NgrddlmZddlZddlZddlZddlZddlZddlZddlm Z ddlm Z ddl m Z ddl mZmZmZmZmZmZmZmZddlZddlmZddlmZdd lmZdd lmZm Z dd l!m"Z"Gd d e#ej Z$ddZ%Gdde&e Z'e$j(Z)dZ*dZ+GddeZ,GddeZ-dS)) annotationsN) OrderedDict)Enum)partial)AnyCallableDictIterableListOptionalTupleType)Document) Embeddings) VectorStore) BaseSettingsSettingsConfigDict)maximal_marginal_relevanceceZdZdZdZdZdZdS)DistanceStrategyz&Enumerator of the Distance strategies.l2cosineinnerN)__name__ __module__ __qualname____doc__ EUCLIDEANCOSINEMAX_INNER_PRODUCTe/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/kinetica.pyrrs&00I Fr"rdocs_and_scoresrreturnList[Document]cd|DS)z!Return docs from docs and scores.cg|]\}}|Sr!r!.0doc_s r# z$_results_to_docs..!s . . .FCC . . .r"r!)r$s r#_results_to_docsr.s . .o . . ..r"ceZdZdZdZdS) Dimensionz-Some default dimensions for known embeddings.iN)rrrrOPENAIr!r"r#r0r0$s77 FFFr"r0 langchainlangchain_kinetica_embeddingsceZdZUdZdZded<dZded<dZd ed <dZd ed <e Z ded <e Z ded <e jZded<ddZeddddZdS)KineticaSettingsa:`Kinetica` client configuration. Attribute: host (str) : An URL to connect to MyScale backend. Defaults to 'localhost'. port (int) : URL port to connect with HTTP. Defaults to 8443. username (str) : Username to login. Defaults to None. password (str) : Password to login. Defaults to None. database (str) : Database name to find the table. Defaults to 'default'. table (str) : Table name to operate on. Defaults to 'vector_table'. metric (str) : Metric to compute distance, supported are ('angular', 'euclidean', 'manhattan', 'hamming', 'dot'). Defaults to 'angular'. https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169 zhttp://127.0.0.1strhosti#intportNz Optional[str]usernamepassworddatabasetablemetricitemr%rc"t||SN)getattr)selfr?s r# __getitem__zKineticaSettings.__getitem__OstT"""r"z.envzutf-8 kinetica_ignore)env_fileenv_file_encoding env_prefixextra)r?r6r%r)rrrrr7__annotations__r9r:r;_LANGCHAIN_DEFAULT_SCHEMA_NAMEr<"_LANGCHAIN_DEFAULT_COLLECTION_NAMEr=DEFAULT_DISTANCE_STRATEGYvaluer>rDr model_configr!r"r#r5r52s$#D""""D"H"""""H""""2H22223E3333+1F1111####&%! LLLr"r5c@eZdZdZeeedddfdUdZdVdZdWdZ e dXdZ e ddeeddfeddYd'Z dZd(Zd[d)Zd[d*Zd[d+Z d\d]d-Z d\d^d.Z d_d`d5Z d_dad7Z d_dbd9Z d_dcd:Zddd<Zded>Ze dfd?Z d_dgdBZ dhdidHZ dhdjdIZ dhdkdJZ dhdldKZ dhdldLZ e de!eeddfeddmdOZ"e de!e#j$eeddfeddndRZ%e e!deeddfeddodTZ&dS)pKineticaa`Kinetica` vector store. To use, you should have the ``gpudb`` python package installed. Args: config: Kinetica connection settings class. embedding_function: Any embedding function implementing `langchain.embeddings.base.Embeddings` interface. collection_name: The name of the collection to use. (default: langchain) NOTE: This is not the name of the table, but the name of the collection. The tables will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. distance_strategy: The distance strategy to use. (default: COSINE) pre_delete_collection: If True, will delete the collection if it exists. (default: False). Useful for testing. engine_args: SQLAlchemy's create engine arguments. Example: .. code-block:: python from langchain_community.vectorstores import Kinetica, KineticaSettings from langchain_community.embeddings.openai import OpenAIEmbeddings kinetica_settings = KineticaSettings( host="http://127.0.0.1", username="", password="" ) COLLECTION_NAME = "kinetica_store" embeddings = OpenAIEmbeddings() vectorstore = Kinetica.from_documents( documents=docs, embedding=embeddings, collection_name=COLLECTION_NAME, config=kinetica_settings, ) FNconfigr5embedding_functionrcollection_namer6 schema_namedistance_strategyrpre_delete_collectionboolloggerOptional[logging.Logger]relevance_score_fn"Optional[Callable[[float], float]]r%Nonec ||_||_||_||_||_||_|pt jt|_ ||_ | |j|_ dS)a<Constructor for the Kinetica class Args: config (KineticaSettings): a `KineticaSettings` instance embedding_function (Embeddings): embedding function to use collection_name (str, optional): the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. schema_name (str, optional): the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. distance_strategy (DistanceStrategy, optional): _description_. Defaults to DEFAULT_DISTANCE_STRATEGY. pre_delete_collection (bool, optional): _description_. Defaults to False. logger (Optional[logging.Logger], optional): _description_. Defaults to None. N) _configrTrUrV_distance_strategyrXlogging getLoggerrrZoverride_relevance_score_fn_Kinetica__get_db_db) rCrSrTrUrVrWrXrZr\s r#__init__zKinetica.__init__sk6 "4.&"3%:"; 1( ; ; +=(==..r" dimensionsr8c ddlm}n#t$rtdwxYw||_d|d}|jr||j|_|j.t|jdkr|jd|j|_dd gd d |ggd gd g|_ | | |_ dS)z' Initialize the store. r GPUdbTableZCould not import Kinetica python API. Please install it with `pip install gpudb>=7.2.2.0`.zvector()N.textstring embeddingbytes)metadatarpjson)idrpuuid)gpudbrk ImportErrorrhrX delete_schemarU table_namerVlen table_schema create_schemacreate_tables_if_not_existsEmbeddingStore)rCrhrkdimension_fields r# __post_init__zKinetica.__post_init__s&  ( ( ( ( ( ( (   G   %1J111  % !    .   'C0@,A,AA,E,E!%!1JJD4HJJDOX  '? 3 * * * $ $ $   *.*J*J*L*L #rc ddlm}n#t$rtdwxYw|}|j|_|j|_d|_||j|S)Nr)GPUdbrlT)r7options)rwrrxOptionsr:r;skip_ssl_cert_verificationr7)rCrSrrs r#__get_dbzKinetica.__get_dbs  # # # # # # #   G   --//!?!?-1*u&+w7777rc|jSrA)rTrCs r# embeddingszKinetica.embeddingss &&r")rVtexts List[str]rList[List[float]]rq metadatasOptional[List[dict]]idsOptional[List[str]]kwargsc  | d|D}|s d|D}|d||| || | | d| }|||jd||||d| |S)aClass method to assist in constructing the `Kinetica` store instance using different combinations of parameters Args: config (KineticaSettings): a `KineticaSettings` instance texts (List[str]): The list of texts to generate embeddings for and store embeddings (List[List[float]]): List of embeddings embedding (Embeddings): the Embedding function dimensions (int): The number of dimensions the embeddings have metadatas (Optional[List[dict]], optional): List of JSON data associated with each text. Defaults to None. ids (Optional[List[str]], optional): List of unique IDs (UUID by default) associated with each text. Defaults to None. collection_name (str, optional): Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. schema_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. distance_strategy (DistanceStrategy, optional): Not used for now. Defaults to DEFAULT_DISTANCE_STRATEGY. pre_delete_collection (bool, optional): Whether to delete the Kinetica schema or not. Defaults to False. logger (Optional[logging.Logger], optional): Logger to use for logging at different levels. Defaults to None. Returns: Kinetica: An instance of Kinetica class NcNg|]"}ttj#Sr!r6rvuuid4r*r,s r#r-z#Kinetica.__from..&4443tz||$$444r"cg|]}iSr!r!rs r#r-z#Kinetica.__from..++++++r")rSrUrVrTrWrXrZrrrrr!)radd_embeddings)clsrSrrrqrhrrrUrWrXrZrVrstores r#__fromzKinetica.__fromsZ ;44e444C ,++U+++I  +#(/"7       J''' J)  PV    r"c ddlm}n#t$rtdwxYw||j|j|jddiS)z2Create the table to store the texts and embeddingsrrjrl is_replicatedtrue)_typenamedbr)rwrkrxr|rzrf)rCrks r#r~z$Kinetica.create_tables_if_not_existss  ( ( ( ( ( ( (   G   z#x$f-     rcN|j|jddidS)zDelete the tableno_error_if_not_existsr)rN)rf clear_tablerzrs r# drop_tableszKinetica.drop_tables.s;  +CV*L      r"cD|j|jdS)zCreate a new Kinetica schemaN)rfr}rVrs r#r}zKinetica.create_schema4s! t/00000r"c|jd|j|jddddS)z|Delete a Kinetica schema with cascade set to `true` This method will delete a schema with all tables in it. zTrying to delete collectionr)rcascadeN)rZdebugrf drop_schemarVrs r#ryzKinetica.delete_schema8sO 7888   FSS     r" Iterable[str]c 4| d|D}|s d|D}g}t||||D]L\}}} } tjd|jzg|R} ||| t j| | gM|j||S)aiAdd embeddings to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. embeddings: List of list of embedding vectors. metadatas: List of metadatas associated with the texts. ids: List of ids for the text embedding pairs kwargs: vectorstore specific parameters NcNg|]"}ttj#Sr!rrs r#r-z+Kinetica.add_embeddings..Srr"cg|]}iSr!r!rs r#r-z+Kinetica.add_embeddings..Vrr"%sf) zipstructpackrhappendrtdumpsrinsert_records) rCrrrrrrecordsrorqrsrubufs r#rzKinetica.add_embeddingsAs" ;44e444C ,++U+++I-0 Is-S-S B B )D)Xr+edo5B BBBC NND#tz(';';R@ A A A A **7333 r"c |jt|}t|d|_t |ds||j|jd||||d|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 (JSON data) associated with the texts. ids: List of IDs (UUID) for the texts supplied; will be generated if None kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. rrrr!)rTembed_documentslistr{rhhasattrrr)rCrrrrrs r# add_textszKinetica.add_textsas$,<> ::1V;   r" List[float]cT||||}|rh|dddkrVd|vrR|d}ttt|}||S|j|ddgS)N status_infostatusOKrmessage)_Kinetica__query_collectionrrvalues_results_to_docs_and_scoresrZerror)rCrqrrresprresultss r#rz/Kinetica.similarity_search_with_score_by_vectors,,Y6BB  =D'1T99i4>O>O#' ?G3W^^%5%5 6 6788G33G<< < $}-i8999 r"c J||||}d|DS)akReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents most similar to the query vector. rcg|]\}}|Sr!r!r)s r#r-z8Kinetica.similarity_search_by_vector..s222Q222r")r)rCrqrrrr$s r#rz$Kinetica.similarity_search_by_vectors<"EE1VF  32/2222r"rcNt|dkrfd|Dng}|S)z$Return docs and scores from results.rc g|]B}t|dtj|dj|dndfCS)r) page_contentrsN)rrtloadsrT)r*resultrCs r#r-z8Kinetica._results_to_docs_and_scores..sl    %+AY!%F1I!6!6"&!8!DF1II$    r")r{)rCrrs` r#rz$Kinetica._results_to_docs_and_scoressT7||a    &      r"Callable[[float], float]c|j|jS|jtjkr|jS|jtjkr|jS|jtjkr|jStd|jd)a8 The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. Nz=No supported normalization function for distance_strategy of z?.Consider providing relevance_score_fn to Kinetica constructor.) rdrarr_cosine_relevance_score_fnr_euclidean_relevance_score_fnr %_max_inner_product_relevance_score_fn ValueErrorrs r#_select_relevance_score_fnz#Kinetica._select_relevance_score_fns  + 73 3  "&6&= = =2 2  $(8(B B B5 5  $(8(J J J= =Q-1-DQQQ r"c |jtjkrdS|jtjkrdS|jtjkrdSt d|jdddtDd) N l2_distancecosine_distance dot_productz#Got unexpected value for distance: z. Should be one of z, cg|] }|j Sr!)rO)r*dss r#r-z.Kinetica.distance_strategy.. s.S.S.SBrx.S.S.Sr"rn)rarrrr rjoinrs r#rWzKinetica.distance_strategys  "&6&@ @ @ =  $(8(? ? ?$$  $(8(J J J =Xd6MXX$(II.S.SBR.S.S.S$T$TXXX r"Optional[Dict[str, str]]r c ||tj|nd}|d|dnd}ddd|Dzdz}|j}d |d |d |jd |jd |d|d }|j||j |} |j| | S)zQuery the collection.Nz where 'z' = JSON(metadata) [,c,g|]}t|Sr!)r6)r*xs r#r-z/Kinetica.__query_collection..-s'B'B'B1A'B'B'Br"]z( SELECT text, metadata, z (embedding, 'zA') as distance, embedding FROM "z"."z" zH ORDER BY distance asc NULLS LAST LIMIT z ) rtrrrWrVrUrZrrfexecute_sql_and_decode) rCrqrr json_filter where_clause embedding_str dist_strategy query_stringrs r#__query_collectionzKinetica.__query_collection s$2-3,>dj(((D & 8{ 7 7 7 7  chh'B'B 'B'B'BCCCcI .  (5  DQ  '  -1,@          ,'''x..|<< $ r"?fetch_k lambda_multfloatc  |||}|d}ttt|} fd|dD} t t j|t j| || | } fdt| DS)aReturn docs selected using the maximal marginal relevance with score to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. rrcJg|]}tjdjz| S)r)runpackrh)r*rqrCs r#r-zOKinetica.max_marginal_relevance_search_with_score_by_vector..as<    M%$/19 = =   r"rq)dtype)rrc"g|] \}}|v | Sr!r!)r*ir mmr_selecteds r#r-zOKinetica.max_marginal_relevance_search_with_score_by_vector..os'IIIdaqL7H7H7H7H7Hr") rrrrrnparrayfloat32r enumerate) rCrqrrrrrrrrembedding_list candidatesrs ` @r#2max_marginal_relevance_search_with_score_by_vectorz;Kinetica.max_marginal_relevance_search_with_score_by_vector?s<&&gf&UU#IsD!1!122344    $[1    2 HYbj 1 1 1 #    55g>> IIIIi 33IIIIr"c \|j|}|j|f||||d|S)avReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. rrrr)rTr'max_marginal_relevance_search_by_vector)rCrrrrrrrqs r#max_marginal_relevance_searchz&Kinetica.max_marginal_relevance_searchqsT8+77>> ;t;  #       r"c `|j|}|jd|||||d|}|S)aReturn docs selected using the maximal marginal relevance with score. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. )rqrrrrr!)rTrr) rCrrrrrrrqrs r#(max_marginal_relevance_search_with_scorez1Kinetica.max_marginal_relevance_search_with_scoresX:+77>> FtF #       r"c F|j|f||||d|}t|S)aReturn docs selected using the maximal marginal relevance to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): 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. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. r)rr.)rCrqrrrrrr$s r#rz0Kinetica.max_marginal_relevance_search_by_vectorsL:R$Q  #       000r"cKt|j|f||||d|}tjd|d{VS)z:Return docs selected using the maximal marginal relevance.rN)rrasyncioget_event_looprun_in_executor)rCrqrrrrrfuncs r#(amax_marginal_relevance_search_by_vectorz1Kinetica.amax_marginal_relevance_search_by_vectorsu  8  #     +--==dDIIIIIIIIIr"rType[Kinetica]c  t|dkrtd ||dd} n,#t$r||dg} YnwxYwt| d} |t |} |jd|| || ||||| ||d | }|S)aAdds the texts passed in to the vector store and returns it Args: cls (Type[Kinetica]): Kinetica class texts (List[str]): A list of texts for which the embeddings are generated embedding (Embeddings): List of embeddings metadatas (Optional[List[dict]], optional): List of dicts, JSON describing the texts/documents. Defaults to None. config (KineticaSettings): a `KineticaSettings` instance collection_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. schema_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. distance_strategy (DistanceStrategy, optional): Distance strategy e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. ids (Optional[List[str]], optional): A list of UUIDs for each text/document. Defaults to None. pre_delete_collection (bool, optional): Indicates whether the Kinetica schema is to be deleted or not. Defaults to False. Returns: Kinetica: a `Kinetica` instance rztexts is emptyr rrrqrhrSrrrUrVrWrXr!)r{rrNotImplementedErrorrr_Kinetica__from)rrrqrrSrUrWrrXrVrfirst_embeddingrhrkinetica_stores r# from_textszKinetica.from_textssN u::??-.. . @'77ac CCOO" @ @ @(44U1X>>?OOO @+,, ..tE{{;; #  !!+#/"7      sA&A+*A+text_embeddingsList[Tuple[str, List[float]]]c  d|D} d|D} t| d}|jd| | ||||||| || d | S)aAdds the embeddings passed in to the vector store and returns it Args: cls (Type[Kinetica]): Kinetica class text_embeddings (List[Tuple[str, List[float]]]): A list of texts and the embeddings embedding (Embeddings): List of embeddings metadatas (Optional[List[dict]], optional): List of dicts, JSON describing the texts/documents. Defaults to None. config (KineticaSettings): a `KineticaSettings` instance dimensions (int, optional): Dimension for the vector data, if not passed a default will be used. Defaults to Dimension.OPENAI. collection_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. schema_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. distance_strategy (DistanceStrategy, optional): Distance strategy e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. ids (Optional[List[str]], optional): A list of UUIDs for each text/document. Defaults to None. pre_delete_collection (bool, optional): Indicates whether the Kinetica schema is to be deleted or not. Defaults to False. Returns: Kinetica: a `Kinetica` instance cg|] }|d S)rr!r*ts r#r-z,Kinetica.from_embeddings..ns///!1///r"cg|] }|d S)rr!r0s r#r-z,Kinetica.from_embeddings..os444qad444r"rr&r!)r{r()rr,rqrrSrhrUrWrrXrVrrrs r#from_embeddingszKinetica.from_embeddingsCsV0////44O444 A'' sz  !!+#/"7      r" documentsc  `d|D} d|D}|jd| ||||| |||d | S)a Adds the list of `Document` passed in to the vector store and returns it Args: cls (Type[Kinetica]): Kinetica class texts (List[str]): A list of texts for which the embeddings are generated embedding (Embeddings): List of embeddings config (KineticaSettings): a `KineticaSettings` instance metadatas (Optional[List[dict]], optional): List of dicts, JSON describing the texts/documents. Defaults to None. collection_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME. schema_name (str, optional): Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME. distance_strategy (DistanceStrategy, optional): Distance strategy e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY. ids (Optional[List[str]], optional): A list of UUIDs for each text/document. Defaults to None. pre_delete_collection (bool, optional): Indicates whether the Kinetica schema is to be deleted or not. Defaults to False. Returns: Kinetica: a `Kinetica` instance cg|] }|j Sr!)rr*ds r#r-z+Kinetica.from_documents..s333A333r"cg|] }|j Sr!)rsr7s r#r-z+Kinetica.from_documents..s333AQZ333r") rrqrrSrUrVrWrrXr!)r+) rr4rqrSrrUrWrrXrVrrs r#from_documentszKinetica.from_documentsspN4333333333 s~  +#/"7      r")rSr5rTrrUr6rVr6rWrrXrYrZr[r\r]r%r^)rhr8r%r^)rSr5r%r)r%r)rSr5rrrrrqrrhr8rrrrrUr6rWrrXrYrZr[rVr6rrr%rR)r%r)r%r^)NN) rrrrrrrrrrr%r) rrrrrrrrr%r)rN) rr6rr8rrrrr%r&)rr6rr8rrr%r)rqrrr8rrr%r) rqrrr8rrrrr%r&)rrr%r)r%r)r%r6)rqrrr8rrr%r )rrrN)rqrrr8rr8rrrrrrr%r)rr6rr8rr8rrrrrrr%r&)rr6rr8rr8rrrrrrr%r)rqrrr8rr8rrrrrrr%r&)rr$rrrqrrrrSr5rUr6rWrrrrXrYrVr6rrr%rR)rr$r,r-rqrrrrSr5rhr8rUr6rWrrrrXrYrVr6rrr%rR)rr$r4r&rqrrSr5rrrUr6rWrrrrXrYrVr6rrr%rR)'rrrrrMrLrNrgrrepropertyr classmethodr(r~rr}ryrrrrrrrrrWrrrrrr#r5r+r0r1r3r:r!r"r#rRrRZs?""P B9.G&++/AE#/#/#/#/#/JMMMM@ 8 8 8 8'''X'+/#'A.G&++/C:CCCCC[CJ    "    1111    +/#' F+/#'      :!%      6!% 2!% ,!% 33333,$6   X  +/ 00000j +/ 0J0J0J0J0Jj +/ $ $ $ $ $ R !% &&&&&V +/ &1&1&1&1&1V +/ JJJJJ2 +/#3#3#5#5A.G#'&+@:@@@@@[@D +/#3#3#5#5#*A.G#'&+; :; ; ; ; ; [; z $4#3#5#5*.A.G#'&+4 :4 4 4 4 4 [4 4 4 r"rR)r$rr%r&). __future__rrenumrtrbrrv collectionsrr functoolsrtypingrrr r r r r rnumpyrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.vectorstoresrpydantic_settingsrr&langchain_community.vectorstores.utilsrr6rr.r8r0rrNrLrMr5rRr!r"r#rHs""""""  ######MMMMMMMMMMMMMMMMMMMM------000000333333>>>>>>>>MMMMMM     sDI   //// T -6!,## %%%%%|%%%P\  \  \  \  \  {\  \  \  \  \  r"