dhKSSKJr SSKrSSKrSSKJrJrJrJrJ r J r J r J r SSK Jr SSKrSSKJr SSKJr SSKJr SSKJr SS KJr \(aSSKrSS jrSSS jjrSS jrSS jr\"SSSS9"SS\55r g)) annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTuple)uuid4) deprecated)Document) Embeddings) VectorStore)maximal_marginal_relevancecUUS/S./S.$)Ntext)namedataType)class properties) index_nametext_keys a/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/weaviate.py_default_schemars#!#H   c JSSKnU=(d [RR S5nU=(d [RR S5nU(aUR R US9OSnUR"SXS.UD6$![a [S5ef=f)Nr_Could not import weaviate python package. Please install it with `pip install weaviate-client` WEAVIATE_URLWEAVIATE_API_KEY)api_key)urlauth_client_secretr)weaviate ImportErrorosenvirongetauth AuthApiKeyClient)r#r"kwargsr%r*s r_create_weaviate_clientr.)s    //C;(:;G8?8== # #G # 4TD ?? Fs Fv FF   C   s B B"c@SSS[R"U5-- - $)N)npexp)vals r_default_score_normalizerr4;s qAs O$ $$rcd[U[R5(aUR5$U$N) isinstancedatetime isoformat)values r_json_serializabler;?s'%**++  Lrz0.3.18z1.0z&langchain_weaviate.WeaviateVectorStore)sinceremovalalternative_importc \rSrSrSrSS\S4SSjjr\SSj5rSSjr SSSjjr SSS jjr SSS jjr SSS jjr SSS jjrSSS jjrSS Sjjr\SSSSSSSS\S.S!Sjjj5rSS"SjjrSrg)#WeaviateEab`Weaviate` vector store. To use, you should have the ``weaviate-client`` python package installed. Example: .. code-block:: python import weaviate from langchain_community.vectorstores import Weaviate client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...) weaviate = Weaviate(client, index_name, text_key) NTcLSSKn[XR5(d[ S[ U535eXlX lX@lX0l UR/Ul X`l Xpl UbURRU5 gg![a [S5ef=f)z Initialize with Weaviate client.rNz_Could not import weaviate python package. Please install it with `pip install weaviate-client`.z5client should be an instance of weaviate.Client, got )r%r&r7r, ValueErrortype_client _index_name _embedding _text_key _query_attrsrelevance_score_fn_by_textextend) selfclientrr embedding attributesrJby_textr%s r__init__Weaviate.__init__Zs   &//22GV ~V  %#!!^^,"4  !    $ $Z 0 "! H  s B B#cUR$r6)rGrMs r embeddingsWeaviate.embeddings|s rcHUR(a UR$[$r6)rJr4rUs r_select_relevance_score_fn#Weaviate._select_relevance_score_fns'&&  # # + rc SSKJn /nSnUR(a;[U[5(d [ U5nURR U5nUR Rn[U5HupURU 0n Ub(X(R5Hup[U 5X'M U"[55n SU;a USUn OSU;aUSUn URU URU U(aXhOSURS5S9 UR!U 5 M SSS5 U$!,(df  U$=f)z4Upload texts with metadata (properties) to Weaviate.rget_valid_uuidNuuidsidstenant) data_object class_nameuuidvectorr`) weaviate.utilr]rGr7listembed_documentsrEbatch enumeraterHitemsr;r add_data_objectrFr)append)rMtexts metadatasr-r]r_rVrhirdata_propertieskeyr3_ids r add_textsWeaviate.add_textss3 126 ??eT**U 88?J \\  5$U+#'>>4"8($-L$6$6$8/A#/F,%9%UW-f$ /!,Cf_ -*C%% /#//,6:=D!::h/ & 3/, 2 3 2 s -CD:: E c UR(aUR"X40UD6$URc [S5eURR U5nUR "XB40UD6$)Return docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query. zC_embedding cannot be None for similarity_search when _by_text=False)rKsimilarity_search_by_textrGrC embed_querysimilarity_search_by_vector)rMquerykr-rOs rsimilarity_searchWeaviate.similarity_searchsi ==11%EfE E& %33E:I33IKFK Krc HSU/0nURS5(aURS5US'URRRURUR5nURS5(a UR URS55nURS5(a UR URS55nURS5(a URURS55nURU5RU5R5nSU;a[SUS35e/nUS S URH6nURUR5n UR[XS 95 M8 U$) rvconceptssearch_distance certainty where_filterr` additionalerrorsError during query: dataGet page_contentmetadata)r)rErzrFrI with_where with_tenantwith_additionalwith_near_text with_limitdorCpoprHrlr) rMrzr{r-content query_objresultdocsresrs rrw"Weaviate.similarity_search_by_textsf$.w"7 ::' ( (#)::.?#@GK LL&&**4+;+;T=N=NO ::n % %!,,VZZ-GHI ::h  !--fjj.BCI ::l # #!11&**\2JKI))'2==a@CCE v 3F84D3EFG G&>%()9)9:C774>>*D KKdA B; rc SU0nURRRURUR5nURS5(a UR URS55nURS5(a UR URS55nURS5(a URURS55nURU5RU5R5nSU;a[SUS35e/nUSSURH6nURUR5n UR[XS 95 M8 U$) z:Look up similar documents by embedding vector in Weaviate.rdrr`rrrrrr)rErzr)rFrIrrrwith_near_vectorrrrCrrHrlr) rMrOr{r-rdrrrrrs rry$Weaviate.similarity_search_by_vectors@I&LL&&**4+;+;T=N=NO ::n % %!,,VZZ-GHI ::h  !--fjj.BCI ::l # #!11&**\2JKI++F3>>qADDF v 3F84D3EFG G&>%()9)9:C774>>*D KKdA B; rc URbURRU5nO [S5eUR"U4X#US.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. Returns: List of Documents selected by maximal marginal relevance. zCmax_marginal_relevance_search requires a suitable Embeddings object)r{fetch_k lambda_mult)rGrxrC'max_marginal_relevance_search_by_vector)rMrzr{rrr-rOs rmax_marginal_relevance_search&Weaviate.max_marginal_relevance_searchs\2 ?? &33E:IU ;;   HN  rc "SU0nURRRURUR5nURS5(a UR URS55nURS5(a UR URS55nURS5RU5RU5R5nUSSURn U V s/sH oSSPM n n [[R"U5XUS9n /n U HPnXRUR5nXRS5 XnU R![#UUS95 MR U $s sn f) aReturn 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. Returns: List of Documents selected by maximal marginal relevance. rdrr`rr _additional)r{rr)rErzr)rFrIrrrrrrrr1arrayrrHrlr)rMrOr{rrr-rdrresultspayloadrrV mmr_selectedridxrmetas rr0Weaviate.max_marginal_relevance_search_by_vector!sd2I&LL&&**4+;+;T=N=NO ::n % %!,,VZZ-GHI ::h  !--fjj.BCI  % %h /  f % Z RT &/%()9)9:DKLG&]+H5G L1 HHY k C<##DNN3D L  ] +%()9)9:C774>>*DFF3}-h7HE  " "H$$Mu#U V;rrF)rN weaviate_urlweaviate_api_key batch_sizerrrQrJc SSKJn U=(d [UUS9nU(aURR US9 U=(d S[ 5R3n[X5nURRU5(dURRU5 U(aURU5OSnU(a[USR55OSnSU ;aU RS5nO1[![#U55Vs/sHnU "[ 55PM nnURn[%U5HZunnU U0nUb%UUR5HnUUUUU'M UUnUUUS .nUbUUUS 'UR&"S 0UD6 M\ UR)5 SSS5 U"UUU 4UUU U S .U D6$![an[S5UeSnAff=fs snf!,(df  NA=f) aNConstruct Weaviate wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in the Weaviate instance. 3. Adds the documents to the newly created Weaviate index. This is intended to be a quick way to get started. Args: texts: Texts to add to vector store. embedding: Text embedding model to use. metadatas: Metadata associated with each text. client: weaviate.Client to use. weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it from the ``Details`` tab. Can be passed in as a named param or by setting the environment variable ``WEAVIATE_URL``. Should not be specified if client is provided. weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud Services, get it from ``Details`` tab. Can be passed in as a named param or by setting the environment variable ``WEAVIATE_API_KEY``. Should not be specified if client is provided. batch_size: Size of batch operations. index_name: Index name. text_key: Key to use for uploading/retrieving text to/from vectorstore. by_text: Whether to search by text or by embedding. relevance_score_fn: Function for converting whatever distance function the vector store uses to a relevance score, which is a normalized similarity score (0 means dissimilar, 1 means similar). kwargs: Additional named parameters to pass to ``Weaviate.__init__()``. Example: .. code-block:: python from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import Weaviate embeddings = OpenAIEmbeddings() weaviate = Weaviate.from_texts( texts, embeddings, weaviate_url="http://localhost:8080" ) rr\rN)r#r")r LangChain_r^)rcrarbrd)rOrPrJrQr)rer]r&r.rh configurer hexrschemaexists create_classrgrfkeysrrangelenrirkflush)clsrmrOrnrNrrrrrrQrJr-r]errVrPr^_rhrorrprqrrparamss r from_textsWeaviate.from_textss @  4 2$   LL " "j " 9=Z }#=  6}}##J// MM & &v .9BY..u5 2;T)A,++-.  f JJw'E6;CJ6GH6G^EG,6GEH \\U$U+4d#((|002/8|C/@, 3Ah #2", )'1!}F8$%%//-,0 KKM36      !1    q G  6I \s*GG%5A:G* G" GG"* G8c xUc [S5eUH&nURRRUS9 M( g)z=Delete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.)rc)rCrEradelete)rMr_r-ids rrWeaviate.delete s< ;9: :B LL $ $ + + + 4r)rKrErGrFrIrHrJ)rNrrstrrrrOOptional[Embeddings]rPOptional[List[str]]rJ"Optional[Callable[[float], float]]rQbool)returnr)rzCallable[[float], float]r6)rmz Iterable[str]rnOptional[List[dict]]r-rr List[str]))rzrr{intr-rrList[Document])rO List[float]r{rr-rrr)rg?) rzrr{rrrrfloatr-rrr) rOrr{rrrrrr-rrr)rzrr{rr-rrzList[Tuple[Document, float]])rmrrOrrnrrNzOptional[weaviate.Client]r Optional[str]rrrz Optional[int]rrrrrQrrJrr-rrr@)r_rr-rrNone)__name__ __module__ __qualname____firstlineno____doc__r4rRpropertyrVrYrsr|rwryrrr classmethodrr__static_attributes__rrrr@r@Es (+/*. & 1 1 1 1 ( 1 ( 1  1 1D +/))() )  )X$%LL L03L L0$% 03 @01$),rs"    *-03M !G G GG G$%   ? L5{L5  L5r