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| from enum import Enum | |
| from typing import List, Union | |
| import numpy as np | |
| Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] | |
| class DistanceStrategy(str, Enum): | |
| """Enumerator of the Distance strategies for calculating distances | |
| between vectors.""" | |
| EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE" | |
| MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT" | |
| COSINE = "COSINE" | |
| def maximal_marginal_relevance( | |
| query_embedding: np.ndarray, | |
| embedding_list: list, | |
| lambda_mult: float = 0.5, | |
| k: int = 4, | |
| ) -> List[int]: | |
| """Calculate maximal marginal relevance.""" | |
| if min(k, len(embedding_list)) <= 0: | |
| return [] | |
| if query_embedding.ndim == 1: | |
| query_embedding = np.expand_dims(query_embedding, axis=0) | |
| similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] | |
| most_similar = int(np.argmax(similarity_to_query)) | |
| idxs = [most_similar] | |
| selected = np.array([embedding_list[most_similar]]) | |
| while len(idxs) < min(k, len(embedding_list)): | |
| best_score = -np.inf | |
| idx_to_add = -1 | |
| similarity_to_selected = cosine_similarity(embedding_list, selected) | |
| for i, query_score in enumerate(similarity_to_query): | |
| if i in idxs: | |
| continue | |
| redundant_score = max(similarity_to_selected[i]) | |
| equation_score = ( | |
| lambda_mult * query_score - (1 - lambda_mult) * redundant_score | |
| ) | |
| if equation_score > best_score: | |
| best_score = equation_score | |
| idx_to_add = i | |
| idxs.append(idx_to_add) | |
| selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) | |
| return idxs | |
| def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: | |
| """Row-wise cosine similarity between two equal-width matrices.""" | |
| if len(X) == 0 or len(Y) == 0: | |
| return np.array([]) | |
| X = np.array(X) | |
| Y = np.array(Y) | |
| if X.shape[1] != Y.shape[1]: | |
| raise ValueError( | |
| f"Number of columns in X and Y must be the same. X has shape {X.shape} " | |
| f"and Y has shape {Y.shape}." | |
| ) | |
| try: | |
| import simsimd as simd # type: ignore | |
| X = np.array(X, dtype=np.float32) | |
| Y = np.array(Y, dtype=np.float32) | |
| Z = 1 - np.array(simd.cdist(X, Y, metric="cosine")) | |
| return Z | |
| except ImportError: | |
| X_norm = np.linalg.norm(X, axis=1) | |
| Y_norm = np.linalg.norm(Y, axis=1) | |
| # Ignore divide by zero errors run time warnings as those are handled below. | |
| with np.errstate(divide="ignore", invalid="ignore"): | |
| similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) | |
| similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 | |
| return similarity | |