NgZddlmZddlZddlZddlmZmZddlmZm Z m Z m Z m Z m Z mZddlmZddlmZddlmZer ddlmZddlmZejZeGd d ZGd d eZdS) ) annotationsN)asdict dataclass) TYPE_CHECKINGAnyDictIterableListOptionalTupleDocument) Embeddings) VectorStore)DocumentCollectioncFeZdZUdZded<ded<ded<ded <d ed <d S) 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__`/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/zep.pyrrsW  III&&&&r$rcveZdZdZdddddDfdZedEdZdFdZdFdZ dGdHdZ dGdId Z dGdId!Z dJdKd*Z dJdKd+Z dLdMd-Z dLdNd/Z dLdNd0Z dLdNd1Z dLdMd2Z dLdOd4Z dLdOd5Z dPdQd;Z dPdQd<Z dPdRd=Z dPdRd>Ze dSdTdAZdUdVdCZxZS)WZepVectorStorea`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 embeddingcollection_namerapi_urlr)rr*Optional[CollectionConfig]r+Optional[Embeddings]returnNonecpt|std ddlm}n#t $rt dwxYw||||_||_|r|j|jkr |j|_||_ | |_ ||_ dS)Nzs  N   , , , , , , ,   C   !y'::: .  /fkT%999.FK"(0022#s ;Ac|jS)z/Access the query embedding object if available.)r=)r>s r% embeddingszZepVectorStore.embeddingsas r$rcddlm} |jj|j}nB#|$r:t d|jd|}YnwxYw|S)z; Load the collection from the Zep backend. r) NotFoundErrorz Collection z$ not found. Creating new collection.) r7rCr9documentget_collectionr,loggerinfo_create_collection)r>rC collections r%r;zZepVectorStore._load_collectionfs -,,,,, 3.==d>RSSJJ 3 3 3 KKXd2XXX   0022JJJ  3 s$-rIs r%rHz!ZepVectorStore._create_collectionvs_& U :T\*9  T,--  r$texts Iterable[str] metadatasOptional[List[Dict[Any, Any]]] document_idsOptional[List[str]]List[ZepDocument]c >ddlm}d}|jr*|jjr|jt jddn|j|jt|}|jrU|jj t|dkr2td|jj dt|dn g}t|D]E\}}| |||r||nd|r||nd|r||ndF|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.documentrr<rr=warningswarnembed_documentslistrlenr6 enumerateappend) r>rLrNrP ZepDocumentrA documentsids r%_generate_documents_to_addz)ZepVectorStore._generate_documents_to_adds @?????    0 A * I  _ (88eEEJ D$4$IS1 NN%%!-(=--JqM**-- ') e$$  DAq    -6@Yq\\D3? I QT/9Cjmmt     r$Optional[List[Dict[str, Any]]]kwargsr List[str]c |jstd||||}|j|}|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. rLrNrPrfrauuidss r% add_textszZepVectorStore.add_textssX$ N 33E9lSS  ..y99 r$cK|jstd||||}|j|d{V}|S)zARun more texts through the embeddings and add to the vectorstore.riN)r<r6rdaadd_documentsrks r% aadd_textszZepVectorStore.aadd_textssn N 33E9lSS &55i@@@@@@@@ r$query search_typerrkrList[Document]c |dkr|j|f||d|S|dkr|j|f||d|Std|d)>Return docs most similar to query using specified search type. similarityrtrmmrsearch_type of ? not allowed. Expected search_type to be 'similarity' or 'mmr'.)similarity_searchmax_marginal_relevance_searchr6r>rrrsrrtrfs r%searchzZepVectorStore.searchs , & &)4)%R1xRR6RR R E ! !545X17 ;+;;; r$cK|dkr|j|f||d|d{VS|dkr|j|f||d|d{VStd|d)rwrxryNrzr{r|)asimilarity_searchamax_marginal_relevance_searchr6rs r%asearchzZepVectorStore.asearchs , & &00X17 E ! !<<X17 ;+;;; r$c <|j|f||d|}d|DS)"Return docs most similar to query.rycg|]\}}|Sr#r#.0doc_s r% z4ZepVectorStore.similarity_search..***Q***r$(_similarity_search_with_relevance_scoresr>rrrtrrfresultss r%r}z ZepVectorStore.similarity_searchsI@$?    -3  +*'****r$List[Tuple[Document, float]]c $|j|f||d|S)z$Run similarity search with distance.ryr)r>rrrtrrfs r%similarity_search_with_scorez+ZepVectorStore.similarity_search_with_scores5=t<    -3   r$c |jstd|jjs8|jr1|j|}|jjd|||d|}n|jj|f||d|}d|DS)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) rir+limitrrrcVg|]&}t|j|j|jpdf'S page_contentrgrrVrscorerrs r%rzKZepVectorStore._similarity_search_with_relevance_scores..KT     !$ \  S      r$r#)r<r6rr= embed_queryrr>rrrtrrf query_vectorrs r%rz7ZepVectorStore._similarity_search_with_relevance_scores"s4 N 0 T_ ?66u==L-d&-&a(FLGG.d&-5;G       r$cK|jstd|jjs>|jr7|j|}|jjd|||d|d{V}n|jj|f||d|d{V}d|DS)rrirNrcVg|]&}t|j|j|jpdf'Srrrs r%rzKZepVectorStore.asimilarity_search_with_relevance_scores..nrr$r#)r<r6rr=rrrs r%(asimilarity_search_with_relevance_scoresz7ZepVectorStore.asimilarity_search_with_relevance_scoresVs N 0 T_ ?66u==L4D,4&a(FLGG5D,45;G       r$cLK|j||fd|i|d{V}d|DS)rrNcg|]\}}|Sr#r#rs r%rz5ZepVectorStore.asimilarity_search..rr$)rrs r%rz!ZepVectorStore.asimilarity_searchyskFE 1  ' +1        +*'****r$ List[float]c r|jstd|jjd|||d|}d|DS)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. rircDg|]}t|j|jSrrrVrrs r%rz>ZepVectorStore.similarity_search_by_vector..C     [      r$r#r<r6rr>r+rtrrfrs r%similarity_search_by_vectorz*ZepVectorStore.similarity_search_by_vectorsy" N *$") q8  ?E         r$cvK|jstd|jjd|||d|}d|DS)z-Return docs most similar to embedding vector.rircDg|]}t|j|jSrrrs r%rz?ZepVectorStore.asimilarity_search_by_vector..rr$r#rrs r%asimilarity_search_by_vectorz+ZepVectorStore.asimilarity_search_by_vectors} N *$") q8  ?E         r$?fetch_k lambda_multfloatc |jstd|jjs:|jr3|j|}|jjd|||d|d|}n|jj|f||d|d|\}}d|DS)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. rirzr+rrrs mmr_lambdarrrsrcDg|]}t|j|jSrrrrcs r%rz@ZepVectorStore.max_marginal_relevance_search..)WWW!ai!*EEEWWWr$r#)r<r6rr=rrsearch_return_query_vector r>rrrtrrrrfrrs r%r~z,ZepVectorStore.max_marginal_relevance_searchs: N 0 T_ ?66u==L-d&-&!!&   GG%PD$4$O%!!& %%  %% !G\XWwWWWWr$c $K|jstd|jjs@|jr9|j|}|jjd|||d|d|d{V}n!|jj|f||d|d|d{V\}}d|DS):Return docs selected using the maximal marginal relevance.rirzrNrcDg|]}t|j|jSrrrs r%rzAZepVectorStore.amax_marginal_relevance_search..rr$r#)r<r6rr=rrasearch_return_query_vectorrs r%rz-ZepVectorStore.amax_marginal_relevance_searchs' N 0 T_ ?66u==L4D,4&!!&   GG+W$*:*V+!!& ++  ++%%%%%% !G\XWwWWWWr$c v|jstd|jjd|||d|d|}d|DS)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. rirzrcDg|]}t|j|jSrrrs r%rzJZepVectorStore.max_marginal_relevance_search_by_vector..Krr$r#rr>r+rtrrrrfrs r%'max_marginal_relevance_search_by_vectorz6ZepVectorStore.max_marginal_relevance_search_by_vector!sw8 N *$") "      XWwWWWWr$c K|jstd|jjd|||d|d|d{V}d|DS)rrirzrNcDg|]}t|j|jSrrrs r%rzKZepVectorStore.amax_marginal_relevance_search_by_vector..err$r#)r<r6rrs r%(amax_marginal_relevance_search_by_vectorz7ZepVectorStore.amax_marginal_relevance_search_by_vectorMs N 1(0 "            XWwWWWWr$Optional[List[dict]]c R||||||} | ||| 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()rm) clsrLr+rNr,r-r)r*rfvecstores r% from_textszZepVectorStore.from_textsgsG@3       5),,,r$idsc |t|dkrtd|jtd|D]}|j|dS)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.)r]r6r<delete_document)r>rrfus r%deletezZepVectorStore.deletesq ;#c((a--;<< <   #;<< < 0 0A   , ,Q / / / / 0 0r$) r,rr-rr)rr*r.r+r/r0r1)r0r/)r0r)NN)rLrMrNrOrPrQr0rR) rLrMrNrerPrQrfrr0rg)Nrq) rrrrsrrrrtrrfrr0ru)rN) rrrrtrrrrfrr0ru) rrrrtrrrrfrr0r) r+rrtrrrrfrr0ru)rrrN)rrrrtrrrrrrrrfrr0ru)r+rrtrrrrrrrrfrr0ru)NNrrNN)rLrgr+r/rNrr,rr-rr)rr*r.rfrr0r')N)rrQrfrr0r1)rrr r!r5propertyrAr;rHrdrmrprrr}rrrrrrr~rrr classmethodrr __classcell__)r?s@r%r'r',s;,"&-1*.$$$$$$$$FX     59,0 (((((Z59,0 >59,0 ,.2 2.2 4-1 + + + + +"-1      -1 2 2 2 2 2 n-1 ! ! ! ! ! L-1 + + + + +$-1      J-1      8 -1 6X6X6X6X6Xv -1 $X$X$X$X$XR -1 *X*X*X*X*X^ -1 XXXXX4+/*.!!%-1''''['R000000000r$r') __future__rloggingrY dataclassesrrtypingrrrr r r r langchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresrrXr`r getLoggerrFrr'r#r$r%rs]""""""))))))))LLLLLLLLLLLLLLLLLL------0000003333337;;;;;;666666      .z 0z 0z 0z 0z 0[z 0z 0z 0z 0z 0r$