Return docs most similar to embedding vector.
similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
partition: str = 'default',
**kwargs: Any = {}
) -> List[Document]| Name | Type | Description |
|---|---|---|
embedding* | List[float] | Embedding to look up documents similar to. |
k | int | Default: 4Number of Documents to return. Defaults to 4. |
filter | Optional[str] | Default: NoneDoc fields filter conditions that meet the SQL where clause specification. |
partition | str | Default: 'default'a partition name in collection. [optional]. |