tjm#ddlmZddlZddlZddlmZmZmZmZm Z ddl m Z ddl m Z ddlmZddlmZejeZGdd eZdS) ) annotationsN)AnyIterableListOptionalTuple)Document) Embeddings) VectorStore)DistanceStrategyceZdZdZejfd2dZed3d Zd4dZ d5dZ d6d7dZ d8d9d"Z d:d;d%Z d&gfdd/Ze d6d?d1ZdS)@KDBAIaT`KDB.AI` vector store. See https://kdb.ai. To use, you should have the `kdbai_client` python package installed. Args: table: kdbai_client.Table object to use as storage, embedding: Any embedding function implementing `langchain.embeddings.base.Embeddings` interface, distance_strategy: One option from DistanceStrategy.EUCLIDEAN_DISTANCE, DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE. See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb). tabler embeddingr distance_strategyOptional[DistanceStrategy]cv ddl}n#t$rtdwxYw||_||_||_dS)Nrz`Could not import kdbai_client python package. Please install it with `pip install kdbai_client`.) kdbai_client ImportError_table _embeddingr)selfrrrrs d/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/vectorstores/kdbai.py__init__zKDBAI.__init__!sg         E    #!2!returnOptional[Embeddings]cHt|jtr|jSdSN) isinstancerr )rs r embeddingszKDBAI.embeddings4s# doz 2 2 #? "ttexts Iterable[str]List[List[float]]ctjtr'jt |Sfd|DS)Nc:g|]}|S)r).0trs r z*KDBAI._embed_documents..=s%222q""222r")r rr embed_documentslist)rr#s` r_embed_documentszKDBAI._embed_documents:sN doz 2 2 @?224;;?? ?2222E2222r"textstr List[float]ct|jtr|j|S||Sr)r rr embed_query)rr/s r _embed_queryzKDBAI._embed_query?s> doz 2 2 5?..t44 4t$$$r"N List[str]idsOptional[List[str]]metadata Optional[Any]Nonec ddln#t$rtdwxYw ddl}n#t$rtdwxYw|j|}|}||d<d|D|d<fd|D|d<||||gd }|j|d dS) NrzRCould not import numpy python package. Please install it with `pip install numpy`.TCould not import pandas python package. Please install it with `pip install pandas`.idc8g|]}|dS)zutf-8)encode)r)r*s rr+z!KDBAI._insert..]s$777Aahhw''777r"r/c>g|]}|dS)float32)dtype)array)r)enps rr+z!KDBAI._insert..^s)IIIQBHHQiH88IIIr"r!)axisF)warn) numpyrpandasrr, DataFrameconcatrinsert)rr#r6r8pdembedsdfrEs @r_insertz KDBAI._insertDs1          >            ?   0077 \\^^4777776 IIII&III<  B>22B 2E*****s"+A metadatasOptional[List[dict]] batch_sizeintkwargsc @ ddl}n#t$rtdwxYwt|}d}|-t||jr|}n||}g}t |dz |zdz} t | D]} | |z} | dz|z} || | } |r || | }n&dt t | D}|$|j| | d}nd}| | ||||z}|S)aRun more texts through the embeddings and add to the vectorstore. Args: texts (Iterable[str]): Texts to add to the vectorstore. metadatas (Optional[List[dict]]): List of metadata corresponding to each chunk of text. ids (Optional[List[str]]): List of IDs corresponding to each chunk of text. batch_size (Optional[int]): Size of batch of chunks of text to insert at once. Returns: List[str]: List of IDs of the added texts. rNr<rFcNg|]"}ttj#Sr()r0uuiduuid4)r)_s rr+z#KDBAI.add_texts..s&JJJ1S..JJJr"T)drop) rJrr-r rKlenrangeiloc reset_indexrQ)rr#rSr6rUrWrNmetadfout_idsnbatchesiistartiendbatch batch_ids batch_metas r add_textszKDBAI.add_textscss,         ?   U #  )R\22 1"i00JJNz1A5x * *A^FEZ'D&+&E Kt , JJc%jj8I8IJJJ !#[5AAtALL ! LL : 6 6 6 )GGr documentsList[Document]c  ddl}n#t$rtdwxYwd|D}|d|D}||||S)aHRun more documents through the embeddings and add to the vectorstore. Args: documents (List[Document]: Documents to add to the vectorstore. batch_size (Optional[int]): Size of batch of documents to insert at once. Returns: List[str]: List of IDs of the added texts. rNr<cg|] }|j Sr() page_contentr)xs rr+z'KDBAI.add_documents..s333A333r"cg|] }|j Sr()r8rqs rr+z'KDBAI.add_documents..s ? ? ? ? ? ?r")r8rU)rJrrKrk)rrlrUrWrNr#r8s r add_documentszKDBAI.add_documentss         ?   43333<< ? ?Y ? ? ?@@~~eh:~NNNrrFquerykfilterOptional[List]List[Tuple[Document, float]]c J|j||f||d|S)anRun similarity search with distance from a query string. Args: query (str): Query string. k (Optional[int]): number of neighbors to retrieve. filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html Returns: List[Document]: List of similar documents. rvrw)&similarity_search_by_vector_with_scorer4)rrurvrwrWs rsimilarity_search_with_scorez"KDBAI.similarity_search_with_scoresC";t:   e $ $ ()&  z@KDBAI.similarity_search_by_vector_with_score..s#!N!N!N41a!v++!Q+++r")rpr8r() poprsearchr r-to_dictappendr items) rrrvrwrWmatchesdocsrowr/scores rr|z,KDBAI.similarity_search_by_vector_with_scores$ &== 3A$$+$Wi[AfWWPVWW gt $ $ ajGGK??)?44  C776??DGGO,,E KK%)!N!N399;;!N!N!N      r"c <|j|f||d|}d|DS)a`Run similarity search from a query string. Args: query (str): Query string. k (Optional[int]): number of neighbors to retrieve. filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html Returns: List[Document]: List of similar documents. r{cg|]\}}|Sr(r()r)docr\s rr+z+KDBAI.similarity_search..s222Q222r")r})rrurvrwrWdocs_and_scoress rsimilarity_searchzKDBAI.similarity_searchsH"<$;  v  )/  32/2222r"clsc  td)zNot implemented.) Exception)rr#rrSrWs r from_textszKDBAI.from_textss*+++r")rrrr rr)rr)r#r$rr%)r/r0rr1r)r#r5r6r7r8r9rr:)NNrR) r#r$rSrTr6r7rUrVrWrrr5)rR)rlrmrUrVrWrrr5) rur0rvrVrwrxrWrrry) rr1rvrVrwrxrWrrry) rur0rvrVrwrxrWrrrm) rrr#r5rr rSrTrWrrr)__name__ __module__ __qualname____doc__r EUCLIDEAN_DISTANCErpropertyr!r.r4rQrkrtr}r|r classmethodrr(r"rrrs,  / 33333&X 3333 %%%%#' +++++D+/#' 55555p<>OOOOO8!#      2!# &&&&&&V!# 33333, +/ ,,,,[,,,r"r) __future__rloggingrZtypingrrrrrlangchain_core.documentsr langchain_core.embeddingsr langchain_core.vectorstoresr &langchain_community.vectorstores.utilsr getLoggerrloggerrr(r"rrs"""""" 77777777777777------000000333333CCCCCC  8 $ $,,,,,K,,,,,r"