Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
search_params: Optional[dict] = None,
**kwargs: Any = {}
) -> List[Document]Returns: List of Documents selected by maximal marginal relevance.
| Name | Type | Description |
|---|---|---|
embedding* | List[float] | Embedding to look up documents similar to. |
k | int | Default: 4Number of Documents to return. Defaults to 4. |
fetch_k | int | Default: 20Number of Documents to fetch to pass to MMR algorithm. |
lambda_mult | float | Default: 0.5Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. |