Method●Since v0.0

amax_marginal_relevance_search_by_vector

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

amax_marginal_relevance_search_by_vector(
  self,
  embedding: list[float],
  k: int = 4,
  fetch_k: int = 20,
  lambda_mult: float = 0.5,
  filter: MetadataFilter | None = None,
  search_params: models.SearchParams | None = None,
  score_threshold: float | None = None,
  consistency: models.ReadConsistency | None = None,
  **kwargs: Any = {}
) -> list[Document]

Parameters

Name	Type	Description
`embedding`*	`list[float]`	Embedding vector to look up documents similar to.
`k`	`int`	Default:`4` Number of `Document` objects to return.
`fetch_k`	`int`	Default:`20` Number of `Document` to fetch to pass to MMR algorithm.
`lambda_mult`	`float`	Default:`0.5` Number between `0` and `1` that determines the degree of diversity among the results with `0` corresponding to maximum diversity and `1` to minimum diversity.
`filter`	`MetadataFilter \| None`	Default:`None` Filter by metadata.
`search_params`	`models.SearchParams \| None`	Default:`None` Additional search params
`score_threshold`	`float \| None`	Default:`None` Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned.
`consistency`	`models.ReadConsistency \| None`	Default:`None` Read consistency of the search. Defines how many replicas should be queried before returning the result. Values: `int` - number of replicas to query, values should present in all queried replicas `'majority'` - query all replicas, but return values present in the majority of replicas `'quorum'` - query the majority of replicas, return values present in all of them `'all'` - query all replicas, and return values present in all replicas
`**kwargs`	`Any`	Default:`{}` Any other named arguments to pass through to `AsyncQdrantClient.Search()`.

View source on GitHub

amax_marginal_relevance_search_by_vector( self, embedding: list[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: MetadataFilter | None = None, search_params: models.SearchParams | None = None, score_threshold: float | None = None, consistency: models.ReadConsistency | None = None, **kwargs: Any = {} ) -> list[Document]

Parameters

Name	Type	Description
`embedding`*	`list[float]`	Embedding vector to look up documents similar to.
`k`	`int`	Default:`4` Number of `Document` objects to return.
`fetch_k`	`int`	Default:`20` Number of `Document` to fetch to pass to MMR algorithm.
`lambda_mult`	`float`	Default:`0.5` Number between `0` and `1` that determines the degree of diversity among the results with `0` corresponding to maximum diversity and `1` to minimum diversity.
`filter`	`MetadataFilter \| None`	Default:`None` Filter by metadata.
`search_params`	`models.SearchParams \| None`	Default:`None` Additional search params
`score_threshold`	`float \| None`	Default:`None` Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned.
`consistency`	`models.ReadConsistency \| None`	Default:`None` Read consistency of the search. Defines how many replicas should be queried before returning the result. Values: `int` - number of replicas to query, values should present in all queried replicas `'majority'` - query all replicas, but return values present in the majority of replicas `'quorum'` - query the majority of replicas, return values present in all of them `'all'` - query all replicas, and return values present in all replicas
`**kwargs`	`Any`	Default:`{}` Any other named arguments to pass through to `AsyncQdrantClient.Search()`.

amax_marginal_relevance_search_by_vector

Parameters

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amax_marginal_relevance_search_by_vector

Parameters