similarity_search_by_vector([embedding, k, ...])
Return docs most similar to embedding vector.
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.
update(ids, texts[, metadatas])
Update the documents which have the specified ids.
use(table_name, **kwargs)
Use the specified table.
__init__(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any) → None[source]¶
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.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.