Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete([ids])
Delete by IDs.
delete_cluster()
Delete the cluster.
from_documents(documents[, embedding, ids, ...])
Create a Bagel vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create and initialize a Bagel instance from list of texts.
get([ids, where, limit, offset, ...])
Gets the collection.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, where])
Run a similarity search with BagelDB.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return docs most similar to embedding vector and similarity score.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, where])