dhR SSKJr SSKrSSKrSSKrSSKJrJrJrJ r J r J r J r J r Jr SSKrSSKJr SSKJr SSKJr SSKJrJr \(aSSKJr SS KJrJr \R>"\ 5r!SS jr"\ "S S S 9r#"SS \5r$g)) annotationsN) TYPE_CHECKINGAnyCallableIterableListOptionalTupleTypeVarUnion)Document) Embeddings) VectorStore)DistanceStrategymaximal_marginal_relevanceClient)NeighborVectorDistanceMetriccPSSKJn U$![an[S5UeSnAff=f)NrrzoCould not import aerospike_vector_search python package. Please install it with `pip install aerospike_vector`.)aerospike_vector_searchr ImportError)res b/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/aerospike.py_import_aerospiker#s;2 M  E  s % %AVST Aerospike)boundc\rSrSrSrSSSSS\R 4SSjjr\SSj5r SS jr SS jr \ SS j5r SSS jjrSS S jjrS!S"SjjrS!S#SjjrS!S$SjjrS!S%SjjrS&Sjr\ S'Sj5rS(S)SjjrS(S*Sjjr\S+S,Sjj5rSrg)-r1zm`Aerospike` vector store. To use, you should have the ``aerospike_vector_search`` python package installed. N_vector_text_idc H[5n [U[5(d[R"S5 [X5(d[ S[ U535eXlX lX`l XPl Xpl X@l X0l XlURU 5Ulg)aInitialize with Aerospike client. Args: client: Aerospike client. embedding: Embeddings object or Callable (deprecated) to embed text. namespace: Namespace to use for storing vectors. This should match index_name: Name of the index previously created in Aerospike. This vector_key: Key to use for vector in metadata. This should match the key used during index creation. text_key: Key to use for text in metadata. id_key: Key to use for id in metadata. set_name: Default set name to use for storing vectors. distance_strategy: Distance strategy to use for similarity search This should match the distance strategy used during index creation. z`Passing in `embedding` as a Callable is deprecated. Please pass in an Embeddings object instead.zDclient should be an instance of aerospike_vector_search.Client, got N)r isinstancerwarningswarn ValueErrortype_client _embedding _text_key _vector_key_id_key _index_name _namespace _set_nameconvert_distance_strategy_distance_strategy) selfclient embedding namespace index_name vector_keytext_keyid_keyset_namedistance_strategy aerospikes r__init__Aerospike.__init__7s<&' )Z00 MM.  &,,F|n&   #!% %#!"&"@"@AR"ScZ[UR[5(a UR$g)z/Access the query embedding object if available.N)r%r+rr4s r embeddingsAerospike.embeddingsms" dooz 2 2?? "rAc[UR[5(a$URR[ U55$UVs/sHo RU5PM sn$s snf)zEmbed search docs.)r%r+rembed_documentslist)r4textsts r_embed_documentsAerospike._embed_documentstsK dooz 2 2??224;? ?,12Eq"E222sA$c[UR[5(aURRU5$URU5$)zEmbed query text.)r%r+r embed_query)r4texts r _embed_queryAerospike._embed_queryzs7 dooz 2 2??..t4 4t$$rAc SSKJn [U[5(aU$XR:Xa[R$XR :Xa[R $XR :Xa[R$[S5e)z Convert Aerospikes distance strategy to langchains DistanceStrategy enum. This is a convenience method to allow users to pass in the same distance metric used to create the index. r)rDUnknown distance strategy, must be cosine, dot_product, or euclidean) aerospike_vector_search.typesrr%rCOSINE DOT_PRODUCTSQUARED_EUCLIDEANEUCLIDEAN_DISTANCEr()r=rs rr2#Aerospike.convert_distance_strategysu G ')9 : :$ $  ; ; ;#** *  @ @ @#// /  F F F#66 6 R  rAc nUc URnUc URnU(aUc [S5e[U5nU=(d. UV s/sH!n [ [ R "55PM# sn nU(aUV s/sHoR5PM nn OU=(d UV s/sHn 0PM sn n[S[U5U5Hn XX-n X;X-n X+X-nURU 5n[XU 5H%unnnUUUR'UUUR'M' [X5H@unnUUUR'URR "SUR"UUUS.UD6 MB M U(a$URR%UR"US9 U$s sn fs sn fs sn f)aRun more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadata associated with the texts. ids: Optional list of ids to associate with the texts. set_name: Optional aerospike set name to add the texts to. batch_size: Batch size to use when adding the texts to the vectorstore. embedding_chunk_size: Chunk size to use when embedding the texts. index_name: Optional aerospike index name used for waiting for index completion. If not provided, the default index_name will be used. wait_for_index: If True, wait for the all the texts to be indexed before returning. Requires index_name to be provided. Defaults to True. kwargs: Additional keyword arguments to pass to the client upsert call. Returns: List of ids from adding the texts into the vectorstore. z6if wait_for_index is True, index_name must be providedr)r7keyr< record_data)r7name)r1r/r(rHstruuiduuid4copyrangelenrKzipr-r,r.r*upsertr0wait_for_index_completion)r4rI metadatasidsr<embedding_chunk_sizer8wait_for_indexkwargs_mi chunk_texts chunk_idschunk_metadatasrDmetadatar6rOids r add_textsAerospike.add_textss@  ~~H  ))J j0UV VU 77Ac$**,'7 +459a9I5I!8%%8%Qb%%8Iq#e*&:;AA$<=K 89I'A,DEO..{;J-0[.))T.7))*+/( . !$I ? H)+& ##"oo% (   !@<,  LL 2 2// 3   I86%8s 'F(F-) F2c SSKJn U(a4UH.nURR"SURUUS.UD6 M0 g!Ua  gf=f)zDelete by vector ID or other criteria. Args: ids: List of ids to delete. **kwargs: Other keyword arguments to pass to client delete call. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. r)AVSServerError)r7r[r<FTr^)rrxr*deleter0)r4rir<rlrxrts rryAerospike.deletes` ; !LL''"&//!)! &! !s*AA  A c NUR"URU54UUUS.UD6$)a/Return aerospike documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Name of the index to search. Overrides the default index_name. kwargs: Additional keyword arguments to pass to the search method. Returns: List of Documents most similar to the query and associated scores. k metadata_keysr8)&similarity_search_by_vector_with_scorerP)r4queryr}r~r8rls rsimilarity_search_with_score&Aerospike.similarity_search_with_score s=.::   e $ '!     rAc /nU(a URU;aUR/U-nUc URnUc [S5eURR"SUUR UUUS.UD6nUHnUR n URU ;aCU RUR5n URn UR[XS9U 45 Mb[RSURS35 M U$)aSReturn aerospike documents most similar to embedding, along with scores. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Name of the index to search. Overrides the default index_name. kwargs: Additional keyword arguments to pass to the client vector_search method. Returns: List of Documents most similar to the query and associated scores. zindex_name must be provided)r8r7rlimit field_names) page_contentrszFound document with no `z` key. Skipping.r^) r,r/r(r* vector_searchr0fieldspopdistanceappendr loggerwarning) r4r6r}r~r8rldocsresultsresultrsrOscores rr0Aerospike.similarity_search_by_vector_with_score*s2 T^^=@!^^,}>NO rAc dUR"U4UUUS.UD6VVs/sHupgUPM snn$s snnf)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Name of the index to search. Overrides the default index_name. kwargs: Additional keyword arguments to pass to the search method. Returns: List of Documents most similar to the query vector. r|)r)r4r6r}r~r8rldocrms rsimilarity_search_by_vector%Aerospike.similarity_search_by_vectorfsU2EE+%         s ,c fUR"U4X#US.UD6nUVVs/sHupxUPM snn$s snnf)aReturn aerospike documents most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Optional name of the index to search. Overrides the default index_name. Returns: List of Documents most similar to the query and score for each r|)r) r4rr}r~r8rldocs_and_scoresrrms rsimilarity_searchAerospike.similarity_searchsE*;;    NT #22//222s -cUR[R:Xa UR$UR[R:Xa UR $UR[R :Xa UR$[S5e)a The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. 0 is dissimilar, 1 is similar. Aerospike's relevance_fn assume euclidean and dot product embeddings are normalized to unit norm. rS) r3rrU_cosine_relevance_score_fnrV%_max_inner_product_relevance_score_fnrX_euclidean_relevance_score_fnr(rCs r_select_relevance_score_fn$Aerospike._select_relevance_score_fnsw  " "&6&=&= =22 2  $ $(8(D(D D== =  $ $(8(K(K K55 5V rAcSUS- - $)zWAerospike returns cosine distance scores between [0,2] 0 is dissimilar, 1 is similar. r^)rs rr$Aerospike._cosine_relevance_score_fns EAIrAc U(a URU;aUR/U-nUR"U4UUUS.UD6n[[R"U/[R S9UV s/sHoR URPM sn UUS9n U(a@URU;a0U H*n XR RUR5 M, U V s/sHoU PM sn $s sn fs sn f)aYReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. 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. metadata_keys: List of metadata keys to return with the documents. If None, all metadata keys will be returned. Defaults to None. index_name: Optional name of the index to search. Overrides the default index_name. Returns: List of Documents selected by maximal marginal relevance. r|)dtype)r} lambda_mult)r-rrnparrayfloat32rsr) r4r6r}fetch_krr~r8rlrr mmr_selectedros r'max_marginal_relevance_search_by_vector1Aerospike.max_marginal_relevance_search_by_vectors< T--]B!--.>M//  '!    2 HHi[ 37; !  $$T%5%56""..AQ.. =/s +!C.  C3c VURU5nUR"UUUU4UUS.UD6$)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. 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. index_name: Name of the index to search. Returns: List of Documents selected by maximal marginal relevance. )r~r8)rPr) r4rr}rrr~r8rlr6s rmax_marginal_relevance_search'Aerospike.max_marginal_relevance_searchsJ6%%e, ;;     (!    rAc  ^U"UUU40U D6n U R"U4UUUUS.U =(d 0D6 U $)a This is a user friendly interface that: 1. Embeds text. 2. Converts the texts into documents. 3. Adds the documents to a provided Aerospike index This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Aerospike from langchain_openai import OpenAIEmbeddings from aerospike_vector_search import Client, HostPort client = Client(seeds=HostPort(host="localhost", port=5000)) aerospike = Aerospike.from_texts( ["foo", "bar", "baz"], embedder, client, "namespace", index_name="index", vector_key="vector", distance_strategy=MODEL_DISTANCE_CALC, ) )rhrir8rj)ru) clsrIr6rhr5r7r8riembeddings_chunk_size client_kwargsrlr>s r from_textsAerospike.from_textss]P         !!6  "  rA) r*r3r+r.r/r0r1r,r-)r5rr6zUnion[Embeddings, Callable]r7r_r8 Optional[str]r9r_r:r_r;r_r<rr=z7Optional[Union[DistanceStrategy, VectorDistanceMetric]])returnzOptional[Embeddings])rI Iterable[str]rzList[List[float]])rOr_r List[float])r=z-Union[VectorDistanceMetric, DistanceStrategy]rr)NNNNT)rIrrhOptional[List[dict]]riOptional[List[str]]r<rrjintr8rrkboolrlrr List[str])NN)rirr<rrlrrzOptional[bool])NN) rr_r}rr~rr8rrlrrList[Tuple[Document, float]]) r6rr}rr~rr8rrlrrr) r6rr}rr~rr8rrlrrList[Document]) rr_r}rr~rr8rrlrrr)rzCallable[[float], float])rfloatrr)rg?NN)r6rr}rrrrrr~rr8rrlrrr)rr_r}rrrrrr~rr8rrlrrr)NNtestNNrN)rIrr6rrhrr5rr7r_r8rrirrrrzOptional[dict]rlrrr)__name__ __module__ __qualname____firstlineno____doc__rrXr?propertyrDrKrP staticmethodr2ruryrrrrrrrr classmethodr__static_attributes__r^rArrr1s"%)#"&  / /4T4T/4T 4T " 4T  4T4T4T 4T 4Tl 3 %  H   :+/#'"&$($(#NN(N! N  N " N"NNN Nd$("&     F-1$(     +  "     & D-1$( :: :+ : " :  : &:~-1$(       +  "       J-1$( 33 3+ 3 " 3  3 342 -1$(3/3/ 3/ 3/  3/ + 3/"3/3/ 3/p -1$($ $  $  $  $ + $ "$ $  $ L +/$(#'%)(,666( 6  6  6"6!6 #6&66 66rA)rr)% __future__rloggingr`r&typingrrrrrr r r r numpyrlangchain_core.documentsr langchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrrrrrTrr getLoggerrrrrrr^rArrso"    -03 .L   8 $v[)d drA