tj;ddlmZddlZddlmZmZmZmZmZm Z m Z m Z m Z erddl Z ddlZ ddlmZmZmZmZddlmZddlmZddlmZddlmZd ZddZddZGddeZdS)) annotationsN) TYPE_CHECKINGAnyCallableDictIterableListOptionalTupleType)ID OneOrManyWhere WhereDocumentDocument) Embeddings)xor_args) VectorStoreresultsrreturnList[Document]c4dt|DS)Ncg|]\}}|Sr.0doc_s d/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/vectorstores/bagel.py z$_results_to_docs..s C C CFCC C C C)_results_to_docs_and_scoresrs r!_results_to_docsr&s C C9'BB C C CCr#List[Tuple[Document, float]]cdt|dd|dd|ddDS)Nc\g|])}t|d|dpi|df*S)r) page_contentmetadatar)rresults r!r"z/_results_to_docs_and_scores.."sJ     vay6!9? C C CVAYO   r# documentsr metadatas distances)zipr%s r!r$r$!sT   K  # K  # K  #     r#c TeZdZUdZdZded<edddddfdLdZedMdZe d dNdOd"Z dPdQd*Z e dfdRd-Z e dfdSd/ZedddeddddfdTd4ZdUd5Ze dfdVd7Ze dfdWd8ZdXd:ZeddedddfdYd<ZdZd@Z d[d\dJZd]d^dKZdS)_Bagela"``Bagel.net`` Inference platform. To use, you should have the ``bagelML`` python package installed. Example: .. code-block:: python from langchain_community.vectorstores import Bagel vectorstore = Bagel(cluster_name="langchain_store") langchainstr_LANGCHAIN_DEFAULT_CLUSTER_NAMEN cluster_nameclient_settingsOptional[bagel.config.Settings]embedding_functionOptional[Embeddings]cluster_metadataOptional[Dict]clientOptional[bagel.Client]relevance_score_fn"Optional[Callable[[float], float]]rNonecN ddl}ddl}n#t$rtdwxYw|||_||_n=|r|}n|jdd}||_|j||_|j|||_ ||_ ||_ dS)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) selfr8r9r;r=r?rArJrMs r!__init__zBagel.__init__:s M LLL      M M MKLL L M  $3D !!DLL #2  #(<#8#8#)&6$9$$ %5D !'5<(899DL ::%;   ,>(#5   s %c|jSN)rUrVs r! embeddingszBagel.embeddings^s ''r#) query_textsquery_embeddingsr\Optional[List[str]]r]Optional[List[List[float]]] n_resultsintwhereOptional[Dict[str, str]]kwargsrrc  ddl}n#t$rtdwxYw|jr/|-|r+t|}|j|}d}|jjd||||d|S)z9Query the Bagel cluster based on the provided parameters.rNrE)r\r]rarcr)rJrLrUlistembed_documentsrSfind)rVr\r]rarcrerJtextss r!__query_clusterzBagel.__query_clusterbs M LLLL M M MKLL L M  # (8(@[(@%%E#7GGNN K!t}! #-       s!rj Iterable[str]r0Optional[List[dict]]idsr[ List[str]c . dDt|jrr|jrttz }|r ig|zzg}g}t D]2\} } | r|| || 3|rZfd|Dfd|D} rfd|Dnd} fd|D} |j| | | |rKfd|D}rfd |Dnd}fd |D}|j||| n1igtz|j S) a Add texts along with their corresponding embeddings and optional metadata to the Bagel cluster. Args: texts (Iterable[str]): Texts to be added. embeddings (Optional[List[float]]): List of embeddingvectors metadatas (Optional[List[dict]]): Optional list of metadatas. ids (Optional[List[str]]): List of unique ID for the texts. Returns: List[str]: List of unique ID representing the added texts. NcNg|]"}ttj#Sr)r6uuiduuid4)rr s r!r"z#Bagel.add_texts..s&4443tz||$$444r#c g|] }| Srr)ridxr0s r!r"z#Bagel.add_texts..sEEEYs^EEEr#c g|] }| Srr)rrurjs r!r"z#Bagel.add_texts..s'L'L'Lsc 'L'L'Lr#c g|] }| Srr)rrur[s r!r"z#Bagel.add_texts..s>>>Z_>>>r#c g|] }| Srr)rrurns r!r"z#Bagel.add_texts..s$G$G$G#SX$G$G$Gr#)r[r0r/rnc g|] }| Srr)rjrjs r!r"z#Bagel.add_texts..s*G*G*G58*G*G*Gr#c g|] }| Srr)rrzr[s r!r"z#Bagel.add_texts..s666qZ]666r#c g|] }| Srr)rrzrns r!r"z#Bagel.add_texts..s(C(C(CAQ(C(C(Cr#)r[r/rn)r[r/r0rn)rgrUrhlen enumerateappendrSupsert)rVrjr0rnr[re length_diff empty_ids non_empty_idsrur,texts_with_metadatasembeddings_with_metadatasids_with_metadatatexts_without_metadatasembeddings_without_metadatasids_without_metadatass ```` r! add_textszBagel.add_texts~sk, ;44e444CU   # I (:u(:1AA%HHJ * e**s9~~5K ;%{(:: IM!*9!5!5 * * X*!((----$$S)))) EEEE}EEE 'L'L'L'Lm'L'L'L$BLV>>>> >>>>RV*%H$G$G$G$G$G$G! $$8'2) %  *G*G*G*GY*G*G*G':DN6666I6666$-)D(C(C(C(C(C(C% $$;5-% s5zz)I M %# !     r#querykc J||||}d|DS)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. )rccg|]\}}|Srrrs r!r"z+Bagel.similarity_search..s222Q222r#)similarity_search_with_score)rVrrrcredocs_and_scoress r!similarity_searchzBagel.similarity_searchs2&;;E1E;RR22/2222r#r'c R||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. )r\rarc_Bagel__query_clusterr$)rVrrrcrers r!rz"Bagel.similarity_search_with_scores.,&&E7au&UU*7333r#cls Type[Bagel] embeddingtext_embeddingsc T|d|||||d| } | || ||} | 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. )r8r;r9r?r=)rjr[r0rnr)r) rrjrr0rnr8r9r=r?rre bagel_clusterr s r! from_textszBagel.from_textsse< %(+-        # #Oyc $  r#cN|j|jjdS)zDelete the cluster.N)rNdelete_clusterrSrIrZs r!rzBagel.delete_cluster!s# ##DM$677777r# List[float]c P||||}t|S)zT Return docs most similar to embedding vector and similarity score. r]rarcr)rVr]rrcrers r!1similarity_search_by_vector_with_relevance_scoresz7Bagel.similarity_search_by_vector_with_relevance_scores%s5&&-%'  +7333r#c P||||}t|S)z-Return docs most similar to embedding vector.r)rr&)rVrrrcrers r!similarity_search_by_vectorz!Bagel.similarity_search_by_vector4s5&&&!5'   (((r#Callable[[float], float]c|jr|jSd}d}|jj}|r ||vr||}|dkr|jS|dkr|jS|dkr|jSt 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.)rTrSr,_cosine_relevance_score_fn_euclidean_relevance_score_fn%_max_inner_product_relevance_score_fn ValueError)rVdistance distance_keyr,s r!_select_relevance_score_fnz Bagel._select_relevance_score_fnAs  + 43 3# =)  . 00 -H x  2 2   5 5   = =<$,<<< r#r/c ^d|D} d|D} |jd| || |||||d|S)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. cg|] }|j Sr)r+rrs r!r"z(Bagel.from_documents..zs777c!777r#cg|] }|j Sr)r,rs r!r"z(Bagel.from_documents..{s777cS\777r#)rjrr0rnr8r9r?r=r)r) rr/rrnr8r9r?r=rerjr0s r!from_documentszBagel.from_documents]sl:87Y77777Y777 s~  %+-      r# document_iddocumentrcb|j}|j}|j|g|g|gdS)zUpdate a document in the cluster. Args: document_id (str): ID of the document to update. document (Document): Document to update. )rnr/r0N)r+r,rSupdate)rVrrtextr,s r!update_documentzBagel.update_documentsL$$  fj      r#Optional[OneOrMany[ID]]Optional[Where]limit Optional[int]offsetwhere_documentOptional[WhereDocument]includeDict[str, Any]cD|||||d}|||d<|jjdi|S)zGets the collection.)rnrcrrrNrr)rSget)rVrnrcrrrrres r!rz Bagel.getsL,      'F9  t} **6***r#c <|j|dS)zW Delete by IDs. Args: ids: List of ids to delete. )rnN)rSdelete)rVrnres r!rz Bagel.deletes# %%%%%r#)r8r6r9r:r;r<r=r>r?r@rArBrrC)rr<)NNr^N) r\r_r]r`rarbrcrdrerrr)NNN) rjrlr0rmrnr_r[r`rerrro) rr6rrbrcrdrerrr) rr6rrbrcrdrerrr')rrrjrorr<r0rmrnr_r8r6r9r:r=r>r?r@rr`rerrr4)rrC) r]rrrbrcrdrerrr') rrrrbrcrdrerrr)rr)rrr/rrr<rnr_r8r6r9r:r?r@r=r>rerrr4)rr6rrrrC)NNNNNN)rnrrcrrrrrrrrr_rrrY)rnr_rerrrC)__name__ __module__ __qualname____doc__r7__annotations__rWpropertyr[rrr DEFAULT_Krr classmethodrrrrrrrrrrr#r!r4r4,sk  ,7#6666<;?37+/)-AE"6"6"6"6"6H(((X(X122,08<*.     32 <+/#'26 GGGGGX*. 333332*. 444442+/*.#';;?+/)-7;(((([(T8888*. 4 4 4 4 4$*. ) ) ) ) )8+/#';;?)-+/( ( ( ( [( T     "(,!%# $26'++++++.&&&&&&&r#r4)rrrr)rrrr') __future__rrrtypingrrrrrr r r r rJrKbagel.api.typesr rrrlangchain_core.documentsrlangchain_core.embeddingsrlangchain_core.utilsrlangchain_core.vectorstoresrrr&r$r4rr#r!rs""""""                       DLLLCCCCCCCCCCCC------000000))))))333333 DDDDI&I&I&I&I&KI&I&I&I&I&r#