Return docs most similar to embedding vector.
similarity_search_by_vector(
self,
embedding: list[float],
k: int = 4,
filter: models.Filter | None = None,
search_params: models.SearchParams | None = None,
offset: int = 0,
score_threshold: float | None = None,
consistency: models.ReadConsistency | None = None,
**kwargs: Any = {}
) -> list[Document]