Source code for langchain_community.vectorstores.utils

"""Utility functions for working with vectors and vectorstores."""

from enum import Enum
from typing import List, Tuple, Type

import numpy as np
from langchain_core.documents import Document

from langchain_community.utils.math import cosine_similarity


[docs]class DistanceStrategy(str, Enum): """Enumerator of the Distance strategies for calculating distances between vectors.""" EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE" MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT" DOT_PRODUCT = "DOT_PRODUCT" JACCARD = "JACCARD" COSINE = "COSINE"
[docs]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
[docs]def filter_complex_metadata( documents: List[Document], *, allowed_types: Tuple[Type, ...] = (str, bool, int, float), ) -> List[Document]: """Filter out metadata types that are not supported for a vector store.""" updated_documents = [] for document in documents: filtered_metadata = {} for key, value in document.metadata.items(): if not isinstance(value, allowed_types): continue filtered_metadata[key] = value document.metadata = filtered_metadata updated_documents.append(document) return updated_documents