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.
create_inverse_metadata(fields)
Create metadata from fields.
create_results(json_result)
Assemble documents.
create_results_with_score(json_result)
Parsing the returned results with scores.
delete([ids])
Delete by vector ID or other criteria.
delete_documents_with_document_id(id_list)
Delete documents based on their IDs.
delete_documents_with_texts(texts)
Delete documents based on their page content.
from_documents(documents, embedding[, config])
Create alibaba cloud opensearch vector store instance.
from_texts(texts, embedding[, metadatas, config])
Create alibaba cloud opensearch vector store instance.
inner_embedding_query(embedding[, ...])
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, search_filter])
Perform similarity retrieval based on text.
similarity_search_by_vector(embedding[, k, ...])
Perform retrieval directly using vectors.
similarity_search_with_relevance_scores(query)
Perform similarity retrieval based on text with scores.
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.