Return docs most similar to query.
delete([ids])
Delete a Redis entry.
drop_index(index_name, delete_documents, ...)
Drop a Redis search index.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding, index_name, ...)
Connect to an existing Redis index.
from_texts(texts, embedding[, metadatas, ...])
Create a Redis vectorstore from a list of texts.
from_texts_return_keys(texts, embedding[, ...])
Create a Redis vectorstore from raw documents.
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, filter, ...])
Run similarity search
similarity_search_by_vector(embedding[, k, ...])
Run similarity search between a query vector and the indexed vectors.
similarity_search_limit_score(query[, k, ...])
[Deprecated]  Returns the most similar indexed documents to the query text within the score_threshold range.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with vector distance.
write_schema(path)
Write the schema to a yaml file.