dhSSKJr SSKrSSKrSSKrSSKrSSKJrJr SSK J r SSK J r J r JrJrJrJrJr SSKrSSKJr SSKJr SSKJr SS KJrJr SS KJrJr SS K J!r! \RD"\#5r$S r%S r&Sr'\"SS55r(\"SS55r)\"SS55r*\"SS55r+"SS\5r,"SS\5r-"SS\5r.g)) annotationsN) dataclassfield)md5)AnyIterableIteratorListOptionalTupleType)CallbackManagerForRetrieverRun)Document) Embeddings)RunnableRunnableConfig) VectorStoreVectorStoreRetriever) ConfigDictivAivAivAc`\rSrSr%SrSrS\S'SrS\S'S rS \S 'S r S \S 'Sr S\S'Sr g) SummaryConfigaRConfiguration for summary generation. is_enabled: True if summary is enabled, False otherwise max_results: maximum number of results to summarize response_lang: requested language for the summary prompt_name: name of the prompt to use for summarization (see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer) Fbool is_enabledint max_resultsengstr response_langz"vectara-summary-ext-24-05-med-omni prompt_namestreamN) __name__ __module__ __qualname____firstlineno____doc__r__annotations__rr r!r"__static_attributes__r#`/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/vectara.pyrrs;JKM3;K;FDr+rcD\rSrSr%SrSrS\S'SrS\S'S rS \S 'S r g ) MMRConfig.awConfiguration for Maximal Marginal Relevance (MMR) search. This will soon be deprated in favor of RerankConfig. is_enabled: True if MMR is enabled, False otherwise mmr_k: number of results to fetch for MMR, defaults to 50 diversity_bias: number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. Frr2rmmr_k333333?floatdiversity_biasr#N) r$r%r&r'r(rr)r1r4r*r#r+r,r.r..s& JE3ONEr+r.cR\rSrSr%SrSrS\S'SrS\S'S rS \S 'S r S\S 'Sr g) RerankConfigCaConfiguration for Reranker. reranker: "mmr", "rerank_multilingual_v1", "udf" or "none" rerank_k: number of results to fetch before reranking, defaults to 50 mmr_diversity_bias: for MMR only - a number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: mmr_diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. user_function: for UDF only - the user function to use for reranking. nonerrerankerr0rrerank_kr2r3mmr_diversity_bias user_functionr#N) r$r%r&r'r(r9r)r:r;r=r*r#r+r,r6r6Cs2 HcHc ##M3r+r6c\rSrSr%SrSrS\S'SrS\S'S rS \S 'S r S \S'Sr S\S'Sr S\S'\ "\ S9rS\S'\ "\S9rS\S'SSSjjrSrg )VectaraQueryConfigYaConfiguration for Vectara query. k: Number of Documents to return. Defaults to 10. lambda_val: lexical match parameter for hybrid search. filter Dictionary of argument(s) to filter on metadata. For example a filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details. score_threshold: minimal score threshold for the result. If defined, results with score less than this value will be filtered out. n_sentence_before: number of sentences before the matching segment to add, defaults to 2 n_sentence_after: number of sentences before the matching segment to add, defaults to 2 rerank_config: RerankConfig configuration dataclass summary_config: SummaryConfig configuration dataclass rkr3 lambda_valr<rfilterNOptional[float]score_thresholdn_sentence_beforen_sentence_after)default_factoryr6 rerank_configrsummary_configc XlX lX0lX@lU (aXlO[ 5UlU(a(XplXpl[R"S[5 O XPlX`lU (aXl gU(a@[SURURS9Ul [R"S[5 g[5Ul g)Nz[n_sentence_context is deprecated. Please use n_sentence_before and n_sentence_after insteadmmrr9r:r;z9MMRConfig is deprecated. Please use RerankConfig instead.)rBrDrErGrMrrIrJwarningswarnDeprecationWarningrLr6r1r4) selfrBrDrErGrIrJn_sentence_context mmr_configrMrLs r,__init__VectaraQueryConfig.__init__ws$ . "0 "//D  %7 "$6 ! MML"  &7 "$4 ! !.  !-#))#-#<#<"D  MMK"  ".D r+)rErBrDrJrIrLrGrM) rArCr<NrHrHNNNN)rBrrDr3rErrGrFrIrrJrrUz Optional[int]rVzOptional[MMRConfig]rMzOptional[SummaryConfig]rLzOptional[RerankConfig])r$r%r&r'r(rBr)rDrErGrIrJrr6rLrrMrWr*r#r+r,r?r?Ys&AsKJFC'+O_+sc"' "EM<E$)-$HNMH+/!" !,0*.260420 2020 20 ) 20  2020*20(20020.2020r+r?c\rSrSrSrSSSjjr\SSj5rSSjrSSjr SSSjjr SSS jjr SS S jjr S!S"S jjr S#S$S jjrS%S jrS&SjrS'SjrS(S)Sjjr\S!S*Sjj5r\S!S+Sjj5rS,SjrS,SjrS-SjrSrg).VectaraaX`Vectara API` vector store. See (https://vectara.com). Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectorstore = Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key ) NcU=(d [RRS5UlU=(d [RRS5UlU=(d [RRS5UlURbURb UR c[ RS5 O"[ RSUR35 XPl [R"5Ul [RRSS9nURRS U5 X@lg) zInitialize with Vectara API.VECTARA_CUSTOMER_IDVECTARA_CORPUS_IDVECTARA_API_KEYNzHCan't find Vectara credentials, customer_id or corpus_id in environment.zUsing corpus id ) max_retrieszhttp://)osenvironget_vectara_customer_id_vectara_corpus_id_vectara_api_keyloggerwarningdebug_sourcerequestsSession_sessionadapters HTTPAdaptermountvectara_api_timeout)rTvectara_customer_idvectara_corpus_idvectara_api_keyrrsourceadapters r,rWVectara.__init__s%8% 2::>> !< !#4# rzz~~ 8 !0 T2::>>BS3T  % % -&&.$$, NN  LL+D,C,C+DE F  ((* ##//A/> Iw/#6 r+cgNr#rTs r, embeddingsVectara.embeddingssr+cLURURSURS.$)z=Returns headers that should be attached to each post request.zapplication/json)z x-api-keyz customer-id Content-TypezX-Source)rgrerkr{s r,_get_post_headersVectara._get_post_headerss*..44.   r+c jURURUS.nURRS[R "U5SUR 5URS9nURS:wa@[RSUSURSURS UR35 g g) z Delete a document from the Vectara corpus. Args: doc_id (str): ID of the document to delete. Returns: bool: True if deletion was successful, False otherwise. ) customer_id corpus_id document_idz$https://api.vectara.io/v1/delete-docT)dataverifyheaderstimeoutz#Delete request failed for doc_id = z with status code , reason z, text F) rerfrnpostjsondumpsrrr status_coderherrorreasontext)rTdoc_idbodyresponses r, _delete_docVectara._delete_docs 4400!  ==%% 2D!**,,, &    3 & LL5fX=O''( (//1B'==/#  r+c0nURUS'URUS'XS'U(aSOSnURRUR 5U[ R "U5URSS9nURnUR 5nSU;aUSS OSnUS :Xd U(aUS :Xag U(aUS :Xagg)Nrrdocumentz$https://api.vectara.io/v1/core/indexzhttps://api.vectara.io/v1/indexT)rurlrrrstatuscodeALREADY_EXISTSE_ALREADY_EXISTS FORBIDDENE_NO_PERMISSIONS E_SUCCEEDED) rerfrnrrrrrrr) rTdoc use_core_apirequest api_endpointrrresult status_strs r, _index_docVectara._index_doc s"$!%!:!: #66 !  32  ==%%**,G$,, & ** 19V1CVH%f- # ?O1O% Z;6% r+c pU(a*UVs/sHo0RU5PM nn[U5$gs snf)zDelete by vector ID or other criteria. Args: ids: List of ids to delete. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. T)rall)rTidskwargsidsuccesss r,deleteVectara.delete(s6 69:c''+cG:w< ;s3c ./n[U5GHupV[RRU5(d[R SUS35 MEU(aX%O0nU[ US54[R"U5S.nUR5n U RS5 URRSURSURS3US U URS 9n U R S :Xa5U R5S S n [R#SUSU S35 GMU R S:Xa*U R5S S n UR%U 5 GMY[R#SUSU R535 GM U$)a  Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain Args: files_list: Iterable of strings, each representing a local file path. Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc. see API docs for full list metadatas: Optional list of metadatas associated with each file Returns: List of ids associated with each of the files indexed zFile z does not exist, skippingrb)file doc_metadatarz https://api.vectara.io/upload?c=z&o=z&d=TrueT)filesrrrrr documentIdz# already exists on Vectara (doc_id=z ), skippingrzError indexing file z: ) enumeraterbpathexistsrhropenrrrpoprnrrerfrrrinfoappend) rT files_list metadatasrdoc_idsinxrmdrrrrs r, add_filesVectara.add_files7sx(":.IC77>>$'' uTF*CDE#,"BtD$/0 $ 2E,,.G KK '}}))243L3L2MSQUQhQhPiipq00 *H##s*!4\B D6!DVHKX%%,!4\Bv& 24&8==?:KLM9/<r+c \[5nUH"nURUR55 M$ UR5nUcUVs/sHn0PM nnU(aSUS'OSS0nUR SS5n U (aSOSn SUS[ R "U5U [X5V V s/sHupU [ R "U 5S .PM sn n 0n URXS 9nUS :Xa%URU5 URU 5 U/$US :Xa [S 5 U/$s snfs sn n f)aBRun more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. doc_metadata: optional metadata for the document This function indexes all the input text strings in the Vectara corpus as a single Vectara document, where each input text is considered a "section" and the metadata are associated with each section. if 'doc_metadata' is provided, it is associated with the Vectara document. Returns: document ID of the document added langchainrvrFpartssectionr metadataJson)rr)rrrziNo permissions to add document to Vectara. Check your corpus ID, customer ID and API key) rupdateencode hexdigestrdrrziprrprint)rTtextsrrrdoc_hashtr_r section_keyrrr success_strs r, add_textsVectara.add_textsls5.5A OOAHHJ '##%  %*+UUI+ %0L "$k2Lzz.%8 !-g9 6 DJJ|4  #E 5 5HDtzz"~> 5 oocoE , ,   V $ OOC x . . A x7,s D#,$D(c [UR[5(a[S0URD6Ul[UR[5(a[ S0URD6UlSUSURR S;aURRO URURURS.URURS./S./0nURS:aSUR0USSSSS 'URR S :Xa)[S URR0S .USSS 'OlURR S:Xa'[ URR"S.USSS 'O+URR S:XaS[$0USSS 'URR&(agURR(URR*URR,S./USSS'U(aSUS.USSSSS'U$)zBuild the body for the API Args: query: Text to look up documents similar to. config: VectaraQueryConfig object Returns: A dictionary with the body of the query queryr)rOudfrerank_multilingual_v1)sentencesBeforesentencesAfter)corpusIdmetadataFilter)rstart numResults contextConfig corpusKeylambdarlexicalInterpolationConfigrO diversityBias) rerankerId mmrConfigrerankingConfigr)r userFunctionrr)maxSummarizedResults responseLangsummarizerPromptNamesummaryT)storeconversationIdchatr#) isinstancerLdictr6rMrr9r:rBrIrJrfrErDMMR_RERANKER_IDr;UDF_RERANKER_IDr=RERANKER_MULTILINGUAL_V1_IDrrr r!)rTrconfigr chat_conv_idrrs r,_get_query_bodyVectara._get_query_bodysV f**D 1 1#/#G&2F2F#GF f++T 2 2$1$JF4I4I$JF ! "#0099GH,,55 $XX,2+C+C*0*A*A& )-(?(?.4mm" 6   q &++NDM! [ )! ,-I J    ( (E 1--v/C/C/V/VW3DM! . / ! ! * *e 3- & 4 4 B B3DM! . / ! ! * *.F F93DM! . /  + +-3,A,A,M,M$*$9$9$G$G,2,A,A,M,M+DM! Y '!&2:W a +A.v6 r+c UR"X40UD6nURRUR5S[R "U5UR S9nURS:wa@[RSSURSURSURS35 /$UR 5nUR(a0US S S Vs/sHnUS UR:dMUPM nnO US S S nUS S S n /n UHkn U SV s0sH oSU S_M n n U SnXSV s0sH oSU S_M nn SU;aSUS'U RU5 U RU 5 Mm [X5V V s/sHup[!U SU S9U S 4PM nn n UR"R$S;aUSUR&nUR(R*(aCUS S SS SnUS S SS SS nUR[!USUS.S9S45 U$s snfs sn fs sn fs sn n f)zRun a Vectara query Args: query: Text to look up documents similar to. config: VectaraQueryConfig object Returns: A list of k Documents matching the given query If summary is enabled, last document is the summary text with 'summary'=True zhttps://api.vectara.io/v1/query)rrrrrQuery failed %s(code r , details ) responseSetrrscorermetadatanamevalue documentIndexrvvectarar page_contentrrOrNrfactualConsistencyT)rfcsrC)rrnrrrrrrrrhrrrrGrrrrrLr9rBrMr)rTrrrrrrr responses documentsrxmrdoc_numdoc_mdresrr s r, vectara_queryVectara.vectara_querys##E5G 5ST5Si7+5SFTv%#,x IIf    R "Y2  3 !"6'  3      ( (,M Mj/C  + +]+A.y9![S0UD6nURX5nU$)aReturn Vectara documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 10. any other querying variable in VectaraQueryConfig like: - lambda_val: lexical match parameter for hybrid search. - filter: filter string - score_threshold: minimal score threshold for the result. - n_sentence_before: number of sentences before the matching segment - n_sentence_after: number of sentences after the matching segment - rerank_config: optional configuration for Reranking (see RerankConfig dataclass) - summary_config: optional configuration for summary (see SummaryConfig dataclass) Returns: List of Documents most similar to the query and score for each. r#)r?r)rTrrrdocss r,similarity_search_with_score$Vectara.similarity_search_with_scoreGs&0$-f-!!%0 r+c `UR"U40UD6nUVVs/sHupEUPM snn$s snnf)zReturn Vectara documents most similar to query, along with scores. Args: query: Text to look up documents similar to. any other querying variable in VectaraQueryConfig Returns: List of Documents most similar to the query )r)rTrrdocs_and_scoresrrs r,similarity_searchVectara.similarity_searchcs;;;   #22//222s *c J[SUSU- S9US'UR"U40UD6$)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 5. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 50 lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. kwargs: any other querying variable in VectaraQueryConfig Returns: List of Documents selected by maximal marginal relevance. rOrPrL)r6r)rTrfetch_k lambda_multrs r,max_marginal_relevance_search%Vectara.max_marginal_relevance_searchws60#/W[# %%e6v66r+c dURS05nU"S0UD6nUR"X4SU0UD6 U$)aConstruct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_texts( texts, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) rr#)rr)clsr embeddingrrrrs r, from_textsVectara.from_textss<6zz."5 --%PPPr+c 8U"S0UD6nURX5 U$)aConstruct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_files( files_list, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) r#)r)r&rr'rrrs r, from_filesVectara.from_filess".--%+r+c[X5$)zReturn a Vectara RAG runnable. VectaraRAGrTrs r,as_ragVectara.as_rags $''r+c[XSS9$)z'Return a Vectara RAG runnable for chat.T)rr.r0s r,as_chatVectara.as_chats$T22r+c D[XRS[55S9$)zreturn a retriever object.r) vectorstorer)VectaraRetrieverrdr?)rTrs r, as_retrieverVectara.as_retrievers!ZZ:L:N%O  r+)rnrkrgrfrerr)NNNxr) rs Optional[str]rtr<rur<rrrrvr)returnOptional[Embeddings])r=r)rrr=rF)rrrrr=rrz)rzOptional[List[str]]rrr=Optional[bool])r Iterable[str]rOptional[List[dict]]rrr= List[str])NN) rrArrBrzOptional[dict]rrr=rC)FN) rrrr?rr@rr<rrr=r)rrrr?rrr=List[Tuple[Document, float]])rrrrr=rD)rrrrr=List[Document])r0g?) rrr!rr"r3rrr=rE) r& Type[Vectara]rrCr'r>rrBrrr=rZ) r&rFrrCr'r>rrBrrr=rZ)rr?r=r/)rrr=r8)r$r%r&r'r(rWpropertyr|rrrrrrrrrrr# classmethodr(r+r1r4r9r*r#r+r,rZrZs$.2+/)-#&! 7* 7) 7' 7 ! 7  7D <!< $+/3!3(3 3  3p+/'+ 77(7% 7  7  7z %&* QQ#Q Q $ Q  Q QfMM#M M & M^ & 8333  3. 777 7  7  7:+/*.  ((     >+/*.  ((     4(3 r+rZcb\rSrSr%SrS\S'S\S'\"SS9rS S jrSS jr S r g )r8izVectara Retriever class.rZr7r?rT)arbitrary_types_allowedc URR"XR40UD6nUVVs/sHupVUPM snn$s snnfrz)r7rr)rTr run_managerrrrrs r,_get_relevant_documents(VectaraRetriever._get_relevant_documentss=**88 VvV"12//222s >c <URR"U40UD6$)zAdd documents to vectorstore.)r7 add_documents)rTrrs r,rPVectaraRetriever.add_documentss--iB6BBr+r#N)rrrLrrrr=rE)rrErrr=rC) r$r%r&r'r(r)r model_configrMrPr*r#r+r,r8r8sO"+ + $L33*H3TW3 3 Cr+r8cn\rSrSrSrSS SjjrS S SjjrS S SjjrSrg) r/izVectara RAG runnable. Parameters: vectara: Vectara object config: VectaraQueryConfig object chat: bool, default False c6XlX lX0lSUlgrz)rrrconv_id)rTrrrs r,rWVectaraRAG.__init__s    r+Nc + # URRXRURUR5nURR R URR5S[R"U5URRSS9nURS:wa?[RSSURSURSURS 35 g /n/nS U0v UR!5GHnU(dM [R""UR%S 55n U S n U Sn U GccU R'SS 5n U cMW[)U R'S55S:a;[RSU R'S5SR'S535 MU R'SS 5n U (aYU R'SS 5(aBU Sn[R+SU35 US:XaS Ul[RS5 GM"U (aU R'SS 5OS nU(aXlU R'SS 5(a+U R'S05R'SS 5nSU0v GM[-U S5nSU0v GMURR.(a4U SVs/sH$nUSURR.:dM"UPM& nnOU SnU Sn/nUHnnUS Vs0sH nUS!US"_M nnUS#nUUS Vs0sH nUS!US"_M nnS$U;aS%US$'UR1U5 UR3U5 Mp [5UU5VVs/sHunn[7USUS&9US4PM nnnURR8R:S';aUS URR<nS(U0v GM g s snfs snfs snfs snnf7f))zGet streaming output from Vectara RAG. Args: input: The input query config: RunnableConfig object kwargs: Any additional arguments Returns: The output dictionary with question, answer and context z&https://api.vectara.io/v1/stream-queryT)rrrrr"rrrrrrNquestionzutf-8rrrrrz&Summary generation failed with status statusDetailrzChat query failed with code RESOURCE_EXHAUSTEDz-Sorry, Vectara chat turns exceeds plan limit.rr rr ranswerrrrrrrrvrrr context)rrrrrUrnrrrrrrrrhrrr iter_linesloadsdecoderdlenrrrGrrrrrLr9rB)rTinputrrrrrrlinerr response_setrrst_coderUr chunkr rrrrrrrs r,r"VectaraRAG.streams ||++E;; 4<<X<<((--LL2248D!LL44 .    3 & LL!--.i7H ==/$    5!!'')Dtzz$++g"67h%m4 '$jjD9G 7;;x01A5 D&{{84Q7;;NKLN!#;;vt4D4 8 8"&x. &B7)$LM"&::+/DL"LL O%BFdhh'7>DG'. {{#7>>%kk*>CGGQUV$cl*  0E#U++{{22&2*%=%%= zDKK,G,GG%="% %1$< ,Z 8I "I&=>z]K]ai73]K"#O"4&/w%7 %C"%CfIqz1%C"$61/8F8, &)!((,'&&)I%> &?EAr %-.vY)+gJ &? {{0099>"/DKKMM2$c**]*^ I%L" sEDPF8P O97O9=PO>*P;P APP-A!Pc SS0nURU5HcnSU;a USUS'MSU;a USUS'M#SU;aUS==US- ss'M;SU;a USUS'MK[RSU35 Me U$)Nr[r<r\rXr zUnknown chunk type: )r"rhr)rTrarrrres r,invokeVectaraRAG.invokes n[['EE!!&y!1Iu$"' "3JU"H x0 %"5\E  3E7;<( r+)rrrUrr?)rrZrr?rrrz)rarrOptional[RunnableConfig]rrr=zIterator[dict])rarrrjrrr=r) r$r%r&r'r(rWr"rhr*r#r+r,r/r/sJO(:BF,0vv)v v  vv,0)   r+r/)/ __future__rrloggingrbrQ dataclassesrrhashlibrtypingrrr r r r r rl langchain_core.callbacks.managerrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.runnablesrrlangchain_core.vectorstoresrrpydanticr getLoggerr$rhrrrrr.r6r?rZr8r/r#r+r,rws"  (GGG.0=I   8 $'  "     (  * O0O0 O0do ko dC+C0[[r+