hWLddlmZddlZddlZddlZddlmZmZmZm Z m Z m Z m Z ddl ZddlmZddlmZddlmZddlmZddlmZd Zd Zd Zd Zd ZdZdZGddeZ y)) annotationsN)AnyDictListOptionalSequenceTupleType)Document) Embeddings)UpsertResponse) VectorStore)overrideHNSWCS langchainilc_textuniqueidc&eZdZeedddej df dfd ZddZedddZ eddZ e d ddZ e ddZ ddZ d dd Ze d dd Ze d d d Ze d d!d Zee d d"d Zee d#dZed$dZeddZd%dZd&dZxZS)' ApertureDBNc  ddlm} m} t |di|t jt|_ |jj|||_ ||_ ||_ ||_||_||_|&|jj#dt%d ddlm} m} | |_| |j(|_ |j*j-|j1y#t$r tdwxYw#t$r|jj'dwxYw#t.$r|jj'dwxYw)aCreate a vectorstore backed by ApertureDB A single ApertureDB instance can support many vectorstores, distinguished by 'descriptor_set' name. The descriptor set is created if it does not exist. Different descriptor sets can use different engines and metrics, be supplied by different embedding models, and have different dimensions. See ApertureDB documentation on `AddDescriptorSet` https://docs.aperturedata.io/query_language/Reference/descriptor_commands/desc_set_commands/AddDescriptorSet for more information on the engine and metric options. Args: embeddings (Embeddings): Embeddings object descriptor_set (str, optional): Descriptor set name. Defaults to "langchain". dimensions (Optional[int], optional): Number of dimensions of the embeddings. Defaults to None. engine (str, optional): Engine to use. Defaults to "HNSW" for new descriptorsets. metric (str, optional): Metric to use. Defaults to "CS" for new descriptorsets. log_level (int, optional): Logging level. Defaults to logging.WARN. rUtilscreate_connectorzMApertureDB is not installed. Please install it using 'pip install aperturedb'NzNo embedding function provided.zFailed to connect to ApertureDB)aperturedb.Utilsrr ImportErrorsuper__init__logging getLogger__name__loggersetLeveldescriptor_setembedding_function dimensionsenginemetric propertiesfatal ValueError exception connectionutilsstatus Exception_find_or_add_descriptor_set) self embeddingsr&r(r)r* log_levelr+kwargsrr __class__s i/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain_community/vectorstores/aperturedb.pyr zApertureDB.__init__sPJ  @ "6"''1  Y',",$  $   KK  ? @>? ?  @+,4??+   JJ    ((*K +  ,  KK ! !+      KK ! !"C D  s#C=&DD>=D&D;>&E$cJ|j} d|dddddidig}|jj|\}}|jjsJ|d|ddvrt |dddnd}|dksJd|dk(r|dddd}|j j d |d |d }t |dk(sJd |j |d|_n@|j|dk7r.|j jd |jd|d|d}t |dk(sJd|j |d|_ n@|j|dk7r.|j jd|jd|d|d} |j| |_ n:|j| k7r+|j jd|jd| |jD cic]-\} } | jtr| t td| /c} } |_y|j j d |d|j t|_|j t |_ |j)t |j"j%d|_ |j5|jjD cic]\} } t| z| c} } nd} |j&j)||j|j|j| |j&j+dd|j&j+dd|j&j+dt,ycc} } wcc} } w)NFindDescriptorSetTall_properties) with_nameenginesmetricsr(resultsentitiesrz+Multiple descriptor sets with the same namezDescriptor set z already exists_engineszOnly one engine is supportedzEngine mismatch: z != _metricszOnly one metric is supportedzMetric mismatch: _dimensionszDimensions mismatch: z does not exist. Creating ittest)namedimr)r*r+ _Descriptor _create_txn_DescriptorSet_name)r&r/query last_query_oklenr$infor)errorr*r(items startswithPROPERTY_PREFIXr+ENGINEMETRICr' embed_queryr0add_descriptorsetcreate_entity_indexUNIQUEID_PROPERTY) r4r& find_ds_queryrb n_entitieser>r?r(kvr+s r9r3z&ApertureDB._find_or_add_descriptor_setls,,E$!/##"& 0$7 &   $$]31,,.11.QqT"566 !()*5 6  QM MM ?!()*5a8A KK  ~.>oN O mGw<1$ D&D D${{"%aj  * !!$5dkk]$wqzl"ST mGw<1$ D&D D${{"%aj  * !!$5dkk]$wqzl"ST=)J&",J. !!+DOO+ /t// 1NtNvNNrlcd|jj|}|j|g|i|SN)r'rW'_similarity_search_with_score_by_vector)r4rMrvr7rws r9similarity_search_with_scorez'ApertureDB.similarity_search_with_scores8++77> ;t;;IWWPVWWrlci}|jD]0\}}|jts|||ttd<2|t}|t }t |||}|S)N page_contentmetadataid)rRrSrTrO TEXT_PROPERTYrZr )r4rqrr`rarrdocs r9rpz"ApertureDB._descriptor_to_document smGGI 8DAq||O,673/123 8&'D8I rlcRddlm}||j}tj}|j |j ||d|j jdtj|z d|Dcgc]}|j||dfc}Scc}w)Nr DescriptorsT)revector k_neighbors distances"ApertureDB similarity search took  seconds _distance aperturedb.Descriptorsrr/time find_similarr&r$rPrp)r4rwr`vectorsr descriptors start_timerqs r9r{z2ApertureDB._similarity_search_with_score_by_vectors 7!$//2 YY[   ##I1PT !  0z1I0J( S LWWa--a0!K.AWWWsB$c Fddlm}||j}tj}|j |j |||j jdtj|z d|Dcgc]}|j|c}Scc}w)a3Returns the k most similar documents to the given embedding vector Args: embedding: The embedding vector to search for k: The number of similar documents to return Returns: List of Document objects ordered by decreasing similarity to the query. rr)rerrrrr)r4rwr`r7rrrrqs r9ruz&ApertureDB.similarity_search_by_vector%s 7!$//2 YY[   ##I1 !  0z1I0J( S :EEA,,Q/EEEsBc |jjd||jj|}|j||||fi|S)aReturns similar documents to the query that also have diversity This algorithm balances relevance and diversity in the search results. Args: query: Query string to search for. k: Number of results to return. fetch_k: Number of results to fetch. lambda_mult: Lambda multiplier for MMR. Returns: List of Document objects ordered by decreasing similarity/diversty. z)Max Marginal Relevance search for query: )r$rPr'rW'max_marginal_relevance_search_by_vector)r4rMr`fetch_k lambda_multr7rws r9max_marginal_relevance_searchz(ApertureDB.max_marginal_relevance_search>sZ, DUGLM++77> ;t;; q'; 28  rlc Jddlm}||j}tj}|j |j |||||j jdtj|z d|D cgc]} |j| c} Scc} w)aReturns similar documents to the vector that also have diversity This algorithm balances relevance and diversity in the search results. Args: embedding: Embedding vector to search for. k: Number of results to return. fetch_k: Number of results to fetch. lambda_mult: Lambda multiplier for MMR. Returns: List of Document objects ordered by decreasing similarity/diversty. rr)rerrrrz&ApertureDB similarity search mmr took r) rrr/rfind_similar_mmrr&r$rPrp) r4rwr`rrr7rrrrqs r9rz2ApertureDB.max_marginal_relevance_search_by_vectorZs, 7!$//2 YY[ $$### %  4TYY[:5M4Nh W :EEA,,Q/EEEsB c >|dd|i|}|j|||S)aLCreates a new vectorstore from a list of texts Args: texts: List of text strings embedding: Embeddings object as for constructing the vectorstore metadatas: Optional list of metadatas associated with the texts. kwargs: Additional arguments to pass to the constructor r5r) add_texts)clstextsrw metadatasr7stores r9 from_textszApertureDB.from_textss)"3y3F3 y) rlc <|dd|i|}|j||S)aXCreates a new vectorstore from a list of documents Args: documents: List of Document objects embedding: Embeddings object as for constructing the vectorstore metadatas: Optional list of metadatas associated with the texts. kwargs: Additional arguments to pass to the constructor r5r) add_documents)r documentsrwr7rs r9from_documentszApertureDB.from_documentss) 3y3F3 I& rlcTddlm}m}|}||}|j|y)zDeletes a vectorstore and all its data from the database Args: descriptor_set: The name of the descriptor set to delete rrN)rrrremove_descriptorset)class_r&rrdbr0s r9delete_vectorstorezApertureDB.delete_vectorstores& =  b  "">2rlcddlm}|}dddiddddig}|j|\}}|jsJ||dddS)zReturns a list of all vectorstores in the database Returns: List of descriptor sets with properties r)rr;r<T)r@r>r?r(rA)rrrMrN)rrrrMresponserjs r9list_vectorstoreszApertureDB.list_vectorstoressu 6  $ 0$7##"& &   hhuo !!+8+!{./ ;;rlc 8d|vr|jd}|r"t|t|k7r tdg}t||D]8\}}t |j |j |}|j|:n|}|j|fi|dS)aAdd or update documents in the vectorstore. Args: documents: Documents to add to the vectorstore. kwargs: Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence. Returns: List of IDs of the added texts. Raises: ValueError: If the number of ids does not match the number of documents. rhzhThe number of ids must match the number of documents. Got {len(ids)} ids and {len(documents)} documents.r~ succeeded) poprOr-zipr rrappendupsert)r4rr7rh documents_id_document doc_with_ids r9rzApertureDB.add_documentss F?**U#Cs3x3y>1 I J!$S)!4 / X&!)!6!6%.. !!+.  /#Jt{{:00==rlc ddlm}|Dcgc](}t|ds|j|j*}}|r|j ||Dcgc]}|j }}|Dcgc]}t |dd |jni}}|jj|} |Dcgc]C}t|dr|j |jnttjE} }g} t|| || D]\} } }}|jDcic]\}}t|z|}}}| |t <||t"<d|j$|di}|g}t'j(| t&j*j-g}| j/||f||j0}|j3| t4 t7| g Scc}wcc}wcc}wcc}wcc}}w) zInsert or update items Updating documents is dependent on the documents' `id` attribute. Args: items: List of Document objects to upsert Returns: UpsertResponse object with succeeded and failed r)ParallelLoaderrNr AddDescriptor)rer+)dtype) batchsize)rfailed)aperturedb.ParallelLoaderrhasattrrrkrgetattrrr'embed_documentsstruuiduuid4rrRrTrrZr&nparrayfloat32tobytesrr/ingest BATCHSIZEr )r4rRr7ritem ids_to_deleterrrr5rhdatarrwr unique_idr`rar+commandrMblobsloaders r9rzApertureDB.upserts =!&$ t)<ATDGG$ $   KK &-23c!!33 $CT:FCLLB N  ,,<F^^=MNTQ/A-q0NJN(,J} %,5J( )..","G IEXXirzz:BBDEE KK ' ( 0 di 0B77G$ 4  Os(G GGG#*"G(-AG-"G2)r5r r&rr(z Optional[int]r) Optional[str]r*rr6intr+zOptional[Dict]r7rreturnNone)rrrz)rhzOptional[List[str]]r7rrzOptional[bool])rhz Sequence[str]rList[Document])) rMrr`rrvrr7rrr)rMrrvrr7rrList[Tuple[Document, float]])rqdictrr )rF)rw List[float]r`rrboolrr)rwrr`rr7rrr)rg?) rMrr`rrrrfloatr7rrr) rwrr`rrrrrr7rrr) rType[ApertureDB]r List[str]rwr rzOptional[List[dict]]r7rrr) rrrrrwr r7rrr)r&rrr)rrr7rrr)rRzSequence[Document]r7rrr )r# __module__ __qualname__rDESCRIPTOR_SETr!WARNr r3rkrsrxr|rpr{rurr classmethodrrrrrr __classcell__)r8s@r9rrsE -$( $ $ %)K+K+K+" K+  K+  K+K+#K+K+ K+K+ZTMl,4#$OO O-0ON48rlr)! __future__rr!rrtypingrrrrrr r numpyrlangchain_core.documentsr langchain_core.embeddingsr langchain_core.indexing.baser langchain_core.vectorstoresrtyping_extensionsrrUrVrrrTrrZrrrlr9rsd" CCC.073&     M8M8rl