j;ddlmZddlZddlmZmZmZmZmZm Z m Z m Z m Z erddl Z ddlZ ddlmZmZmZmZddlmZddlmZddlmZddlmZd Zdd Zdd ZGd d eZy)) annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTupleType)ID OneOrManyWhere WhereDocument)Document) Embeddings)xor_args) VectorStorecJt|Dcgc]\}}| c}}Scc}}wN)_results_to_docs_and_scores)resultsdoc_s p/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/vectorstores/bagel.py_results_to_docsrs%9'B CBFCCB CC Cs ct|dd|dd|ddDcgc]}t|d|dxsi|df c}Scc}w)N documentsr metadatas distances) page_contentmetadata)zipr)rresults rrr!su K  # K  # K  #   F vay6!9? CVAYO   s#Ac 0eZdZUdZdZded<edddddf ddZeddZe d d dd Z d dd Z e df dd Z e df dd Zedddeddddf dd ZddZe df d dZe df d!dZd"dZeddedddf d#dZd$dZ d% d&dZd'd(dZy))Bagela"``Bagel.net`` Inference platform. To use, you should have the ``bagelML`` python package installed. Example: .. code-block:: python from langchain_community.vectorstores import Bagel vectorstore = Bagel(cluster_name="langchain_store") langchainstr_LANGCHAIN_DEFAULT_CLUSTER_NAMENcH ddl}ddl}|||_||_n@|r|}n|j j dd}||_|j||_|jj|||_ ||_ ||_ y#t$r tdwxYw)zInitialize with bagel clientrN+Please install bagel `pip install bagelML`.restzapi.bageldb.ai)bagel_api_implbagel_server_host)namer$) bagel bagel.config ImportError_client_settings_clientconfigSettingsClientget_or_create_cluster_clusteroverride_relevance_score_fn_embedding_function) self cluster_nameclient_settingsembedding_functioncluster_metadataclientrelevance_score_fnr3r6s r__init__zBagel.__init__:s M    $3D !!DL#2 #(<<#8#8#)&6$9$ %5D !'5<<(89DL ::%;  ,>(#5 + MKL L Ms B B!c|jSr)r>r?s r embeddingszBagel.embeddings^s''') query_textsquery_embeddingsc  ddl}|jr,|*|r(t|}|jj |}d}|j j d||||d|S#t$r tdwxYw)z9Query the Bagel cluster based on the provided parameters.rNr.)rKrL n_resultswhere)r3r5r>listembed_documentsr<find)r?rKrLrNrOkwargsr3textss r__query_clusterzBagel.__query_clusterbs M   # #(8(@[%E#77GGN K!t}}!! #-      MKL L Ms AA4c |*|Dcgc]}ttj!}}t|}|jr||r|jj |}|r)t |t |z }|r |ig|zz}g}g} t|D]*\} } | r| j| |j| ,| rl| D cgc]} ||  }} | D cgc]} ||  } } |r| D cgc]} ||  c} nd} | D cgc]} ||  }} |jj| || ||rY|Dcgc]}|| }}|r|Dcgc]}|| c}nd}|Dcgc]}|| }}|jj||||Sigt |z}|jj|||||Scc}wcc} wcc} wcc} wcc} wcc}wcc}wcc}w)a Add texts along with their corresponding embeddings and optional metadata to the Bagel cluster. Args: texts (Iterable[str]): Texts to be added. embeddings (Optional[List[float]]): List of embeddingvectors metadatas (Optional[List[dict]]): Optional list of metadatas. ids (Optional[List[str]]): List of unique ID for the texts. Returns: List[str]: List of unique ID representing the added texts. N)rIr rids)rIrrX)rIrr rX) r+uuiduuid4rQr>rRlen enumerateappendr<upsert)r?rUr rXrIrTr length_diff empty_ids non_empty_idsidxr$texts_with_metadatasembeddings_with_metadatasids_with_metadatajtexts_without_metadatasembeddings_without_metadatasids_without_metadatass r add_textszBagel.add_texts~s., ;.34e3tzz|$eC4U   # # (:u11AA%HJ e*s9~5K%{(:: IM!*9!5 X!((-$$S) "6 7DE}Ys^} E>K'Lmsc m$'LBL > Z_ >RV*:G$G#SX!$G $$8'2) % =F*GY58Y'*G:DI6IqZ]I6$-:C(CAQ%(C $$;5-% s5z)I MM %# !   a5"F'L>$G+H6(Cs/$F? G G 0 G G8 G G! G"c `|j|||}|Dcgc]\}}| c}}Scc}}w)a Run a similarity search with Bagel. Args: query (str): The query text to search for similar documents/texts. k (int): The number of results to return. where (Optional[Dict[str, str]]): Metadata filters to narrow down. Returns: List[Document]: List of documents objects representing the documents most similar to the query text. )rO)similarity_search_with_score)r?querykrOrTdocs_and_scoresrrs rsimilarity_searchzBagel.similarity_searchs7&;;E1E;R"12/Q/222s *c B|j|g||}t|S)a Run a similarity search with Bagel and return documents with their corresponding similarity scores. Args: query (str): The query text to search for similar documents. k (int): The number of results to return. where (Optional[Dict[str, str]]): Filter using metadata. Returns: List[Tuple[Document, float]]: List of tuples, each containing a Document object representing a similar document and its corresponding similarity score. )rKrNrO_Bagel__query_clusterr)r?rmrnrOrTrs rrlz"Bagel.similarity_search_with_scores(,&&E7au&U*733rJc L|d|||||d| } | j|| ||} | S)a Create and initialize a Bagel instance from list of texts. Args: texts (List[str]): List of text content to be added. cluster_name (str): The name of the Bagel cluster. client_settings (Optional[bagel.config.Settings]): Client settings. cluster_metadata (Optional[Dict]): Metadata of the cluster. embeddings (Optional[Embeddings]): List of embedding. metadatas (Optional[List[dict]]): List of metadata. ids (Optional[List[str]]): List of unique ID. Defaults to None. client (Optional[bagel.Client]): Bagel client instance. Returns: Bagel: Bagel vectorstore. )r@rBrArDrC)rUrIr rXrP)rj) clsrU embeddingr rXr@rArCrDtext_embeddingsrT bagel_clusterrs r from_textszBagel.from_textssT< %(+-      # #Oyc $ rJcb|jj|jjy)zDelete the cluster.N)r7delete_clusterr<r2rHs rr{zBagel.delete_cluster!s ##DMM$6$67rJc @|j|||}t|S)zT Return docs most similar to embedding vector and similarity score. rLrNrOrr)r?rLrnrOrTrs r1similarity_search_by_vector_with_relevance_scoresz7Bagel.similarity_search_by_vector_with_relevance_scores%s-&&-%' +733rJc @|j|||}t|S)z-Return docs most similar to embedding vector.r})rsr)r?rvrnrOrTrs rsimilarity_search_by_vectorz!Bagel.similarity_search_by_vector4s-&&&!5'  ((rJc|jr |jSd}d}|jj}|r ||vr||}|dk(r |jS|dk(r |jS|dk(r |j St d|d)z Select and return the appropriate relevance score function based on the distance metric used in the Bagel cluster. l2z hnsw:spacecosineipzANo supported normalization function for distance metric of type: z=. Consider providing relevance_score_fn to Bagel constructor.)r=r<r$_cosine_relevance_score_fn_euclidean_relevance_score_fn%_max_inner_product_relevance_score_fn ValueError)r?distance distance_keyr$s r_select_relevance_score_fnz Bagel._select_relevance_score_fnAs  + +33 3# ==))  0 -H x 22 2  55 5  == =$$,:.<< rJc |D cgc]} | j} } |D cgc]} | j} } |jd| || |||||d|Scc} wcc} w)a Create a Bagel vectorstore from a list of documents. Args: documents (List[Document]): List of Document objects to add to the Bagel vectorstore. embedding (Optional[List[float]]): List of embedding. ids (Optional[List[str]]): List of IDs. Defaults to None. cluster_name (str): The name of the Bagel cluster. client_settings (Optional[bagel.config.Settings]): Client settings. client (Optional[bagel.Client]): Bagel client instance. cluster_metadata (Optional[Dict]): Metadata associated with the Bagel cluster. Defaults to None. Returns: Bagel: Bagel vectorstore. )rUrvr rXr@rArDrCrP)r#r$ry) rurrvrXr@rArDrCrTrrUr s rfrom_documentszBagel.from_documents]sx:.77Yc!!Y7-67YcS\\Y 7s~~  %+-    87s AAcv|j}|j}|jj|g|g|gy)zUpdate a document in the cluster. Args: document_id (str): ID of the document to update. document (Document): Document to update. )rXrr N)r#r$r<update)r? document_iddocumenttextr$s rupdate_documentzBagel.update_documents@$$$$  fj  rJcX|||||d}|||d<|jjdi|S)zGets the collection.)rXrOlimitoffsetwhere_documentincluderP)r<get)r?rXrOrrrrrTs rrz Bagel.getsF,     'F9  t}}  *6**rJc <|jj|y)zW Delete by IDs. Args: ids: List of ids to delete. )rXN)r<delete)r?rXrTs rrz Bagel.deletes %rJ)r@r+rAOptional[bagel.config.Settings]rBOptional[Embeddings]rCOptional[Dict]rDOptional[bagel.Client]rEz"Optional[Callable[[float], float]]returnNone)rr)NNN) rKOptional[List[str]]rLOptional[List[List[float]]]rNintrOOptional[Dict[str, str]]rTrrList[Document])NNN) rUz Iterable[str]r Optional[List[dict]]rXrrIrrTrr List[str]) rmr+rnrrOrrTrrr) rmr+rnrrOrrTrrList[Tuple[Document, float]])ru Type[Bagel]rUrrvrr rrXrr@r+rArrCrrDrrwrrTrrr))rr) rL List[float]rnrrOrrTrrr) rvrrnrrOrrTrrr)rzCallable[[float], float])rurrrrvrrXrr@r+rArrDrrCrrTrrr))rr+rrrr)NNNNNN)rXzOptional[OneOrMany[ID]]rOzOptional[Where]r Optional[int]rrrzOptional[WhereDocument]rrrzDict[str, Any]r)rXrrTrrr)__name__ __module__ __qualname____doc__r,__annotations__rFpropertyrIrrsrj DEFAULT_Krprl classmethodryr{r~rrrrrrrPrJrr)r),s ,7#S6<;?37+/)-AE"6"69"61 "6 ) "6 ' "6?"6 "6H((12,08<*.  ( 6   (      3 <+/#'26 GG(G! G 0 G  G GX*. 33 3( 3  3  32*. 44 4( 4  4 & 42+/*.#';;?+/)-7;( ((((( ( ! (  (9()('(5(( ((T8*. 4% 4  4( 4  4 & 4$*. ) )  )( )  )  )8+/#';;?)-+/( ( !( (( ! (  ( 9 ( '( )( (  ( ( T  "(,!%# $26'++ $++ +  + 0 +%+ +.&rJr))rrrr)rrrr) __future__rrYtypingrrrrrr r r r r3r4bagel.api.typesr rrrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.utilsrlangchain_core.vectorstoresrrrrr)rPrJrrsU"    CC-0)3 DI&KI&rJ