**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
clear_data(collection_name: str = '') → None[source]¶
Clear data in a collection.
Parameters
collection_name (Optional[str]) – The name of the collection.
If not provided, the default collection will be used.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, client: Any = None, db_path: Optional[str] = '/tmp/langchain-epsilla', db_name: Optional[str] = 'langchain_store', collection_name: Optional[str] = 'langchain_collection', drop_old: Optional[bool] = False, **kwargs: Any) → Epsilla[source]¶
Create an Epsilla vectorstore from a list of documents.
Parameters
texts (List[str]) – List of text data to be inserted.
embeddings (Embeddings) – Embedding function.
client (pyepsilla.vectordb.Client) – Epsilla client to connect to.
metadatas (Optional[List[dict]]) – Metadata for each text.
Defaults to None.