langchain.vectorstores.vearch.Vearch¶
class langchain.vectorstores.vearch.Vearch(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)[source]¶
Initialize vearch vector store
flag 1 for cluster,0 for standalone
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function[, path_or_url, ...])
Initialize vearch vector store flag 1 for cluster,0 for standalone
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
returns
List of ids from adding the texts into the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
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])
Return docs most similar to query.