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Return docs most similar to embedding vector.
similarity_search_by_vector( self, embedding: list[float], k: int = 4, filter: dict[str, Any] | None = None, **kwargs: Any = {} ) -> list[Document]
embedding
list[float]
Embedding to look up documents similar to.
k
int
4
Number of Documents to return. Defaults to 4.
filter
dict[str, Any] | None
None
Filter on the metadata to apply.
**kwargs
Any
{}
Additional arguments are ignored.
