dhMSSKJr SSKrSSKrSSKrSSKJrJrJrJ r J r J r J r J r SSKrSSKJr SSKJr SSKJr \(aSSKrSr"SS \5rg) ) annotationsN) TYPE_CHECKINGAnyDictIterableListOptionalTupleType)Document) Embeddings) VectorStorec>\rSrSrSrSrSrS\\\4SSjjr\SSj5r \ S\\\4SS jj5r \ SS\\\4SS jj5r S S SS .SSS ./4SSjjr S S!SjjrS"S#SjjrS$Sjr\ S\\\4S%Sjj5r\4S&Sjjr\4S'Sjjr\4S(SjjrS)S(SjjrS"S*SjjrS"S+SjjrSrg),Vearchlangchain_vearchcluster_client_dbNstrc U(aSSKnOSSKnU(a|Uc [S5eU(dDURnUS- nU[ [ R"55RS5S- nX@l X l WRU5UlOUc&[R"5RSS 5n OUn [RR!U 5(d[R""U 5 [RR%U S 5n [RR!U 5(d[R""U 5 WR&"X5UlXlU(dDUR*nUS- nU[ [ R"55RS5S- nX0lXlXPlg![a [S5ef=f) zCInitialize vearch vector store flag 1 for cluster,0 for standalone rNzhCould not import suitable python package. Please install it with `pip install vearch or vearch_cluster`.zPlease input url of cluster_-\/log)vearch_clustervearch ImportError ValueError_DEFAULT_CLUSTER_DB_NAMEruuiduuid4split using_db_nameurl VearchClusterosgetcwdreplacepathisdirmakedirsjoinEngineusing_metapath_DEFAULT_TABLE_NAMEusing_table_nameembedding_funcflag) selfembedding_function path_or_url table_namedb_namer5kwargsrr metadata_pathlog_paths _/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/vearch.py__init__Vearch.__init__s % " !>??7733tzz|,2237;;!( "H(66{CDK" " 3 3D# > + 77==// M*ww||M59H77==** H% -- @DK"/ 11J # J #djjl+11#6r: :J *0 G Q  s GGG&cUR$N)r4r6s r> embeddingsVearch.embeddingsNs"""c UVs/sHoRPM n nUVs/sHoRPM n nUR"SU UU UUUUS.UD6$s snfs snf)Return Vearch VectorStore)texts embedding metadatasr8r9r:r5) page_contentmetadata from_texts) cls documentsrJr8r9r:r5r;drIrKs r>from_documentsVearch.from_documentsRsm*33A3)23AZZ 3~~  #!    43s AAc :U"UUUUUUS9n U RXS9 U $)rH)r7rJr8r:r9r5)rIrK) add_texts) rPrIrJrKr8r9r:r5r; vearch_dbs r>rOVearch.from_textsms9(#!   %=rFtext)fieldtyperNc [RR[RRS.nSSSSS.S.nUVs/sH"n[R"USX5S 5PM$ nn[R "S [RR S US S SS0SS9nURRUURUUS9nU$s snf)z Create VectorStore Table Args: dim:dimension of vector fields_list: the field you want to store Return: code,0 for success,1 for failed )intri'IVFPQi ) ncentroids nsubvector) index_sizeretrieval_typeretrieval_paramr[r\text_embeddingT MemoryOnly cache_sizeF)namer\is_index dimensionmodel_id store_type store_param has_source)rjfields vector_field) rdataTypeINTSTRINGGammaFieldInfoGammaVectorInfoVECTOR create_tabler3) r6dim field_list type_dict engine_infofirqrr response_codes r> _create_tableVearch._create_tables"#OO//8N8NO %.2"E !    ! !"W+yF/D E   --!''#%u-   00 &&% 1 ) s(C c URSSSSSSS0S.SS0SS0S S US S .S .S.nURRURU5nU$)zi Create VectorStore space Args: dim:dimension of vector Return: code,0 failed for ,1 for success rgammaFLAT metric_typeL2)rjrcrdrer\stringvectorTrh)r\indexrlrn)rZrNrf)rj partition_num replica_numengine properties)r3r create_spacer&)r6rz space_configrs r> _create_spaceVearch._create_spaces))"(!4$ HH%!!$". #  6 001C1C\R rFc xSnURb$URR[U55nUc [S5eUR(GaUR R 5nURU;a7UR RUR5nU(d [S5eUR RUR5nURU;a/UR[US55nU(d [S5e/n UGbUGb[XU5Hupn 0n XS'U SU S'[R"U 5nS U[R R#U5- R%50U S 'UR R'URURU 5nUS S :XaU R)US 5 MUR R'URURU 5nU R)US 5 M U $[*R,R/UR0URS-5n[*R,R3U5(d1[US5nUR5U5nU(a [S5eUGbUb/n[XU5Haupn 0nU US'U SUS'[R"U 5nU[R R#U5- US 'UR)U5 Mc UR R7U5n Sn[U 5[U5:wa<[8R:"S5 US:aOUS- n[U 5[U5:waM<UR R=5 W $)zF Returns: List of ids from adding the texts into the vectorstore. Nzembeddings is Nonezcreate db failed!!!rzcreate space failed!!!rZsourcerNfeaturerfstatus_id.schemazcreate table failed!!!g?r)r4embed_documentslistr!r5rlist_dbsr& create_db list_spacesr3rlenzipnparraylinalgnormtolist insert_oneappendr)r,r/r1existsraddtimesleepdump)r6rIrKr;rDdbs_listcreate_db_code space_listcreate_space_codedocidrZrNembedprofilesembed_np insert_res retry_insert table_pathrzr doc_items profiles_vt_times r>rVVearch.add_textssK    *,,<>E%)*?-0:-N)DE/1H'+V$+3H+=HZ(!xxH!Hryy~~h/G$G#O#O#Q2H-."&!7!7**D,A,A8"J"(+s2 Z%67 '+{{'='= ..0E0Ex(  \%%89 '.O^ 5##T%:%:Y%FJ77>>*--*Q-( $ 2 23 7  $%=>>%)*? -0:-N)DE13J)-Jv&-5h-?Jz*!xxH3;biinnX>V3VJ/0$$Z0 .O  2%jC O3JJsOzaKF %jC O3    " rFc8URR5 g)z* load vearch engine for standalone vearch N)rloadrCs r>_load Vearch._load*s rFc $U(d [S5eU(d [S5e[RRX#S-5n[RR U5(d [S5eU"UUUUUS9nUR 5 U$)zzLoad the local specified table of standalone vearch. Returns: Success or failure of loading the local specified table zNo metadata path!!!zNo table name!!!rz$vearch vectorbase table not exist!!!)r7r8r9r:r5)r!r)r,r/rr) rPrJr8r9r:r5r;rrWs r> load_localVearch.load_local0s23 3/0 0WW\\+I/EF ww~~j))CD D(#!   rFc URc [S5eURRU5nURXB5nU$)z% Return docs most similar to query. embedding_func is None!!!)r4r! embed_querysimilarity_search_by_vector)r6querykr;rDdocss r>similarity_searchVearch.similarity_searchPsG    &89 9((44U; // > rFc [R"U5nUR(avSSU[RR U5- R 5S./0USS/S.nUR RURURU5nUSSnORSU[RR U5- S.//SS S S .US .nUR RU5nUS Sn/nUHYn Sn 0n UR(aU Sn U Hn U S:XaXn MU S:XdMXU S'M UR[XS95 M[ U$)_The most k similar documents and scores of the specified query. Args: embeddings: embedding vector of the query. k: The k most similar documents to the text query. min_score: the score of similar documents to the text query Returns: The k most similar documents to the specified text query. 0 is dissimilar, 1 is the most similar. sumrfr[rrZrNrsizerqhitsr InnerProductrnproberrqis_brute_searchretopnr result_itemsrg_sourcerrMrN) rrr5rrrrsearchr&r3rr ) r6rJrr;r query_data query_resultresritemcontent meta_dataitem_keys r>r"Vearch.similarity_search_by_vector`st# 99%5(- u0E(E'M'M'O!:. J ;;--""D$9$9:Lv&v.C "2#(299>>%+@#@ #$3AR#P J ;;--j9Lq/.1CDGIyyI v%"nGz)*..Ih' ! KKgJ K rFc ZURc [S5eURRU5n[R"U5nUR (avSSU[R RU5- R5S./0U/SQS.nURRURURU5nUSSnORSU[R RU5- S.//SS S S .US .nURRU5nUS Sn/n UHn Sn 0n UR (a U Sn U Sn U H<nUS:XaXn MUS:Xa XU S'MUR S:wdM0US:XdM8Xn M> [XS9W 4nU RU5 M U $)rrrrfr)rfrZrNrrrrrrrrrrg_scorerrZrNrscorer)r4r!rrrr5rrrrrr&r3r r)r6rrr;rDrrrrresultsrrrrrtmp_ress r>similarity_search_with_score#Vearch.similarity_search_with_scores    &89 9((44U; $ 99%5(- u0E(E'M'M'O@ J ;;--""D$9$9:Lv&v.C "2#(299>>%+@#@ #$3AR#P J ;;--j9Lq/.1C02DGIyyXI v%"nGz)*..Ih'99>h'&9 NE! WI5QG NN7 ##$rFc (UR"X40UD6$rB)r)r6rrr;s r>(_similarity_search_with_relevance_scores/Vearch._similarity_search_with_relevance_scoress 00DVDDrFc TSn/nUbUR5S:XaU$UHrnUR(a2URRURUR U5nOURR U5nURU5 Mt [SU55nU$)aDelete the documents which have the specified ids. Args: ids: The ids of the embedding vectors. **kwargs: Other keyword arguments that subclasses might use. Returns: Optional[bool]: True if deletion is successful. False otherwise, None if not implemented. Nrc3(# UH oS:Hv M g7f)rNrL).0is r> Vearch.delete.. s*'Qq&'s) __len__r5rdeleter&r3del_docrall)r6idsr;retrrs r>r Vearch.deletes# ;#++-1,JCyykk((););T=R=RTWXkk))#. NN3   *'** rFc <0nUbUR5S:XaU$UR(aSSU00nURRURUR U5nUHNnUSSLaM Sn0nUSH&n U S:Xa USU nMU S :XdMUSU US 'M( [ XxS 9X6S 'MP U$UH\n URRU 5nU0:XaM&Sn0nUHn U S:XaXYnMU S :XdMXYUS 'M [ XxS 9X5S 'M^ U$) zReturn docs according ids. Args: ids: The ids of the embedding vectors. Returns: Documents which satisfy the input conditions. rrrfoundFrgrrZrNrrr)rr5r mget_by_idsr&r3r get_doc_by_id) r6rr;rr docs_detailrecordr meta_infor[ids r>get Vearch.get sm(* ;#++-1,N 99!E3<0J++11""D$9$9:K&'?e+ #I.E"("3E": *,.4Y.?.F (+ /*2!(*u &&@!"kk77; "$ (E"-"4 *,.9.@ (+ )/7!(/E*+ rF)r4r5r'r&r1r3r)r7r r8 Optional[str]r9rr:rr5r^r;rreturnNone)rzOptional[Embeddings])rP Type[Vearch]rQList[Document]rJr r8rr9rr:rr5r^r;rrr)rPr rI List[str]rJr rKOptional[List[dict]]r8rr9rr:rr5r^r;rrr)rzr^r{z List[dict]rr^)rY)rzr^rr^rB)rIz Iterable[str]rKr r;rrr )rr )rJr r8rr9rr:rr5r^r;rrr)rrrr^r;rrr )rJz List[float]rr^r;rrr )rrrr^r;rrzList[Tuple[Document, float]])r)rOptional[List[str]]r;rrzOptional[bool])rrr;rrzDict[str, Document])__name__ __module__ __qualname____firstlineno__r2r"_DEFAULT_VERSIONr?propertyrD classmethodrSrOrrrVrr DEFAULT_TOPNrrrrrr__static_attributes__rLrFr>rrs,2 &*-/$ 4&4#4 4  4  44 4l## &*-/$  !  #           4 +/%)-/$ (  #    4e , % 0" + ++ +^( ( (Z+/KK(K K  KZ &*-/$ #      D    &>> > >  >FFF F F & FVEE E E & E$(   <$(5 55  55rFr) __future__rr)rr#typingrrrrrr r r numpyrlangchain_core.documentsr langchain_core.embeddingsr langchain_core.vectorstoresrrrrrLrFr>rs?" 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