hhKddlmZddlZddlZddlmZmZmZmZm Z m Z m Z m Z ddl mZddlZddlmZddlmZddlmZddlmZdd lmZerddlZd"dZ d#d$dZd%dZd&dZedddGd d!eZ dS)') annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTupleuuid4) deprecated)Document) Embeddings) VectorStore)maximal_marginal_relevance index_namestrtext_keyreturnrc||dgdgdS)Ntext)namedataType)class properties)rrs f/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/langchain_community/vectorstores/weaviate.py_default_schemars-!#H     url Optional[str]api_keykwargsrweaviate.Clientc * ddl}n#t$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$r+r0s r_create_weaviate_clientr3)s       C     ///C;(:;;G8? I8= # #G # 4 4 4TD 8? Fst F Fv F FF!valfloatc<dddtj|zz z S)N)npexp)r5s r_default_score_normalizerr;;s qAs O$ $$r valuecbt|tjr|S|SN) isinstancedatetime isoformat)r<s r_json_serializablerB?s,%*++!   Lr z0.3.18z1.0z&langchain_weaviate.WeaviateVectorStore)sinceremovalalternative_importc eZdZdZddedfd=dZed>dZd?dZ d@dAdZ dBdCd"Z dBdCd#Z dBdDd%Z dEdFd+Z dEdGd,Z dBdHd.Ze d@dddddd/d0ed1dId9Zd@dJd<ZdS)KWeaviatea`Weaviate` vector store. To use, you should have the ``weaviate-client`` python package installed. Example: .. code-block:: python import weaviate from langchain_community.vectorstores import Weaviate client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...) weaviate = Weaviate(client, index_name, text_key) NTclientrrrr embeddingOptional[Embeddings] attributesOptional[List[str]]relevance_score_fn"Optional[Callable[[float], float]]by_textboolc^ ddl}n#t$rtdwxYwt||jst dt |||_||_||_||_ |j g|_ ||_ ||_ ||j |dSdS)z Initialize with Weaviate client.rNz_Could not import weaviate python package. Please install it with `pip install weaviate-client`.z5client should be an instance of weaviate.Client, got )r+r,r?r2 ValueErrortype_client _index_name _embedding _text_key _query_attrsrM_by_textextend) selfrHrrrIrKrMrOr+s r__init__zWeaviate.__init__Zs  OOOO   H   &(/22 VV VV  %#!!^,"4  !   $ $Z 0 0 0 0 0 " !r4rc|jSr>)rVr[s r embeddingszWeaviate.embeddings|s r Callable[[float], float]c,|jr|jntSr>)rMr;r^s r_select_relevance_score_fnz#Weaviate._select_relevance_score_fns& +D # #* r texts Iterable[str] metadatasOptional[List[dict]]r$ List[str]c ddlm}g}d}|jr>t|tst |}|j|}|jj5}t|D]\}} |j | i} |2|| D]\} } t| | | <|t} d|vr|d|} nd|vr|d|} | | |j| |r||nd|d||  dddn #1swxYwY|S)z4Upload texts with metadata (properties) to Weaviate.rget_valid_uuidNuuidsidstenant) data_object class_nameuuidvectorrm) weaviate.utilrjrVr?listembed_documentsrTbatch enumeraterWitemsrBr add_data_objectrUr/append)r[rcrer$rjrlr_ruirdata_propertieskeyr5_ids r add_textszWeaviate.add_textss 10000026 ? @eT** $U 88??J \  5$U++  4#'>4"8($-aL$6$6$8$8GGS/A#/F/F,, %nUWW--f$$ /!,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)rYsimilarity_search_by_textrVrR embed_querysimilarity_search_by_vector)r[rrr$rIs rsimilarity_searchzWeaviate.similarity_searchs = L141%EEfEE E& %33E::I343IqKKFKK Kr c d|gi}|dr|d|d<|jj|j|j}|dr(||d}|dr(||d}|dr(||d}|| | }d|vrtd|dg}|d d |jD]@}| |j } |t| | A|S) rconceptssearch_distance certainty where_filterrm additionalerrorsError during query: dataGet page_contentmetadata)r/rTrrUrX with_where with_tenantwith_additionalwith_near_text with_limitdorRpoprWryr) r[rrr$content query_objresultdocsresrs rrz"Weaviate.similarity_search_by_texts$.w"7 ::' ( ( A#)::.?#@#@GK L&**4+;T=NOO ::n % % I!,,VZZ-G-GHHI ::h   D!--fjj.B.BCCI ::l # # L!11&**\2J2JKKI))'22==a@@CCEE v  FF84DFFGG G&>%()9: C CC774>**D KKdSAAA B B B B r List[float]c 4d|i}|jj|j|j}|dr(||d}|dr(||d}|dr(||d}|| | }d|vrtd|dg}|dd|jD]@}| |j } |t| | A|S) z:Look up similar documents by embedding vector in Weaviate.rqrrmrrrrrr)rTrr/rUrXrrrwith_near_vectorrrrRrrWryr) r[rIrr$rqrrrrrs rrz$Weaviate.similarity_search_by_vectorsI&L&**4+;T=NOO ::n % % I!,,VZZ-G-GHHI ::h   D!--fjj.B.BCCI ::l # # L!11&**\2J2JKKI++F33>>qAADDFF v  FF84DFFGG G&>%()9: C CC774>**D KKdSAAA B B B B r ?fetch_k lambda_multr6c |j|j|}ntd|j|f|||d|S)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. NzCmax_marginal_relevance_search requires a suitable Embeddings object)rrr)rVrrR'max_marginal_relevance_search_by_vector)r[rrrrr$rIs rmax_marginal_relevance_searchz&Weaviate.max_marginal_relevance_searchsm2 ? &33E::IIU zDWeaviate.max_marginal_relevance_search_by_vector..Hs"LLL&f]+H5LLLr )rrrr)rTrr/rUrXrrrrrrrr9arrayrrWryr)r[rIrrrr$rqrresultspayloadr_ mmr_selectedridxrmetas rrz0Weaviate.max_marginal_relevance_search_by_vector!s2I&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 Vr rF)rH weaviate_urlweaviate_api_key batch_sizerrrOrMrOptional[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" ) rrir'N)r!r#)r LangChain_rkc>g|]}tSrr )r_rjs rrz'Weaviate.from_texts..s'HHH^^EGG,,HHHr )rprnrorq)rIrKrMrOr)rrrjr,r3ru configurer hexrschemaexists create_classrtrskeysrrangelenrvrxflush)clsrcrIrerHrrrrrrOrMr$err_rKrkrurzrr{r|r}paramsrjs @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# )$)BGGGrlNonec r|td|D]"}|jj|#dS)zUDelete by vector IDs. Args: ids: List of ids to delete. NzNo ids provided to delete.)rp)rRrTrndelete)r[rlr$ids rrzWeaviate.delete sR ;9:: : 5 5B L $ + + + 4 4 4 4 5 5r )rHrrrrrrIrJrKrLrMrNrOrP)rrJ)rr`r>)rcrdrerfr$rrrg)r)rrrrr$rrr)rIrrrr$rrr)rrr) rrrrrrrr6r$rrr) rIrrrrrrr6r$rrr)rrrrr$rrr)rcrgrIrrerfrHrrr"rr"rrrr"rrrOrPrMrNr$rrrG)rlrLr$rrr)__name__ __module__ __qualname____doc__r;r\propertyr_rbr~rrrrrr classmethodrrrr rrGrGEs  (+/*. & 1 1 1 1 1DX    +/)))))X$%LLLLL0$%@010 " " " " " N 22222j$%.....` +/ B -1&**.$($( &B B B B B [B H 5 5 5 5 5 5 5r rG)rrrrrr)NN)r!r"r#r"r$rrr%)r5r6rr6)r<rrr)! __future__rr@r-typingrrrrrr r r rpr numpyr9langchain_core._apirlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrr+rr3r;rBrGrr rrs""""""                     ******------000000333333MMMMMMOOO    !GGGGG$%%%%    ? L5L5L5L5L5{L5L5  L5L5L5r