Create an Epsilla vectorstore from raw documents.
from_texts(
cls: Type[Epsilla],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Any = None,
db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PATH,
db_name: Optional[str] = _LANGCHAIN_DEFAULT_DB_NAME,
collection_name: Optional[str] = _LANGCHAIN_DEFAULT_TABLE_NAME,
drop_old: Optional[bool] = False,
**kwargs: Any = {}
) -> Epsilla| Name | Type | Description |
|---|---|---|
texts* | List[str] | List of text data to be inserted. |
embeddings* | Embeddings | Embedding function. |
client | pyepsilla.vectordb.Client | Default: NoneEpsilla client to connect to. |
metadatas | Optional[List[dict]] | Default: NoneMetadata for each text. Defaults to None. |
db_path | Optional[str] | Default: _LANGCHAIN_DEFAULT_DB_PATHThe path where the database will be persisted. Defaults to "/tmp/langchain-epsilla". |
db_name | Optional[str] | Default: _LANGCHAIN_DEFAULT_DB_NAMEGive a name to the loaded database. Defaults to "langchain_store". |
collection_name | Optional[str] | Default: _LANGCHAIN_DEFAULT_TABLE_NAMEWhich collection to use. Defaults to "langchain_collection". If provided, default collection name will be set as well. |
drop_old | Optional[bool] | Default: FalseWhether to drop the previous collection and create a new one. Defaults to False. |