**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.
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, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, table_name: str = 'test', database_name: str = 'default', connection_args: Dict[str, Any] = {'host': 'localhost', 'password': 'admin', 'port': '7788', 'username': 'admin'}, index_params: Optional[Dict[Any, Any]] = None, search_params: Optional[Dict[str, Any]] = None, drop_old: bool = False, **kwargs: Any) → Hippo[source]¶
Creates an instance of the VST class from the given texts.
Parameters
texts (List[str]) – List of texts to be added.
embedding (Embeddings) – Embedding model for the texts.
metadatas (List[dict], optional) – 
None. (List of metadata dictionaries for each text.Defaults to) –