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.
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
create_index([index_type])
date_to_range_filter(**kwargs)
delete([ids])
Delete by vector ID or other criteria.
delete_by_metadata(filter, **kwargs)
Delete by vector ID or other criteria.
drop_index()
drop_tables()
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct TimescaleVector wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ...])
Get intsance of an existing TimescaleVector store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_service_url(kwargs)
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.
service_url_from_db_params(host, port, ...)
Return connection string from database parameters.
similarity_search(query[, k, filter, predicates])
Run similarity search with TimescaleVector with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.