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 and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
consolidate_updates(**kwargs)
create(index_uri, index_type, dimensions, ...)
delete([ids, timestamp])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding, ...)
Construct TileDB index from embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct a TileDB index from raw documents.
load(index_uri, embedding, *[, metric, ...])
Load a TileDB index from a URI.
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.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs and their similarity scores selected using the maximal marginal
process_index_results(ids, scores, *[, k, ...])
Turns TileDB results into a list of documents and scores.
search(query, search_type, **kwargs)