dhZSSKJr SSKrSSKrSSKJrJr SSKJrJ r J r J r J r J r Jr SSKJr SSKJr SSKJr \(a SSKJr SSKJr \R0"5r\"S S 55r"S S \5rg) ) annotationsN)asdict dataclass) TYPE_CHECKINGAnyDictIterableListOptionalTupleDocument) Embeddings) VectorStore)DocumentCollectioncL\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S rg)CollectionConfigaVConfiguration for a `Zep Collection`. If the collection does not exist, it will be created. Attributes: name (str): The name of the collection. description (Optional[str]): An optional description of the collection. metadata (Optional[Dict[str, Any]]): Optional metadata for the collection. embedding_dimensions (int): The number of dimensions for the embeddings in the collection. This should match the Zep server configuration if auto-embed is true. is_auto_embedded (bool): A flag indicating whether the collection is automatically embedded by Zep. strname Optional[str] descriptionOptional[Dict[str, Any]]metadataintembedding_dimensionsboolis_auto_embeddedN)__name__ __module__ __qualname____firstlineno____doc____annotations____static_attributes__r\/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/zep.pyrrs&  I&&r'rcB^\rSrSrSrSSSS.SU4Sjjjr\SSj5rSSjrSSjr SS S jjr SS!S jjr SS!S jjr S"S#S jjr S"S#S jjrS$S%SjjrS$S&SjjrS$S&SjjrS$S&SjjrS$S%SjjrS$S'SjjrS$S'SjjrS(S)SjjrS(S)SjjrS(S*SjjrS(S*Sjjr\S+S,Sjj5rS-S.SjjrSrU=r$)/ZepVectorStore,a`Zep` vector store. It provides methods for adding texts or documents to the store, searching for similar documents, and deleting documents. Search scores are calculated using cosine similarity normalized to [0, 1]. Args: api_url (str): The URL of the Zep API. collection_name (str): The name of the collection in the Zep store. api_key (Optional[str]): The API key for the Zep API. config (Optional[CollectionConfig]): The configuration for the collection. Required if the collection does not already exist. embedding (Optional[Embeddings]): Optional embedding function to use to embed the texts. Required if the collection is not auto-embedded. Napi_keyconfig embeddingcN>[TU]5 U(d [S5eSSKJn U"X#S9UlXlU(a+URUR:waURUlX@l UR5Ul XPl g![ a [ S5ef=f)Nzs N   , !: . fkkT%9%99..FK"(002# C  s BB$cUR$)z/Access the query embedding object if available.)r<)r=s r( embeddingsZepVectorStore.embeddingsasr'cSSKJn URRR UR 5nU$!Ua7 [ RSUR S35 UR5nU$f=f)z+ Load the collection from the Zep backend. r) NotFoundErrorz Collection z$ not found. Creating new collection.) r5rEr7documentget_collectionr8loggerinfo_create_collection)r=rE collections r(r:ZepVectorStore._load_collectionfs{ - 3..==d>R>RSJ  3 KKd2233WX 002J  3s/99A65A6cUR(d [S5eURRR"S0[ UR5D6nU$)z- Create a new collection in the Zep backend. zCCollection config must be specified when creating a new collection.r)r9r4r7rFadd_collectionr)r=rKs r(rJ!ZepVectorStore._create_collectionvsT&&U \\**99 T,,- r'c SSKJn SnUR(a>URR(a#URb[ R "SSS9 OURbURR[U55nUR(aWURR[US5:wa1[SURRS[US535eO/n[U5H>upxURU"UU(aX'OSU(aX7OSU(aXWOSS95 M@ U$) Nrr z{The collection is set to auto-embed and an embedding function is present. Ignoring the embedding function.) stacklevelzkThe embedding dimensions of the collection and the embedding function do not match. Collection dimensions: z, Embedding dimensions: )contentr document_idr/)zep_python.documentrr;rr<warningswarnembed_documentslistrlenr4 enumerateappend) r=texts metadatas document_ids ZepDocumentrB documentsids r(_generate_documents_to_add)ZepVectorStore._generate_documents_to_adds* @    0 0 A A* I  __ (88eEJD$4$4$I$IS1 N%!((==>?JqM*+- ') e$DA   -6Y\D3? T/9jmt  %r'c UR(d [S5eURXU5nURRU5nU$)aRun 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. document_ids: Optional list of document ids associated with the texts. kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. Return docs most similar to query using specified search type. similaritykrmmrsearch_type of ? not allowed. Expected search_type to be 'similarity' or 'mmr'.)similarity_searchmax_marginal_relevance_searchr4r=query search_typerrvrjs r(searchZepVectorStore.searchsu , &))%R1R6R R E !5517 !+/;; r'c# US:XaUR"U4XCS.UD6IShvN $US:XaUR"U4XCS.UD6IShvN $[SUS35eN6N7f)rsrtruNrwrxry)asimilarity_searchamax_marginal_relevance_searchr4r|s r(asearchZepVectorStore.asearchs , &0017 E !<<17 !+/;; s!AA"AAAAc dUR"U4X#S.UD6nUVVs/sHupgUPM snn$s snnf)"Return docs most similar to query.ru(_similarity_search_with_relevance_scoresr=r}rvrrjresultsdoc_s r(rz ZepVectorStore.similarity_searchsC??   -3 #**''***s ,c ,UR"U4X#S.UD6$)z$Run similarity search with distance.rur)r=r}rvrrjs r(similarity_search_with_score+ZepVectorStore.similarity_search_with_scores*<<   -3  r'c UR(d [S5eURR(dLUR(a;URR U5nURR "SXRUS.UD6nOURR "U4X#S.UD6nUVs/sH6n[ URURS9UR=(d S4PM8 sn$s snf)a, Default similarity search with relevance scores. Modify if necessary in subclass. Return docs and relevance scores in the range [0, 1]. 0 is dissimilar, 1 is most similar. Args: query: input text k: Number of Documents to return. Defaults to 4. metadata: Optional, metadata filter **kwargs: kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 and filter the resulting set of retrieved docs Returns: List of Tuples of (doc, similarity_score) rgr/limitrrr page_contentrr) r;r4rr< embed_queryrrrSrscorer=r}rvrrj query_vectorrrs r(r7ZepVectorStore._similarity_search_with_relevance_scores"s4N 00T__??66u=L&&--&(FLG&&--5;G   !$ \\  S     s'6 > 8>>c UR(d [S5eURR"SXUS.UD6nUVs/sH!n[URUR S9PM# sn$s snf)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. metadata: Optional, metadata filter Returns: List of Documents most similar to the query vector. rgrrrr;r4rrrSrr=r/rvrrjrrs r(similarity_search_by_vector*ZepVectorStore.similarity_search_by_vectors"N "")) 8 ?E     [[     s'A*c# UR(d [S5eURR"SXUS.UD6nUVs/sH!n[URUR S9PM# sn$s snf7f)z-Return docs most similar to embedding vector.rgrrrrrs r(asimilarity_search_by_vector+ZepVectorStore.asimilarity_search_by_vectorsN "")) 8 ?E     [[     sAA1'A,)A1c UR(d [S5eURR(dOUR(a>URR U5nURR "SUUUSUS.UD6nO$URR "U4UUSUS.UD6upUV s/sH!n [U RU RS9PM# sn $s sn f)a/Return 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. Zep determines this automatically and this parameter is ignored. 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. metadata: Optional, metadata to filter the resulting set of retrieved docs Returns: List of Documents selected by maximal marginal relevance. rgrwr/rrr~ mmr_lambdarrr~rrr) r;r4rr<rrsearch_return_query_vectorrrSr r=r}rvfetch_k lambda_multrrjrrrcs r(r{,ZepVectorStore.max_marginal_relevance_searchs:N 00T__??66u=L&&--&!!&   G%)$4$4$O$O%!!& %  % !GPWWw!aii!**EwWWWs/'Cc # UR(d [S5eURR(dWUR(aFURR U5nURR "SUUUSUS.UD6IShvN nO,URR "U4UUSUS.UD6IShvN upUV s/sH!n [U RU RS9PM# sn $N_N6s sn f7f):Return docs selected using the maximal marginal relevance.rgrwrNrrr) r;r4rr<rrasearch_return_query_vectorrrSrrs r(r-ZepVectorStore.amax_marginal_relevance_searchsN 00T__??66u=L ,,44&!!&   G+/*:*:*V*V+!!& +  +% !GPWWw!aii!**EwWW%%Xs6B C4 C+ *C46C-7 C4'C/(C4-C4/C4c UR(d [S5eURR"SUUUSUS.UD6nUVs/sH!n[URUR S9PM# sn$s snf)a8Return 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. Zep determines this automatically and this parameter is ignored. 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. metadata: Optional, metadata to filter the resulting set of retrieved docs Returns: List of Documents selected by maximal marginal relevance. rgrwrrrr r=r/rvrrrrjrrcs r('max_marginal_relevance_search_by_vector6ZepVectorStore.max_marginal_relevance_search_by_vector!s~8N "")) "    PWWw!aii!**EwWWWs'A-c # UR(d [S5eURR"SUUUSUS.UD6IShvN nUVs/sH!n[URUR S9PM# sn$N2s snf7f)rrgrwrNrr)r;r4rrrSrrs r((amax_marginal_relevance_search_by_vector7ZepVectorStore.amax_marginal_relevance_search_by_vectorMsN ((00 "     PWWw!aii!**EwWW Xs$AA>A7A> 'A94A>9A>c <U"UUUUUS9n U RX5 U $)a Class method that returns a ZepVectorStore instance initialized from texts. If the collection does not exist, it will be created. Args: texts (List[str]): The list of texts to add to the vectorstore. embedding (Optional[Embeddings]): Optional embedding function to use to embed the texts. metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata associated with the texts. collection_name (str): The name of the collection in the Zep store. api_url (str): The URL of the Zep API. api_key (Optional[str]): The API key for the Zep API. config (Optional[CollectionConfig]): The configuration for the collection. kwargs: Additional parameters specific to the vectorstore. Returns: ZepVectorStore: An instance of ZepVectorStore. r,)rl) clsr]r/r^r8r>r-r.rjvecstores r( from_textsZepVectorStore.from_textsgs3@     5,r'c Ub[U5S:Xa [S5eURc [S5eUHnURRU5 M g)zDelete by Zep vector UUIDs. Parameters ---------- ids : Optional[List[str]] The UUIDs of the vectors to delete. Raises ------ ValueError If no UUIDs are provided. NrzNo uuids provided to delete.zNo collection name provided.)rZr4r;delete_document)r=idsrjus r(deleteZepVectorStore.deletesU ;#c(a-;< <    #;< <A    , ,Q /r')r7r;r9r<r8) r8rr>rr-rr.Optional[CollectionConfig]r/Optional[Embeddings]returnNone)rr)rr)NN)r] Iterable[str]r^zOptional[List[Dict[Any, Any]]]r_Optional[List[str]]rzList[ZepDocument]) r]rr^zOptional[List[Dict[str, Any]]]r_rrjrr List[str])N) r}rr~rrrrvrrjrrList[Document])N) r}rrvrrrrjrrr) r}rrvrrrrjrrzList[Tuple[Document, float]]) r/ List[float]rvrrrrjrrr)rrg?N)r}rrvrrrrfloatrrrjrrr)r/rrvrrrrrrrrjrrr)NNrNN)r]rr/rr^zOptional[List[dict]]r8rr>rr-rr.rrjrrr*)N)rrrjrrr)r r!r"r#r$r3propertyrBr:rJrdrlrprrrzrrrrrrr{rrr classmethodrrr& __classcell__)r?s@r(r*r*,sj,"&-1*.$$$  $ + $($ $$F  59,0 ((2(* (  (Z59,0 2*     >59,0 2*     ,.2 +      2.2 +      4-1 + +  ++ +  +  +"-1       +    &  -1 2 2  2 + 2  2 & 2 n-1 ! !  ! + !  ! & ! L-1 + +  ++ +  +  +$-1       +      J-1     +      8 -1 6X6X 6X 6X  6X + 6X6X 6Xv -1 $X$X $X $X  $X + $X$X $XR -1 *X*X *X *X  *X + *X*X *X^ -1 XX X X  X + XX X4+/*.!!%-1''('( '  '  ''+'' ''R00r'r*) __future__rloggingrV dataclassesrrtypingrrrr r r r langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresrrUr`r getLoggerrHrr*rr'r(rsc")LLL-03;6      .z 0[z 0r'