j^0ddlmZddlZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZddlmZddlmZer ddlmZddlmZGd d eeZe d eeef Zej4eZd ZGddeZy)) annotationsN)Enum) TYPE_CHECKINGAnyDict GeneratorIterableListOptionalTypeVarUnion)Document) VectorStore) Embeddings) Collectionc eZdZdZdZ dZ dZy)DocumentDBSimilarityTypez)DocumentDB Similarity Type as enumerator.cosine dotProduct euclideanN)__name__ __module__ __qualname____doc__COSDOTEUCu/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/vectorstores/documentdb.pyrrs3 C C CrrDocumentDBDocumentType)boundcleZdZdZdddd ddZeddZddZe dd Z dd Z dd Z d e jd df ddZ d d!dZd"dZe d# d$dZd d%dZd d&dZ d' d(dZ d)dd d*dZy)+DocumentDBVectorSearcha`Amazon DocumentDB (with MongoDB compatibility)` vector store. Please refer to the official Vector Search documentation for more details: https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html To use, you should have both: - the ``pymongo`` python package installed - a connection string and credentials associated with a DocumentDB cluster Example: . code-block:: python from langchain_community.vectorstores import DocumentDBVectorSearch from langchain_community.embeddings.openai import OpenAIEmbeddings from pymongo import MongoClient mongo_client = MongoClient("") collection = mongo_client[""][""] embeddings = OpenAIEmbeddings() vectorstore = DocumentDBVectorSearch(collection, embeddings) vectorSearchIndex textContent vectorContent) index_nametext_key embedding_keyct||_||_||_||_||_t j |_y)aConstructor for DocumentDBVectorSearch Args: collection: MongoDB collection to add the texts to. embedding: Text embedding model to use. index_name: Name of the Vector Search index. text_key: MongoDB field that will contain the text for each document. embedding_key: MongoDB field that will contain the embedding for each document. N) _collection _embedding _index_name _text_key_embedding_keyrr_similarity_type)self collection embeddingr)r*r+s r __init__zDocumentDBVectorSearch.__init__Bs8(&#%!+ 8 < <rc|jSN)r.r3s r embeddingsz!DocumentDBVectorSearch.embeddings]s rc|jS)zUReturns the index name Returns: Returns the index name )r/r9s r get_index_namez%DocumentDBVectorSearch.get_index_nameasrc  ddlm}||}|jd\}}|||} || |fi|S#t$r tdwxYw)aCreates an Instance of DocumentDBVectorSearch from a Connection String Args: connection_string: The DocumentDB cluster endpoint connection string namespace: The namespace (database.collection) embedding: The embedding utility **kwargs: Dynamic keyword arguments Returns: an instance of the vector store r) MongoClientzGCould not import pymongo, please install it with `pip install pymongo`..)pymongor> ImportErrorsplit) clsconnection_string namespacer5kwargsr>clientdb_namecollection_namer4s r from_connection_stringz-DocumentDBVectorSearch.from_connection_stringjsm(  + **;<#,??3#7 G__5 :y3F33 )  s 6A c|jj}|j}|D]}|jd}||k(syy)zVerifies if the specified index name during instance construction exists on the collection Returns: Returns True on success and False if no such index exists on the collection nameTF)r- list_indexesr/pop)r3cursorr)rescurrent_index_names r index_existsz#DocumentDBVectorSearch.index_existssK!!..0%% C!$ !Z/ rcp|jr&|jj|jyy)zEDeletes the index specified during instance construction if it existsN)rRr- drop_indexr/r9s r delete_indexz#DocumentDBVectorSearch.delete_indexs-        ' '(8(8 9 ri@c ||_|jj|j|jdid||||ddgd}|jj }|j |}|S)aCreates an index using the index name specified at instance construction Args: dimensions: Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000 similarity: Similarity algorithm to use with the HNSW index. Possible options are: - DocumentDBSimilarityType.COS (cosine distance), - DocumentDBSimilarityType.EUC (Euclidean distance), and - DocumentDBSimilarityType.DOT (dot product). m: Specifies the max number of connections for an HNSW index. Large impact on memory consumption. ef_construction: Specifies the size of the dynamic candidate list for constructing the graph for HNSW index. Higher values lead to more accurate results but slower indexing speed. Returns: An object describing the created index vectorhnsw)type similarity dimensionsmefConstruction)rLkey vectorOptions) createIndexesindexes)r2r-rLr/r1databasecommand)r3r]r\r^ef_constructioncreate_index_commandscurrent_databasecreate_index_responsess r create_indexz#DocumentDBVectorSearch.create_indexs@!+"--22!,, //: &&0&0*9 &  ! $ ++442B1I1I !2 &%rNc |jdt}|xs d|D}g}g}g}tt||D][\} \} } |j | |j | | dz|zdk(s7|j |j ||g}g}]|r!|j |j |||S)N batch_sizec3 K|]}iywr8r).0_s r z3DocumentDBVectorSearch.add_texts..s:Mu!2us r)getDEFAULT_INSERT_BATCH_SIZE enumeratezipappendextend _insert_texts) r3texts metadatasrFrl _metadatas texts_batchmetadatas_batch result_idsitextmetadatas r add_textsz DocumentDBVectorSearch.add_textss ZZ .GH -6-M:Mu:M   #,S -C#D Ah   t $  " "8 ,A#q(!!$"4"4[/"RS "$ $E    d00oN Orc |sgS|jj|}t|||Dcgc]"\}}}|j||j|i|$}}}}|j j |}|jScc}}}w)zUsed to Load Documents into the collection Args: texts: The list of documents strings to load metadatas: The list of metadata objects associated with each document Returns: )r.embed_documentsrur0r1r- insert_many inserted_ids) r3ryrzr:tr^r5 to_insert insert_results r rxz$DocumentDBVectorSearch._insert_textssI__44U; $'ui#D #D1i^^Q 3 3Y D! D#D  ((44Y? )))  s'Bc Z| td|||fi|}|j|||S)Nz*Must provide 'collection' named parameter.)rz) ValueErrorr)rCryr5rzr4rF vectorstores r from_textsz!DocumentDBVectorSearch.from_textss@  IJ J*i:6: ey9rc N| td|D]}|j|y)Nz#No document ids provided to delete.T)rdelete_document_by_id)r3idsrF document_ids r deletezDocumentDBVectorSearch.deletes. ;BC CK  & &{ 3rc ddlm}| td|jj d||iy#t$r}td|d}~wwxYw)zjRemoves a Specific Document by Id Args: document_id: The document identifier r)ObjectIdz>Unable to import bson, please install with `pip install bson`.Nz"No document id provided to delete._id) bson.objectidrrArr- delete_one)r3rres r rz,DocumentDBVectorSearch.delete_document_by_id&sa   .  AB B ##UH[,A$BC P  s9 A AAc  |si}d|idd||j|j||diig}|jj|}g}|D]9}|j |j } |j t| |;|S)aReturns a list of documents. Args: embeddings: The query vector k: the number of documents to return ef_search: Specifies the size of the dynamic candidate list that HNSW index uses during search. A higher value of efSearch provides better recall at cost of speed. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: A list of documents closest to the query vector z$matchz$search vectorSearch)rYpathr\kefSearch) page_contentr)r1r2r- aggregaterNr0rvr) r3r:r ef_searchfilterpipelinerOdocsrPrs r _similarity_search_without_scorez7DocumentDBVectorSearch._similarity_search_without_score7s*F v "", $ 3 3&*&;&;$- %  * !!++H5C774>>*D KKdSA B r)rc |jj|}|j||||}|Dcgc]}|c}Scc}w)N)r:rrr)r. embed_queryr) r3queryrrrrFr:rdocs r similarity_searchz(DocumentDBVectorSearch.similarity_searchgsP__007 44!Q)F5  $$tt$$$s A) r4z"Collection[DocumentDBDocumentType]r5rr)strr*rr+r)returnr)rr) rDrrErr5rrFrrr%)rbool)rNone) r]intr\rr^rrfrrzdict[str, Any]r8)ryz Iterable[str]rzzOptional[List[Dict[str, Any]]]rFrrr )ry List[str]rzzList[Dict[str, Any]]rr )NN) ryrr5rrzzOptional[List[dict]]r4z,Optional[Collection[DocumentDBDocumentType]]rFrrr%)rzOptional[List[str]]rFrrzOptional[bool])rz Optional[str]rr)(N) r:z List[float]rrrrrOptional[Dict[str, Any]]rList[Document])rr) rrrrrrrrrFrrr)rrrrr6propertyr:r< classmethodrJrRrUrrrjrrxrrrrrrrr r%r%,s!4.%,=6==  =  ==6 444 4  4 44>$:/G/K/K! <&<&-<&  <&  <&  <&B592   ,*0 +/CG   (  A      D(+/ .. . . ) .  .f % ,0 % %  % % ) % %  %rr%) __future__rloggingenumrtypingrrrrr r r r r langchain_core.documentsrlangchain_core.vectorstoresrlangchain_core.embeddingsrpymongo.collectionrrrr! getLoggerrloggerrsr%rrr rsz"   .34-sD!!9c3hP   8 $H%[H%r