h0&ddlmZmZmZmZmZmZddlmZddl Z ddl Z ddl m Z ddlmZddlmZddlmZddlmZGd d eZy) )AnyIterableListOptionalTuplecast)uuid4N)Document) Embeddings) get_from_env) VectorStore)DistanceStrategyceZdZUdZdZeed<dezZejdfdede de d ed ef d Z e d efd Zd efdZd efdZd efdZ d"deedeeede ded eef dZe d e fdZd#deeeded eefdZd$deede d eefdZ d$dede ded eefdZ d$dede ded eeeeffdZ d$deede ded eefdZ!e"ddd dejfdeede deeedede d ed eded dfd!Z#y)%SemaDBa`SemaDB` vector store. This vector store is a wrapper around the SemaDB database. Example: .. code-block:: python from langchain_community.vectorstores import SemaDB db = SemaDB('mycollection', 768, embeddings, DistanceStrategy.COSINE) zsemadb.p.rapidapi.comHOSTzhttps://collection_name vector_size embeddingdistance_strategyapi_keycf||_||_|xs tdd|_||_||_y)z#initialize the SemaDB vector store.rSEMADB_API_KEYN)rrr r _embeddingr)selfrrrrrs e/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain_community/vectorstores/semadb.py__init__zSemaDB.__init__s7 /&K,y:J"K #!2returnc>d|jtjdS)zReturn the common headers.zapplication/json)z content-typezX-RapidAPI-KeyzX-RapidAPI-Host)rrrrs rheaderszSemaDB.headers.s/"ll%{{  rc|jtjk(ry|jtjk(r t d|jtj k(ry|jtj k(r t d|jtjk(ryt d|j)z&Return the internal distance strategy. euclideanz,Max inner product is not supported by SemaDBdotcosinezUnknown distance strategy )rrEUCLIDEAN_DISTANCEMAX_INNER_PRODUCT ValueError DOT_PRODUCTJACCARDCOSINEr!s r_get_internal_distance_strategyz&SemaDB._get_internal_distance_strategy7s  ! !%5%H%H H  # #'7'I'I IKL L  # #'7'C'C C  # #'7'?'? ?KL L  # #'7'>'> >9$:P:P9QRS Src|j|j|jd}tjt j dz||j}|jdk(S)z/Creates the corresponding collection in SemaDB.)id vectorSizedistanceMetricz /collectionsjsonr") rrr-requestspostrBASE_URLr" status_code)rpayloadresponses rcreate_collectionzSemaDB.create_collectionFsb&&**"BBD  == OOn ,LL  ##s**rctjtjd|jz|j }|j dk(S)z/Deletes the corresponding collection in SemaDB. /collections/)r"r4)r5deleterr7rr"r8)rr:s rdelete_collectionzSemaDB.delete_collectionTsD?? OO d.B.B-CD DLL ##s**rNtexts metadatas batch_sizekwargsc t|ts t|}|jj|}t |d|j k7r't dt |dd|j |jtjk(rftj|}|tjj|ddz }tttt |j#}g}g}|Vt%|||D]E\} } } t't)} |j+| |j+| | i| d| idGnOt%||D]@\} } t't)} |j+| |j+| | d| idBt-dt ||D]} || | |z}t/j0t2j4d |j6d zd |i|j8 }|j:d k7r$t=d|t d|j>|jAd}t |dkDst d||S)zAdd texts to the vector store.rzEmbedding size mismatch z != T)axiskeepdimstext)r/vectormetadatar=/pointspointsr2r4zHERE--zError adding points: failedRanges)! isinstancelistrembed_documentslenrr)rrr,nparraylinalgnormrrfloattolistzipstrr appendranger5r6rr7rr"r8printrHr3)rr@rArBrC embeddings embed_matrixidsrLrHrrJnew_idibatchr: failed_rangess r add_textszSemaDB.add_texts\sj%&KE__44U; z!} !1!1 1*3z!}+=*>d4CSCSBTU   ! !%5%<%< <88J/L'"))..1t+9+Ld4;/1D1D1FGJ  -0 I-N )iUW 6" $"+$Bx$BFD>$B $'uj#9 iUW 6" $"+%+TN q#f+z2 JA1q:~.E}}M$2F2F1Gw"OO& H ##s*h& #8!HII$MMON;M=!A% #8!HII J rc|jS)zReturn the embeddings.)rr!s rr]zSemaDB.embeddingssrr_c d|i}tjtjd|jdz||j }|j dk(xrt|jddk(S)a3Delete by vector ID or other criteria. Args: ids: List of ids to delete. **kwargs: Other keyword arguments that subclasses might use. Returns: Optional[bool]: True if deletion is successful, False otherwise, None if not implemented. r_r=rKr2r4 failedPointsr) r5r>rr7rr"r8rQr3)rr_rCr9r:s rr>z SemaDB.deletest 3 ?? OO d.B.B-C7K KLL  ##s*Xs8==?>3R/SWX/XXrkc|jtjk(r\tj|}|tj j |z }ttt|j}||d}tjtjd|jdz||j }|j"dk7rt%d|j&|j)dS)zSearch points.)rIlimitr=z/points/searchr2r4zError searching: rL)rrr,rRrSrTrUrrrVrWr5r6rr7rr"r8r)rHr3)rrrhvecr9r:s r_search_pointszSemaDB._search_pointss  ! !%5%<%< <((9%C s++CT%[#**,7I  == OO d.B.B-C>R RLL    3 &0@A A}}x((rqueryc ^|jj|}|j||S)z"Return docs most similar to query.rh)r embed_querysimilarity_search_by_vector)rrmrhrCquery_embeddings rsimilarity_searchzSemaDB.similarity_searchs.//55e<//1/EErc |jj|}|j||}|Dcgc]}t|dd|d|dfc}Scc}w)z$Run similarity search with distance.rorJrH page_contentrJdistance)rrprlr )rrmrhrCrrrLps rsimilarity_search_with_scorez#SemaDB.similarity_search_with_scoresq//55e<$$_$:   a mF&;a mT*     s"Ac z|j||}|Dcgc]}t|dd|dc}Scc}w)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query vector. rorJrHru)rlr )rrrhrCrLrxs rrqz"SemaDB.similarity_search_by_vectorsO$$Y!$4  !J-"7!J- P   s8rc |s td|s td|s td||||||} | js td| j||| S)z9Return VectorStore initialized from texts and embeddings.z Collection name must be providedzVector size must be providedzAPI key must be provided)rrzError creating collection)rA)r)r;rd) clsr@rrArrrrrCsemadbs r from_textszSemaDB.from_textss|?@ @;< <78 8   /  '')89 9)4 r)Ni)N))$__name__ __module__ __qualname____doc__rrY__annotations__r7rr'intr rpropertydictr"r-boolr;r?rrrrrdr]r>rVrlr rsrryrq classmethodr~rrrrs (D#'D H/?.Q.Q 3 3 3 3 , 3  3    T T +4 ++4++/ ?}?DJ'? ?  ? c ?BJY(49-YYQUY*)U ))DJ),$%FF F03F hF$%       03  eHeO$ %  01 e ), .Q.QCyDJ'     , rr)typingrrrrrruuidr numpyrRr5langchain_core.documentsr langchain_core.embeddingsr langchain_core.utilsr langchain_core.vectorstoresr &langchain_community.vectorstores.utilsrrrrrrs1==-0-3CB[Br