Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
table_name (str) – Name of the collection to create.
logging_and_data_dir (Optional[str]) – Directory to persist the table.
client (Optional[awadb.Client]) – AwaDB client
Returns
AwaDB vectorstore.
Return type
AwaDB
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, table_name: str = 'langchain_awadb', logging_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any) → AwaDB[source]#
Create an AwaDB vectorstore from a raw documents.
Parameters
texts (List[str]) – List of texts to add to the table.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
table_name (str) – Name of the table to create.
logging_and_data_dir (Optional[str]) – Directory of logging and persistence.
client (Optional[awadb.Client]) – AwaDB client
Returns
AwaDB vectorstore.
Return type
AwaDB
load_local(table_name: str = 'langchain_awadb', **kwargs: Any) → bool[source]#
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html