Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
Add list of text along with embeddings to the vector store
adelete([ids])
Delete by document ID asynchronously.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Create SurrealDBStore from list of text asynchronously
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
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])
Run similarity search on query asynchronously
asimilarity_search_by_vector(embedding[, k])
Run similarity search on query embedding asynchronously
asimilarity_search_with_relevance_scores(query)
Run similarity search asynchronously and return relevance scores
asimilarity_search_with_score(query[, k])
Run similarity search asynchronously and return distance scores
delete([ids])
Delete by document ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Create SurrealDBStore from list of text
initialize()
Initialize connection to surrealdb database and authenticate if credentials are provided
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.