dhRSSKJr SSKrSSKrSSKJrJrJrJrJ r J r J r J r J r SSKrSSKJr SSKJr SSKJr SSKJr \(aSSKr\R2"5rSr"S S \5rg) ) annotationsN) TYPE_CHECKINGAnyDictIterableListOptionalSetTupleType)Document) Embeddings) VectorStore)maximal_marginal_relevancec\rSrSr%SrSrS\S'\SSS4SSjjr\SSj5r SSS jjr SS jr \ SS4S S jjr \ SS4S!S jjrS"S#S jjrS\ SSS4S$SjjrS%S&SjjrS%S'SjjrS(S)SjjrS*S+SjjrS*S,SjjrSSjrSSjrS-SjrS.Sjr\SS\SS4S/Sjj5r\S\SS4S0Sjj5rSrg)1AwaDBz`AwaDB` vector store.langchain_awadbstr_DEFAULT_TABLE_NAMENc SSKnUbX@lO1UbUR"U5UlOUR"5UlXR:Xa8US- nU[ [ R"55RS5S- nURRU5 0Ul UbX RU'Xl g![a [S5ef=f)aInitialize with AwaDB client. If table_name is not specified, a random table name of `_DEFAULT_TABLE_NAME + last segment of uuid` would be created automatically. Args: table_name: Name of the table created, default _DEFAULT_TABLE_NAME. embedding: Optional Embeddings initially set. log_and_data_dir: Optional the root directory of log and data. client: Optional AwaDB client. kwargs: Any possible extend parameters in the future. Returns: None. rNzRCould not import awadb python package. Please install it with `pip install awadb`._-) awadb ImportError awadb_clientClientrruuiduuid4splitCreatetable2embeddingsusing_table_name)self table_name embeddinglog_and_data_dirclientkwargsrs ^/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/awadb.py__init__AwaDB.__init__s.     & +$)LL1A$B!$)LLN! 11 1 # J #djjl+11#6r: :J   ,79  09 ! !* - *- >  s CCcjURUR;aURUR$gN)r%r$)r&s r, embeddingsAwaDB.embeddingsKs0  D$9$9 9(()>)>? ?c  URc [S5eSnURUR;a1URURR [ U55nURR SSUUUU5$)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. is_duplicate_texts: Optional whether to duplicate texts. Defaults to True. kwargs: any possible extend parameters in the future. Returns: List of ids from adding the texts into the vectorstore. NAwaDB client is None!!!embedding_texttext_embedding)r ValueErrorr%r$embed_documentslistAddTexts)r&texts metadatasis_duplicate_textsr+r1s r, add_textsAwaDB.add_textsQs"    $67 7  D$9$9 9..t/D/DEUUU J  ))         r3c hURc [S5eURRU5$)zLoad the local specified table. Args: table_name: Table name kwargs: Any possible extend parameters in the future. Returns: Success or failure of loading the local specified table r5)rr8Load)r&r'r+s r, load_localAwaDB.load_localts3    $67 7  %%j11r3c "URc [S5eSnURUR;a)URURR U5nOSSKJn U"5RU5n1SknURUUUUUS9$)aReturn docs most similar to query. Args: query: Text query. k: The maximum number of documents to return. text_in_page_content: Filter by the text in page_content of Document. meta_filter (Optional[dict]): Filter by metadata. Defaults to None. E.g. `{"color" : "red", "price": 4.20}`. Optional. E.g. `{"max_price" : 15.66, "min_price": 4.20}` `price` is the metadata field, means range filter(4.20<'price'<15.66). E.g. `{"maxe_price" : 15.66, "mine_price": 4.20}` `price` is the metadata field, means range filter(4.20<='price'<=15.66). kwargs: Any possible extend parameters in the future. Returns: Returns the k most similar documents to the specified text query. Nr5r AwaEmbedding>_idscorer7text_in_page_content meta_filternot_include_fields_in_metadata) rr8r%r$ embed_queryrrG Embeddingsimilarity_search_by_vector) r&querykrKrLr+r(rGnot_include_fieldss r,similarity_searchAwaDB.similarity_searchs4    $67 7  D$9$9 9--d.C.CDPPQVWI *$007I'I//  !5#+= 0  r3c URc [S5eSnURUR;a)URURR U5nOSSKJn U"5RU5n/nSS1n URUUUUU S9n U H3n U RSn U RS X4n URU 5 M5 U$) aThe most k similar documents and scores of the specified query. Args: query: Text query. k: The k most similar documents to the text query. text_in_page_content: Filter by the text in page_content of Document. meta_filter: Filter by metadata. Defaults to None. kwargs: Any possible extend parameters in the future. Returns: The k most similar documents to the specified text query. 0 is dissimilar, 1 is the most similar. Nr5rrFr7rHrJrI) rr8r%r$rNrrGrOrPmetadataappend)r&rQrRrKrLr+r(rGresultsrSretrieval_docsdocrI doc_tuples r,similarity_search_with_score"AwaDB.similarity_search_with_scores,    $67 7  D$9$9 9--d.C.CDPPQVWI *$007I02(8%'@99  !5#+= : "CLL)E W% I NN9 % " r3c (UR"X40UD6$r0)r])r&rQrRr+s r,(_similarity_search_with_relevance_scores.AwaDB._similarity_search_with_relevance_scoress 00DVDDr3c JURc [S5e/nUcU$URRUUUUUS9nUR5S:XaU$USSHDn Sn 0n U Hn U S:XaXn MUbX;aMXX'M! UR [ XS95 MF U$)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. text_in_page_content: Filter by the text in page_content of Document. meta_filter: Filter by metadata. Defaults to None. not_incude_fields_in_metadata: Not include meta fields of each document. Returns: List of Documents which are the most similar to the query vector. r5)rKrLrSr ResultItemsr6 page_contentrW)rr8Search__len__rXr ) r&r(rRrKrLrMr+rY show_results item_detailcontent meta_dataitem_keys r,rP!AwaDB.similarity_search_by_vectors,    $67 7"$  N((//  !5#= 0     !Q &N'?=9KGI'//)3G3?A &1&; #( NN8M N:r3c LURc [S5e/nURUR;a)URURR U5nOSSKJn U "5RU5nUR5S:Xa/$URUUUUUUS9n U $)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. text_in_page_content: Filter by the text in page_content of Document. meta_filter (Optional[dict]): Filter by metadata. Defaults to None. Returns: List of Documents selected by maximal marginal relevance. r5rrF) lambda_multrKrL) rr8r%r$rNrrGrOrh'max_marginal_relevance_search_by_vector) r&rQrRfetch_krprKrLr+r(rGrYs r,max_marginal_relevance_search#AwaDB.max_marginal_relevance_search)s:    $67 7!#  D$9$9 9--d.C.CDPPQVWI *$007I    ! #I>>  #!5# ? r3c URc [S5e/nUcU$SS1n URUUUUU S9n /n U H!n U RU RS5 M# [ [ R"U[ RS9U S9n U H9nSXR;dMXRS URX5 M; U$)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. 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. text_in_page_content: Filter by the text in page_content of Document. meta_filter (Optional[dict]): Filter by metadata. Defaults to None. Returns: List of Documents selected by maximal marginal relevance. r5rHrIrJr7)dtype)embedding_list) rr8rPrXrWrnparrayfloat32)r&r(rRrrrprKrLr+rYrSretrieved_docstop_embeddingsr[ selected_docss_ids r,rq-AwaDB.max_marginal_relevance_search_by_vector^s<    $67 7"$  N#('"299  !5#+= : !C  ! !#,,/?"@ A"3 HHYbjj 1. "D>#7#@#@@"(112BC~34"r3c URc [S5eURRUUUUUS9n0nUH@n Sn 0n U H#n U S:XaXn MU S:XdU S:XaMXX'M% [XS9n XU S'MB U$)aReturn docs according ids. Args: ids: The ids of the embedding vectors. text_in_page_content: Filter by the text in page_content of Document. meta_filter: Filter by any metadata of the document. not_include_fields: Not pack the specified fields of each document. limit: The number of documents to return. Defaults to 5. Optional. Returns: Documents which satisfy the input conditions. r5)idsrKrLrSlimitrdr6r7rHre)rr8Getr )r&rrKrLrSrr+ docs_detailrY doc_detailrk meta_infofieldr[s r,get AwaDB.gets,    $67 7''++!5#1 , (*%JGI#,,(/G..%5.#-#4  $DC),Ju% &&r3c URc [S5eSnUbUR5S:XaU$URRU5nU$)aDelete the documents which have the specified ids. Args: ids: The ids of the embedding vectors. **kwargs: Other keyword arguments that subclasses might use. Returns: Optional[bool]: True if deletion is successful. False otherwise, None if not implemented. Nr5r)rr8rhDelete)r&rr+rets r,delete AwaDB.deletesS    $67 7" ;#++-1,J&&s+ r3c hURc [S5eURRUSX#S9$)aUpdate the documents which have the specified ids. Args: ids: The id list of the updating embedding vector. texts: The texts of the updating documents. metadatas: The metadatas of the updating documents. Returns: the ids of the updated documents. r5r6)rtext_field_namer<r=)rr8 UpdateTexts)r&rr<r=r+s r,update AwaDB.updatesB"    $67 7  ,,%5U-  r3c rURcgURRU5nU(aXlU$)zCreate a new table.F)rr#r%r&r'r+rs r, create_tableAwaDB.create_tables7    $&&z2 $. ! r3c rURcgURRU5nU(aXlU$)zJUse the specified table. Don't know the tables, please invoke list_tables.F)rUser%rs r,use AwaDB.use s7    $##J/ $. ! r3c TURc/$URR5$)z*List all the tables created by the client.)r ListAllTablesr&r+s r, list_tablesAwaDB.list_tabless*    $I  ..00r3c UR$)zGet the current table.)r%rs r,get_current_tableAwaDB.get_current_table(s $$$r3c 6U"UUUUS9nURXS9 U$)aCreate an AwaDB vectorstore from a raw documents. Args: texts (List[str]): List of texts to add to the table. embedding (Optional[Embeddings]): Embedding function. Defaults to None. metadatas (Optional[List[dict]]): List of metadatas. Defaults to None. table_name (str): Name of the table to create. log_and_data_dir (Optional[str]): Directory of logging and persistence. client (Optional[awadb.Client]): AwaDB client Returns: AwaDB: AwaDB vectorstore. )r'r(r)r*)r<r=)r?) clsr<r(r=r'r)r*r+rs r, from_textsAwaDB.from_texts0s30!-  U@r3c UVs/sHowRPM nnUVs/sHowRPM n nURUUU UUUS9$s snfs snf)aCreate an AwaDB vectorstore from a list of documents. If a log_and_data_dir specified, the table will be persisted there. Args: documents (List[Document]): List of documents to add to the vectorstore. embedding (Optional[Embeddings]): Embedding function. Defaults to None. table_name (str): Name of the table to create. log_and_data_dir (Optional[str]): Directory to persist the table. client (Optional[awadb.Client]): AwaDB client. Any: Any possible parameters in the future Returns: AwaDB: AwaDB vectorstore. )r<r(r=r'r)r*)rfrWr) r documentsr(r'r)r*r+r[r<r=s r,from_documentsAwaDB.from_documentsQsb2.77Yc!!Y7-67Yc\\Y 7~~!-   87s A A)rr$r%) r'rr(Optional[Embeddings]r) Optional[str]r*Optional[awadb.Client]r+rreturnNone)rr)NN) r< Iterable[str]r=Optional[List[dict]]r>Optional[bool]r+rr List[str])r'rr+rrbool) rQrrRintrKrrLOptional[dict]r+rrList[Document]) rQrrRrrKrrLrr+rrList[Tuple[Document, float]])r)rQrrRrr+rrr)r(zOptional[List[float]]rRrrKrrLrrMOptional[Set[str]]r+rrr)rg?NN)rQrrRrrrrrpfloatrKrrLrr+rrr)r(z List[float]rRrrrrrprrKrrLrr+rrr)NNNNN)rOptional[List[str]]rKrrLrrSrrz Optional[int]r+rrzDict[str, Document]r0)rrr+rrr) rrr<rr=rr+rrr)r+rrr)r+rrr)r Type[AwaDB]r<rr(rr=rr'rr)rr*rr+rrr)rrrrr(rr'rr)rr*rr+rrr)__name__ __module__ __qualname____firstlineno____doc__r__annotations__r-propertyr1r?rC DEFAULT_TOPNrTr]r`rPrsrqrrrrrrr classmethodrr__static_attributes__r3r,rrs00.*.*.)- /+/+(/+( /+ ' /+  /+ /+b+/-1 ! ! (! + !  !  ! F222  2..2&* , ,  , , , $ ,  ,  , b.2&* 22 2, 2 $ 2  2 &2nEE E E & E,0.2&*=A 5(5 5, 5 $ 5 ); 55 5t .2&*33 3 3  3 , 3$33 3p .2&*<< < <  < , <$<< <@$(.2&*15# 0 0,0$ 0 / 0  00 0h$(   6+/    (      0      1 1  1%% %+/*.-*.)- ((    ( ' @+/-*.)- ! ! !! (!  ! ( ! ' ! !  ! ! r3r) __future__rloggingr typingrrrrrr r r r numpyrxlangchain_core.documentsr langchain_core.embeddingsrlangchain_core.vectorstoresr&langchain_community.vectorstores.utilsrr getLoggerloggerrrrr3r,rsM" WWW-03M     ^  K^  r3