cosine similarity of results.
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Returns:
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Tuple of two lists. First contains two-tuples of indices (X_idx, Y_idx),
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second contains corresponding cosine similarities.
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"""
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if len(X) == 0 or len(Y) == 0:
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return [], []
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score_array = cosine_similarity(X, Y)
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score_threshold = score_threshold or -1.0
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score_array[score_array < score_threshold] = 0
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top_k = min(top_k or len(score_array), np.count_nonzero(score_array))
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top_k_idxs = np.argpartition(score_array, -top_k, axis=None)[-top_k:]
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top_k_idxs = top_k_idxs[np.argsort(score_array.ravel()[top_k_idxs])][::-1]
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ret_idxs = np.unravel_index(top_k_idxs, score_array.shape)
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scores = score_array.ravel()[top_k_idxs].tolist()
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return list(zip(*ret_idxs)), scores # type: ignore
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