Ng3KddlmZddlZddlZddlmZmZmZmZm Z m Z m Z m Z ddl mZddlZddlmZddlmZddlmZddlmZerddlZddZ dddZd dZd!dZGddeZdS)") annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTupleuuid4)Document) Embeddings) VectorStore)maximal_marginal_relevance index_namestrtext_keyreturnrc||dgdgdS)Ntext)namedataType)class properties)rrs e/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/weaviate.py_default_schemars-!#H     url Optional[str]api_keykwargsrweaviate.Clientc * ddl}n#t$rtdwxYw|ptjd}|ptjd}|r|j|nd}|jd||d|S)Nr_Could not import weaviate python package. Please install it with `pip install weaviate-client` WEAVIATE_URLWEAVIATE_API_KEY)r")r auth_client_secretr)weaviate ImportErrorosenvirongetauth AuthApiKeyClient)r r"r#r*r/s r_create_weaviate_clientr2(s       C     ///C;(:;;G8? I8= # #G # 4 4 4TD 8? Fst F Fv F FF!valfloatc<dddtj|zz z S)N)npexp)r4s r_default_score_normalizerr::s qAs O$ $$rvaluecbt|tjr|S|SN) isinstancedatetime isoformat)r;s r_json_serializablerA>s,%*++!   Lrc eZdZdZddedfd=dZed>dZd?dZ d@dAdZ dBdCd"Z dBdCd#Z dBdDd%Z dEdFd+Z dEdGd,Z dBdHd.Ze d@dddddd/d0ed1dId9Zd@dJd<ZdS)KWeaviatea`Weaviate` vector store. To use, you should have the ``weaviate-client`` python package installed. Example: .. code-block:: python import weaviate from langchain_community.vectorstores import Weaviate client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...) weaviate = Weaviate(client, index_name, text_key) NTclientrrrr embeddingOptional[Embeddings] attributesOptional[List[str]]relevance_score_fn"Optional[Callable[[float], float]]by_textboolc^ ddl}n#t$rtdwxYwt||jst dt |||_||_||_||_ |j g|_ ||_ ||_ ||j |dSdS)z Initialize with Weaviate client.rNz_Could not import weaviate python package. Please install it with `pip install weaviate-client`.z5client should be an instance of weaviate.Client, got )r*r+r>r1 ValueErrortype_client _index_name _embedding _text_key _query_attrsrI_by_textextend) selfrDrrrErGrIrKr*s r__init__zWeaviate.__init__Ts  OOOO   H   &(/22 VV VV  %#!!^,"4  !   $ $Z 0 0 0 0 0 " !r3rc|jSr=)rRrWs r embeddingszWeaviate.embeddingsvs rCallable[[float], float]c,|jr|jntSr=)rIr:rZs r_select_relevance_score_fnz#Weaviate._select_relevance_score_fnzs& +D # #* rtexts Iterable[str] metadatasOptional[List[dict]]r# List[str]c ddlm}g}d}|jr>t|tst |}|j|}|jj5}t|D]\}} |j | i} |2|| D]\} } t| | | <|t} d|vr|d|} nd|vr|d|} | | |j| |r||nd|d||  dddn #1swxYwY|S)z4Upload texts with metadata (properties) to Weaviate.rget_valid_uuidNuuidsidstenant) data_object class_nameuuidvectorri) weaviate.utilrfrRr>listembed_documentsrPbatch enumeraterSitemsrAr add_data_objectrQr.append)rWr_rar#rfrhr[rqirdata_propertieskeyr4_ids r add_textszWeaviate.add_textss 10000026 ? @eT** $U 88??J \  5$U++  4#'>4"8($-aL$6$6$8$8GGS/A#/F/F,, %nUWW--f$$ /!,CCf__ -*C%% /#/,6@:a==D!::h// & 3/                2 sC E  E E querykintList[Document]c |jr|j||fi|S|jtd|j|}|j||fi|S)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. NzC_embedding cannot be None for similarity_search when _by_text=False)rUsimilarity_search_by_textrRrN embed_querysimilarity_search_by_vector)rWr|r}r#rEs rsimilarity_searchzWeaviate.similarity_searchs = L141%EEfEE E& %33E::I343IqKKFKK Krc d|gi}|dr|d|d<|jj|j|j}|dr(||d}|dr(||d}|dr(||d}|| | }d|vrtd|dg}|d d |jD]@}| |j } |t| | A|S) rconceptssearch_distance certainty where_filterri additionalerrorsError during query: dataGet page_contentmetadata)r.rPr|rQrT with_where with_tenantwith_additionalwith_near_text with_limitdorNpoprSrur) rWr|r}r#content query_objresultdocsresrs rrz"Weaviate.similarity_search_by_texts$.w"7 ::' ( ( A#)::.?#@#@GK L&**4+;T=NOO ::n % % I!,,VZZ-G-GHHI ::h   D!--fjj.B.BCCI ::l # # L!11&**\2J2JKKI))'22==a@@CCEE v  FF84DFFGG G&>%()9: C CC774>**D KKdSAAA B B B B r List[float]c 4d|i}|jj|j|j}|dr(||d}|dr(||d}|dr(||d}|| | }d|vrtd|dg}|dd|jD]@}| |j } |t| | A|S) z:Look up similar documents by embedding vector in Weaviate.rmrrirrrrrr)rPr|r.rQrTrrrwith_near_vectorrrrNrrSrur) rWrEr}r#rmrrrrrs rrz$Weaviate.similarity_search_by_vectorsI&L&**4+;T=NOO ::n % % I!,,VZZ-G-GHHI ::h   D!--fjj.B.BCCI ::l # # L!11&**\2J2JKKI++F33>>qAADDFF v  FF84DFFGG G&>%()9: C CC774>**D KKdSAAA B B B B r?fetch_k lambda_multr5c |j|j|}ntd|j|f|||d|S)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. NzCmax_marginal_relevance_search requires a suitable Embeddings object)r}rr)rRrrN'max_marginal_relevance_search_by_vector)rWr|r}rrr#rEs rmax_marginal_relevance_searchz&Weaviate.max_marginal_relevance_searchsm2 ? &33E::IIU zDWeaviate.max_marginal_relevance_search_by_vector..Bs"LLL&f]+H5LLLr)r}rrr)rPr|r.rQrTrrrrrrrr8arrayrrSrur)rWrEr}rrr#rmrresultspayloadr[ mmr_selectedridxrmetas rrz0Weaviate.max_marginal_relevance_search_by_vectors2I&L&**4+;T=NOO ::n % % I!,,VZZ-G-GHHI ::h   D!--fjj.B.BCCI  % %h / /  f % % Z RTT &/%()9:LLGLLL 1 HY  qk    D DC3<##DN33D CL  ] + + +3%()9: W WC774>**DF3}-h7HHE  " "H$$M$M$Mu#U V V V VrrF)rD weaviate_urlweaviate_api_key batch_sizerrrKrIrOptional[weaviate.Client]rr!rr Optional[int]c  ddlmn"#t$r} td| d} ~ wwxYw|pt||}|r|j||pdt j}t|| }|j |s|j ||r| |nd}|r't|dnd}d| vr| d}n(fd t!t#|D}|j5}t%|D]a\}}| |i}|.||D]}|||||<||}|||d }| |||d <|jd i|b|dddn #1swxYwY|||| f||| | d | S)avConstruct 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" ) rrer&N)r r")r LangChain_rgc>g|]}tSrr )r_rfs rrz'Weaviate.from_texts..s'HHH^^EGG,,HHHr)rlrjrkrm)rErGrIrKr)rnrfr+r2rq configurer hexrschemaexists create_classrprokeysrrangelenrrrtflush)clsr_rErarDrrrrrrKrIr#err[rGrgrqrvrrwrxryparamsrfs @r from_textszWeaviate.from_textss@  4 4 4 4 4 4 4   G    2$     : L " "j " 9 9 9=#= #=#=  X66}##J// / M & &v . . .9BLY..u555 2;ET)A,++--...  f  JJw''EEHHHHeCJJ6G6GHHHE \ U$U++ 0 04d#((|0022AA/8|C/@,,Ah #2", )'1!}F8$%%////// KKMMM3               6s      !1      s# )$)BGGGrhNonec r|td|D]"}|jj|#dS)zUDelete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.)rl)rNrPrjdelete)rWrhr#ids rrzWeaviate.deletesR ;9:: : 5 5B L $ + + + 4 4 4 4 5 5r)rDrrrrrrErFrGrHrIrJrKrL)rrF)rr\r=)r_r`rarbr#rrrc)r{)r|rr}r~r#rrr)rErr}r~r#rrr)r{rr) r|rr}r~rr~rr5r#rrr) rErr}r~rr~rr5r#rrr)r|rr}r~r#rrr)r_rcrErrarbrDrrr!rr!rrrr!rrrKrLrIrJr#rrrC)rhrHr#rrr)__name__ __module__ __qualname____doc__r:rXpropertyr[r^rzrrrrrr classmethodrrrrrrCrCDs  (+/*. & 1 1 1 1 1DX    +/)))))X$%LLLLL0$%@010 " " " " " N 22222j$%.....` +/ B -1&**.$($( &B B B B B [B H 5 5 5 5 5 5 5rrC)rrrrrr)NN)r r!r"r!r#rrr$)r4r5rr5)r;rrr) __future__rr?r,typingrrrrrr r r rlr numpyr8langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrr*rr2r:rArCrrrrs""""""                     ------000000333333MMMMMMOOO    !GGGGG$%%%% L5L5L5L5L5{L5L5L5L5L5r