Accepts a query_embedding (vector), and returns documents with similar embeddings.
similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
distance_func: DistanceFunction = DistanceFunction.COSINE_SIM,
where_str: Optional[str] = None,
**kwargs: Any = {}
) -> List[Document]