jZddlmZddlZddlZddlmZmZddlmZm Z m Z m Z m Z m Z mZddlmZddlmZddlmZer ddlmZddlmZej0ZeGd d ZGd d eZy) ) annotationsN)asdict dataclass) TYPE_CHECKINGAnyDictIterableListOptionalTupleDocument) Embeddings) VectorStore)DocumentCollectioncDeZdZUdZded<ded<ded<ded <d ed <y ) CollectionConfigaConfiguration 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____doc____annotations__n/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/vectorstores/zep.pyrrs&  I&&r$rceZdZdZdddd dfdZeddZddZddZ d ddZ d dd Z d dd Z d d!d Z d d!d Z d" d#d Z d" d$dZ d" d$dZ d" d$dZ d" d#dZ d" d%dZ d" d%dZ d& d'dZ d& d'dZ d& d(dZ d& d(dZe d) d*dZd+d,dZxZS)-ZepVectorStorea`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 embeddingcDt||s td ddlm}||||_||_|r*|j|jk7r|j|_||_ |j|_ ||_ y#t $r t dwxYw)Nzs N   , !': . fkkT%9%99..FK"(002# C  s B Bc|jS)z/Access the query embedding object if available.)r8)r9s r% embeddingszZepVectorStore.embeddingsasr$cddlm} |jjj |j }|S#|$r7t jd|j d|j}Y|SwxYw)z; Load the collection from the Zep backend. r) NotFoundErrorz Collection z$ not found. Creating new collection.) r1r?r3documentget_collectionr4loggerinfo_create_collection)r9r? collections r%r6zZepVectorStore._load_collectionfs{ - 3..==d>R>RSJ  3 KKd2233WX 002J  3s/98A54A5c|js td|jjjdit |j}|S)z= Create a new collection in the Zep backend. zCCollection config must be specified when creating a new collection.r#)r5r0r3r@add_collectionr)r9rEs r%rDz!ZepVectorStore._create_collectionvsV&&U :T\\**99 T,,- r$c Rddlm}d}|jr:|jjr$|jt j ddn|j|jjt|}|jrW|jjt|dk7r2td|jjdt|d g}t|D]5\}}|j|||r||nd|r||nd|r||nd7|S) 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.documentrr7rr8warningswarnembed_documentslistrlenr0 enumerateappend) r9texts metadatas document_ids ZepDocumentr= documentsids r%_generate_documents_to_addz)ZepVectorStore._generate_documents_to_adds0 @    0 0 A A* I  __ (88eEJD$4$4$I$IS1 N%!((==>?JqM*+- ') e$DAq   -6Yq\D3? QT/9jmt  %r$c |js td|j|||}|jj|}|S)aRun more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. document_ids: Optional list of document ids associated with the texts. kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. Return docs most similar to query using specified search type. similaritykrmmrsearch_type of ? not allowed. Expected search_type to be 'similarity' or 'mmr'.)similarity_searchmax_marginal_relevance_searchr0r9query search_typerrkras r%searchzZepVectorStore.searchs} , &)4))%R1xR6R R E !5455X17 !+/;; r$cK|dk(r|j|f||d|d{S|dk(r|j|f||d|d{Std|d767w)rhrirjNrlrmrn)asimilarity_searchamax_marginal_relevance_searchr0rqs r%asearchzZepVectorStore.asearchs , &000X17 E !<<<X17 !+/;; s!AA"AAAAc d|j|f||d|}|Dcgc]\}}| c}}Scc}}w)"Return docs most similar to query.rj(_similarity_search_with_relevance_scoresr9rrrkrraresultsdoc_s r%roz ZepVectorStore.similarity_searchsJ@$??   -3 #**'Q'***s ,c .|j|f||d|S)z$Run similarity search with distance.rjr{)r9rrrkrras r%similarity_search_with_scorez+ZepVectorStore.similarity_search_with_scores.=t<<   -3  r$c |js td|jjsH|jr<|jj |}|jj d|||d|}n |jj |f||d|}|Dcgc]3}t |j|j|jxsdf5c}Scc}w)a Default similarity search with relevance scores. Modify if necessary in subclass. Return docs and relevance scores in the range [0, 1]. 0 is dissimilar, 1 is most similar. Args: query: input text k: Number of Documents to return. Defaults to 4. metadata: Optional, metadata filter **kwargs: kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 and filter the resulting set of retrieved docs Returns: List of Tuples of (doc, similarity_score) r^r+limitrrr page_contentrr#) r7r0rr8 embed_queryrtrrKrscorer9rrrkrra query_vectorr~rs r%r|z7ZepVectorStore._similarity_search_with_relevance_scores"s4N 00T__??66u=L-d&&--&a(FLG.d&&--5;G   !$ \\  S     s8CcK|js td|jjsP|jrD|jj |}|jj d|||d|d{}n(|jj |f||d|d{}|Dcgc]3}t |j|j|jxsdf5c}S7l7Ecc}ww)rzr^rNrrrr#) r7r0rr8rrxrrKrrrs r%(asimilarity_search_with_relevance_scoresz7ZepVectorStore.asimilarity_search_with_relevance_scoresVs N 00T__??66u=L4D,,44&a(FLG5D,,445;G   !$ \\  S      s6A8C0:C';(C0#C)$C0,8C+$C0)C0+C0cK|j||fd|i|d{}|Dcgc]\}}| c}}S7cc}}ww)rzrN)rr}s r%rvz!ZepVectorStore.asimilarity_searchysZFEE 1 ' +1  #**'Q'**  +s>6 > 8>>c |js td|jjd|||d|}|Dcgc]#}t|j|j %c}Scc}w)aFReturn 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. r^rrr#r7r0rtrrKrr9r+rkrrar~rs r%similarity_search_by_vectorz*ZepVectorStore.similarity_search_by_vectors"N *$"")) q8 ?E     [[     s(A'cK|js td|jjd|||d|}|Dcgc]#}t|j|j %c}Scc}ww)z-Return docs most similar to embedding vector.r^rrr#rrs r%asimilarity_search_by_vectorz+ZepVectorStore.asimilarity_search_by_vectorsN *$"")) q8 ?E     [[     s|jj |}|jj d|||d|d|}n%|jj |f||d|d|\}}|D cgc]#} t| j| j%c} Scc} w)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. 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. r^rlr+rrrs mmr_lambdarrrsrrr#) r7r0rr8rrtsearch_return_query_vectorrrKr r9rrrkfetch_k lambda_multrrarr~r[s r%rpz,ZepVectorStore.max_marginal_relevance_searchs:N 00T__??66u=L-d&&--&!!&   G%PD$4$4$O$O%!!& %  % !G\PWWw!aii!**EwWWWs!(C c K|js td|jjsR|jrF|jj |}|jj d|||d|d|d{}n-|jj |f||d|d|d{\}}|D cgc]#} t| j| j%c} S7a78cc} ww):Return docs selected using the maximal marginal relevance.r^rlrNrrr#) r7r0rr8rrxasearch_return_query_vectorrrKrrs r%rwz-ZepVectorStore.amax_marginal_relevance_searchsN 00T__??66u=L4D,,44&!!&   G+W$*:*:*V*V+!!& +  +% !G\PWWw!aii!**EwWW%%Xs6A:C'<C=*C''C ( C'3(C"C' C'"C'c |js td|jjd|||d|d|}|Dcgc]#}t|j|j %c}Scc}w)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. 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. r^rlrrr#r r9r+rkrrrrar~r[s r%'max_marginal_relevance_search_by_vectorz6ZepVectorStore.max_marginal_relevance_search_by_vector!s8N *$"")) "    PWWw!aii!**EwWWWs(A)c K|js td|jjd|||d|d|d{}|Dcgc]#}t|j|j %c}S73cc}ww)rr^rlrNrr#)r7r0rxrrKrrs r%(amax_marginal_relevance_search_by_vectorz7ZepVectorStore.amax_marginal_relevance_search_by_vectorMsN 1((00 "     PWWw!aii!**EwWW Xs"=A:A3A:(A50A:5A:c D||||||} | j||| S)a Class method that returns a ZepVectorStore instance initialized from texts. If the collection does not exist, it will be created. Args: texts (List[str]): The list of texts to add to the vectorstore. embedding (Optional[Embeddings]): Optional embedding function to use to embed the texts. metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata associated with the texts. collection_name (str): The name of the collection in the Zep store. api_url (str): The URL of the Zep API. api_key (Optional[str]): The API key for the Zep API. config (Optional[CollectionConfig]): The configuration for the collection. kwargs: Additional parameters specific to the vectorstore. Returns: ZepVectorStore: An instance of ZepVectorStore. r()rc) clsrUr+rVr4r:r)r*ravecstores r% from_textszZepVectorStore.from_textsgs5@     5),r$c |t|dk(r td|j td|D]}|jj|y)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.)rRr0r7delete_document)r9idsraus r%deletezZepVectorStore.deletesU ;#c(a-;< <    #;< <A    , ,Q /r$) r4rr:rr)rr*Optional[CollectionConfig]r+Optional[Embeddings]returnNone)rr)rr)NN)rU Iterable[str]rVzOptional[List[Dict[Any, Any]]]rWOptional[List[str]]rzList[ZepDocument]) rUrrVzOptional[List[Dict[str, Any]]]rWrrarr List[str])N) rrrrsrrrrkrrarrList[Document])N) rrrrkrrrrarrr) rrrrkrrrrarrzList[Tuple[Document, float]]) r+ List[float]rkrrrrarrr)rg?N)rrrrkrrrrfloatrrrarrr)r+rrkrrrrrrrrarrr)NNrNN)rUrr+rrVzOptional[List[dict]]r4rr:rr)rr*rrarrr')N)rrrarrr)rrr r!r/propertyr=r6rDr\rcrfrtrxrorr|rrvrrrprwrr classmethodrr __classcell__)r;s@r%r'r',se,"&-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__rloggingrN dataclassesrrtypingrrrr r r r langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresrrMrXr getLoggerrBrr'r#r$r%rse")LLL-03;6      .z 0[z 0r$