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Defaults to 20. lambda_mult: 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. Defaults to 0.5 filters (str, optional): Filtering expression. Defaults to None. Returns: List of Documents most similar to the query and score for each rVrmrprrs rK/hybrid_max_marginal_relevance_search_with_scorez;AzureSearch.hybrid_max_marginal_relevance_search_with_scores`8$$U+ %$%% ug /6 :@ @ RXXi%8kUV  rMcK|j|d{}|j|||fd|i|d{}t|tj|||d{S7M707w)rNrVrmrvrrs rK0ahybrid_max_marginal_relevance_search_with_scorez6 > 8>>c |j|fd|i|}|dk(r!|D cgc]\}}} |||k\r||fc} }}S|dk(r!|D cgc]\}} } || |k\r|| fc} } }Sycc} }}wcc} } }w) Returns the most similar indexed documents to the query text. Args: query (str): The query text for which to find similar documents. k (int): The number of documents to return. Default is 4. score_type: Must either be "score" or "reranker_score". Defaulted to "score". filters: Filtering expression. 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Can include things like: score_threshold: Minimum relevance threshold for similarity_score_threshold fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR; 1 for minimum diversity and 0 for maximum. (Default: 0.5) filter: Filter by document metadata Returns: AzureSearchVectorStoreRetriever: Retriever class for VectorStore. tagsN vectorstorerw)rextend_get_retriever_tagsAzureSearchVectorStoreRetriever)rrIrs rK as_retrieverzAzureSearch.as_retrieversG(zz&$'-2 D,,./.U4U6UPTUUrM)r,NNNNNNN) rrurrurtrurzUnion[Callable, Embeddings]rrur Optional[str]rnOptional[List[SearchField]]roOptional[VectorSearch]rCOptional[Union[SemanticConfiguration, List[SemanticConfiguration]]]rqOptional[List[ScoringProfile]]rrrrsOptional[CorsOptions]rz Optional[int]rOptional[Dict[str, Any]]rJrrIr)rvNone)rvzOptional[Embeddings])rrurv List[float]r) rz Iterable[str]rOptional[List[dict]]rOptional[List[str]]rIrrv List[str])r!Iterable[Tuple[str, List[float]]]rrrrrvr)rrrIrrvbool)r\) r3rurrFrrrIrrvList[Document])r3rurrFrIrrvList[Tuple[Document, float]]) r3rurrFrGOptional[float]rIrrvr) r3rurrFrVrrIrrvr)r\N) r3rurrFrVrrIrrvr)r\?)r3rurrFrsrFrorrVrrIrrvr)r3rurrFrIrrvr) rrrrurrFrVrrIrrvzSearchItemPaged[dict]) rrrrurrFrVrrIrrvzAsyncSearchItemPaged[dict])r\r) r3rurrFrz"Literal['score', 'reranker_score']rGrrIrrvr) r3rurrFrVrrIrrvz#List[Tuple[Document, float, float]])Nr`r`NrN)rType[AzureSearch]rrrrrrrrurrurJrrtrurnrrIrrvr)rrrrrrrrrrurrurtrurnrrIrrvr)rIrrvr)1__name__ __module__ __qualname____doc__rrpropertyrrrrrrr&r(r5r<rArFrNrSror>r8rCrtrxr0r?r9rDrrrrrbrhr1r@r:rErr classmethodrrrrrrwrMrKrrsp 0$59.204 ;?15.2v*"37EI/3'v*"v*v* v* 8 v*  v*&3v*,v*.v*" v*9v*"/v*,v*"#0#v*$+C%v*& -'v*()v*p!2  A+/Q %) QQ(Q " Q  Q QB+/X %) XX(X " X  X XB+/;/ %) ;/:;/(;/ " ;/  ;/@+/7/ %) 7/:7/(7/ " 7/  7/r & &*   #    ('( K K # K36 K % K" &*   #    ('( K K # K36 K % K"  ,0       )     &  $  ,0       )     &  $%3BF33 32?3RU3 3"$%3BF33 32?3RU3 3*!% .. . .  . & .4!% 55 5 5  5 & 58  "&                &  J % "&% %  %  %  % % %  &% N 3$%33 3033 3&!% .. . .  . & .2!% 55 5 5  5 & 56  ,0       )     &  $  ,0       )     &  $ # "&# #  #  #  # # #  &# P & "&& &  &  &  & & &  && \"& " " "   "  " "  " T"& " " "   "  " "  $" J$%66 6036 6($%66 6036 6,9@ % ,0 %% %7 % ) %% &%T9@ % ,0 %% %7 % ) %% &%P$%UBFUU U2?URUU ,Up$%VBFVV V2?VRUV ,Vp +/%' "/3+.2 (  #    -, 2 +/%' "/3+.2 (  #    -, 2 +/  &( "+.2 :(  # , : +/  &( "+.2 :(  # , <VrMrceZdZUdZded< dZded< dZded < iZd ed < d Zd ed<e dZ e de ddZ ddZ ddZy)rz-Retriever that uses `Azure Cognitive Search`.rrr,rurr\rFrdict search_kwargs)r+similarity_score_thresholdr,hybrid_score_thresholdr-semantic_hybrid_score_thresholdzClassVar[Collection[str]]allowed_search_typesT)arbitrary_types_allowedbefore)modecjd|vr.|d}||jvrtd|d|j|S)zValidate search type.rr.z not allowed. 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