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 vector ID or other criteria.
delete_texts(ids)
Delete a list of docs from the Rockset collection
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Create Rockset wrapper with existing texts.
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, distance_func, ...])
Same as similarity_search_with_relevance_scores but doesn't return the scores.
similarity_search_by_vector(embedding[, k, ...])
Accepts a query_embedding (vector), and returns documents with similar embeddings.
similarity_search_by_vector_with_relevance_scores(...)
Accepts a query_embedding (vector), and returns documents with similar embeddings along with their relevance scores.
similarity_search_with_relevance_scores(query)
Perform a similarity search with Rockset
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')[source]¶
Initialize with Rockset client.
:param client: Rockset client object