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 the documents which have the specified ids.
from_documents(documents, embedding[, ...])
Return Vearch VectorStore
from_texts(texts, embedding[, metadatas, ...])
Return Vearch VectorStore
get([ids])
Return docs according ids.
load_local(embedding[, path_or_url, ...])
Load the local specified table of standalone vearch.
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
The most k similar documents and scores of the specified query.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
The most k similar documents and scores of the specified query.
__init__(embedding_function: Embeddings, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any) → None[source]¶
Initialize vearch vector store
flag 1 for cluster,0 for standalone