Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
Returns
List of documents most similar to the query text.
Return type
List[Document]
class langchain.vectorstores.AwaDB(table_name: str = 'langchain_awadb', embedding_model: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None)[source]#
Interface implemented by AwaDB vector stores.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param kwargs: vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = 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 list of documents.
If a logging_and_data_dir specified, the table will be persisted there.
Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html