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https://github.com/hwchase17/langchain.git
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d01bad5169
This patch fixes the #18022 issue, converting the SimSIMD internal zero-copy outputs to NumPy. I've also noticed, that oftentimes `dtype=np.float32` conversion is used before passing to SimSIMD. Which numeric types do LangChain users generally care about? We support `float64`, `float32`, `float16`, and `int8` for cosine distances and `float16` seems reasonable for practically any kind of embeddings and any modern piece of hardware, so we can change that part as well 🤗
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
from enum import Enum
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from typing import List, Union
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import numpy as np
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Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
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class DistanceStrategy(str, Enum):
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"""Enumerator of the Distance strategies for calculating distances
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between vectors."""
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EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE"
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MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT"
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COSINE = "COSINE"
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def maximal_marginal_relevance(
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query_embedding: np.ndarray,
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embedding_list: list,
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lambda_mult: float = 0.5,
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k: int = 4,
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) -> List[int]:
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"""Calculate maximal marginal relevance."""
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if min(k, len(embedding_list)) <= 0:
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return []
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if query_embedding.ndim == 1:
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query_embedding = np.expand_dims(query_embedding, axis=0)
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similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
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most_similar = int(np.argmax(similarity_to_query))
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idxs = [most_similar]
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selected = np.array([embedding_list[most_similar]])
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while len(idxs) < min(k, len(embedding_list)):
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best_score = -np.inf
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idx_to_add = -1
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similarity_to_selected = cosine_similarity(embedding_list, selected)
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for i, query_score in enumerate(similarity_to_query):
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if i in idxs:
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continue
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redundant_score = max(similarity_to_selected[i])
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equation_score = (
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lambda_mult * query_score - (1 - lambda_mult) * redundant_score
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)
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if equation_score > best_score:
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best_score = equation_score
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idx_to_add = i
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idxs.append(idx_to_add)
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selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
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return idxs
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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 # type: ignore
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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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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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