langchain.vectorstores.awadb.AwaDB¶
class langchain.vectorstores.awadb.AwaDB(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any)[source]¶
AwaDB vector store.
Initialize with AwaDB client.If table_name is not specified,
a random table name of _DEFAULT_TABLE_NAME + last segment of uuid
would be created automatically.
Parameters
table_name – Name of the table created, default _DEFAULT_TABLE_NAME.
embedding – Optional Embeddings initially set.
log_and_data_dir – Optional the root directory of log and data.
client – Optional AwaDB client.
kwargs – Any possible extend parameters in the future.
Returns
None.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([table_name, embedding, ...])
Initialize with AwaDB client.
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, is_duplicate_texts])
Run more texts through the embeddings and add to 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.