dhm#SSKJr SSKrSSKrSSKJrJrJrJrJ r SSK J r SSK J r SSKJr SSKJr \R$"\5r"SS \5rg) ) annotationsN)AnyIterableListOptionalTuple)Document) Embeddings) VectorStore)DistanceStrategycr\rSrSrSr\R 4SSjjr\SSj5r SSjr SSjr SSSjjr SSS jjr SSS jjrS /4SS jjrS /S .SSjjrS /4SSjjr\SSSjj5rSrg) KDBAIa,`KDB.AI` vector store. See https://kdb.ai. To use, you should have the `kdbai_client` python package installed. Args: table: kdbai_client.Table object to use as storage, embedding: Any embedding function implementing `langchain.embeddings.base.Embeddings` interface, distance_strategy: One option from DistanceStrategy.EUCLIDEAN_DISTANCE, DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE. See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb). cdSSKnXlX lX0lg![a [S5ef=f)Nrz`Could not import kdbai_client python package. Please install it with `pip install kdbai_client`.) kdbai_client ImportError_table _embeddingdistance_strategy)selftable embeddingrrs ^/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/kdbai.py__init__KDBAI.__init__!s@    #!2 E  s/cZ[UR[5(a UR$gN) isinstancerr )rs r embeddingsKDBAI.embeddings4s dooz 2 2?? "c[UR[5(a$URR[ U55$UVs/sHo RU5PM sn$s snfr)rrr embed_documentslist)rtextsts r_embed_documentsKDBAI._embed_documents:sK dooz 2 2??224;? ?,12Eq"E222sA$c[UR[5(aURRU5$URU5$r)rrr embed_query)rtexts r _embed_queryKDBAI._embed_query?s7 dooz 2 2??..t4 4t$$r!NcSSKnSSKnURR U5nUR 5nX'S'UVs/sHoR S5PM snUS'UV s/sHoRU SS9PM sn US 'UbURXs/S S 9nURRUS S 9 g![a [S5ef=f![a [S5ef=fs snfs sn f)NrzRCould not import numpy python package. Please install it with `pip install numpy`.TCould not import pandas python package. Please install it with `pip install pandas`.idzutf-8r+float32)dtyper)axisF)warn) numpyrpandasrr# DataFrameencodearrayconcatrinsert) rr%idsmetadatanppdembedsdfr&es r_insert KDBAI._insertDs     007 \\^4167Ahhw'76 BHI&QHHQiH8&I<  B>2B 2E*+ >   ?  8Is!B.CC !C%.CCc <SSKn[U5nSnUb.[X&R5(aUnOUR U5n/n[ U5S- U-S-n [ U 5Hn X-n U S-U-n XU n U(aX;U nO?[ [ U 55Vs/sH!n[[R"55PM# nnUbURXRSS9nOSnURXU5 X-nM U$![a [S5ef=fs snf)aRun more texts through the embeddings and add to the vectorstore. Args: texts (Iterable[str]): Texts to add to the vectorstore. metadatas (Optional[List[dict]]): List of metadata corresponding to each chunk of text. ids (Optional[List[str]]): List of IDs corresponding to each chunk of text. batch_size (Optional[int]): Size of batch of chunks of text to insert at once. Returns: List[str]: List of IDs of the added texts. rNr/r3T)drop) r7rr$rr8lenrangestruuiduuid4iloc reset_indexrD)rr% metadatasr= batch_sizekwargsr@metadfout_idsnbatchesiistartiendbatch batch_ids_ batch_metas r add_textsKDBAI.add_textscs.,  U #  )\\22"i0JNz1A5xA^FEZ'D&Et, 8=c%j8IJ8I1S.8I J!#[[5AAtAL ! LL: 6)G!; ?  ,KsD'DDc SSKnUVs/sHoURPM nnURUVs/sHoURPM sn5nUR XgUS9$![a [S5ef=fs snfs snf)aRun more documents through the embeddings and add to the vectorstore. Args: documents (List[Document]: Documents to add to the vectorstore. batch_size (Optional[int]): Size of batch of documents to insert at once. Returns: List[str]: List of IDs of the added texts. rNr/)r>rP)r7r page_contentr8r>r\)r documentsrPrQr@xr%r>s r add_documentsKDBAI.add_documentss~  *33A3<) poprsearchrr$to_dictappendr items) rrrfrgrQmatchesdocsrowr+scorevs rrh,KDBAI.similarity_search_by_vector_with_scores$ &= 3A++$$Wi[AWPVW gt $ $ajGK??)?4C776?DGGO,E KK%)3699;!N;41!v+$!$;!N  5 "Os / C>Cc dUR"U4X#S.UD6nUVVs/sHupgUPM snn$s snnf)a(Run similarity search from a query string. Args: query (str): Query string. k (Optional[int]): number of neighbors to retrieve. filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html Returns: List[Document]: List of similar documents. re)rj)rrirfrgrQdocs_and_scoresdocrZs rsimilarity_searchKDBAI.similarity_searchsC";;   )/ #22//222s ,c [S5e)zNot implemented.) Exception)clsr%rrOrQs r from_textsKDBAI.from_textss*++r!)rrr)rrrr rzOptional[DistanceStrategy])returnzOptional[Embeddings])r% Iterable[str]rzList[List[float]])r+rJr List[float]r)r% List[str]r=Optional[List[str]]r>z Optional[Any]rNone)NN ) r%rrOOptional[List[dict]]r=rrPintrQrrr)r)r`List[Document]rPrrQrrr) rirJrfrrgOptional[List]rQrrList[Tuple[Document, float]]) rrrfrrgrrQrrr) rirJrfrrgrrQrrr) rrr%rrr rOrrQrrr)__name__ __module__ __qualname____firstlineno____doc__r EUCLIDEAN_DISTANCErpropertyrr'r,rDr\rbrjrhr classmethodr__static_attributes__rrr!rrrs,  / / 333 3& 3 %#' ++!+ +  +D+/#' 55(5! 5  5  5 5p<>O'O58OILO O8!#         &  2!# &&  &  &  & &&V!# 33 3 3  3  3, +/ , ,,,( ,  , ,,r!r) __future__rloggingrKtypingrrrrrlangchain_core.documentsr langchain_core.embeddingsr langchain_core.vectorstoresr &langchain_community.vectorstores.utilsr getLoggerrloggerrrrr!rrs>" 77-03C   8 $,K,r!