a bgm0@sddlmZddlZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZddlmZddlmZerddlmZddlmZGd d d eeZe d eeefd ZeeZd ZGdddeZdS)) annotationsN)Enum) TYPE_CHECKINGAnyDict GeneratorIterableListOptionalTypeVarUnion)Document) VectorStore) Embeddings) Collectionc@seZdZdZdZdZdZdS)DocumentDBSimilarityTypez)DocumentDB Similarity Type as enumerator.ZcosineZ dotProductZ euclideanN)__name__ __module__ __qualname____doc__COSDOTZEUCrry/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/vectorstores/documentdb.pyrs rDocumentDBDocumentType)boundc @sLeZdZdZdddddddddd d d Zedd d dZdd ddZeddddddddZ dd ddZ dd ddZ de j ddfddddd d!d"d#ZdJd%d&dd'd(d)d*Zd+d,d'd-d.d/ZedKd+dd0d1ddd2d3d4ZdLd5dd6d7d8d9ZdMd:dd;d") collection = mongo_client[""][""] embeddings = OpenAIEmbeddings() vectorstore = DocumentDBVectorSearch(collection, embeddings) ZvectorSearchIndexZ textContentZ vectorContent) index_nametext_key embedding_keyz"Collection[DocumentDBDocumentType]rstr) collection embeddingrrr cCs*||_||_||_||_||_tj|_dS)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)selfr"r#rrr rrr__init__Bs zDocumentDBVectorSearch.__init__)returncCs|jSN)r%r*rrr embeddings]sz!DocumentDBVectorSearch.embeddingscCs|jS)zUReturns the index name Returns: Returns the index name )r&r.rrrget_index_nameasz%DocumentDBVectorSearch.get_index_namer)connection_string namespacer#kwargsr,c Ks`zddlm}Wnty*tdYn0||}|d\}}|||} || |fi|S)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`..)Zpymongor4 ImportErrorsplit) clsr1r2r#r3r4clientZdb_nameZcollection_namer"rrrfrom_connection_stringjs   z-DocumentDBVectorSearch.from_connection_stringboolcCs6|j}|j}|D]}|d}||krdSqdS)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$Z list_indexesr&pop)r*cursorrresZcurrent_index_namerrr index_existss  z#DocumentDBVectorSearch.index_existsNonecCs|r|j|jdS)zEDeletes the index specified during instance construction if it existsN)r@r$Z drop_indexr&r.rrr delete_indexsz#DocumentDBVectorSearch.delete_indexi@intrzdict[str, Any]) dimensions similaritymef_constructionr,c CsH||_|jj|j|jdid||||ddgd}|jj}||}|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 vectorZhnsw)typerGrFrHZefConstruction)r<keyZ vectorOptions)Z createIndexesZindexes)r)r$r<r&r(Zdatabasecommand)r*rFrGrHrIZcreate_index_commandsZcurrent_databaseZcreate_index_responsesrrr create_indexs$ z#DocumentDBVectorSearch.create_indexNz Iterable[str]zOptional[List[Dict[str, Any]]]r )texts metadatasr3r,c Ks|dt}|pdd|D}g}g}g}tt||D]J\} \} } || || | d|dkr8||||g}g}q8|r|||||S)N batch_sizecss|] }iVqdSr-r).0_rrr z3DocumentDBVectorSearch.add_texts..r)getDEFAULT_INSERT_BATCH_SIZE enumeratezipappendextend _insert_texts) r*rOrPr3rQZ _metadatasZ texts_batchZmetadatas_batchZ result_idsitextmetadatarrr add_textss   z DocumentDBVectorSearch.add_textsz List[str]zList[Dict[str, Any]])rOrPr,cs@|sgSj|}fddt|||D}j|}|jS)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: cs&g|]\}}}j|j|i|qSr)r'r()rRtrHr#r.rr sz8DocumentDBVectorSearch._insert_texts..)r%Zembed_documentsrZr$Z insert_manyZ inserted_ids)r*rOrPr/Z to_insertZ insert_resultrr.rr]s     z$DocumentDBVectorSearch._insert_textszOptional[List[dict]]z,Optional[Collection[DocumentDBDocumentType]])rOr#rPr"r3r,cKs4|durtd|||fi|}|j||d|S)Nz*Must provide 'collection' named parameter.)rP) ValueErrorra)r8rOr#rPr"r3Z vectorstorerrr from_textss z!DocumentDBVectorSearch.from_textszOptional[List[str]]zOptional[bool])idsr3r,cKs(|durtd|D]}||qdS)Nz#No document ids provided to delete.T)rddelete_document_by_id)r*rfr3 document_idrrrdeletes  zDocumentDBVectorSearch.deletez Optional[str])rhr,c Cshzddlm}Wn.ty>}ztd|WYd}~n d}~00|durPtd|jd||idS)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.Z_id)Z bson.objectidrjr6rdr$Z delete_one)r*rhrjerrrrg&sz,DocumentDBVectorSearch.delete_document_by_id(z List[float]zOptional[Dict[str, Any]]zList[Document])r/k ef_searchfilterr,c Csh|si}d|idd||j|j||diig}|j|}g}|D]"}||j} |t| |dq@|S)aReturns a list of documents. Args: embeddings: The query vector k: the number of documents to return ef_search: Specifies the size of the dynamic candidate list that HNSW index uses during search. A higher value of efSearch provides better recall at cost of speed. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: A list of documents closest to the query vector z$matchz$searchZ vectorSearch)rJpathrGrnZefSearch)Z page_contentr`)r(r)r$Z aggregater=r'r[r ) r*r/rnrorpZpipeliner>docsr?r_rrr _similarity_search_without_score7s(  z7DocumentDBVectorSearch._similarity_search_without_score)rp)queryrnrorpr3r,cKs,|j|}|j||||d}dd|DS)N)r/rnrorpcSsg|]}|qSrr)rRdocrrrrctrUz.)r%Z embed_queryrs)r*rtrnrorpr3r/rrrrrsimilarity_searchgs z(DocumentDBVectorSearch.similarity_search)N)NN)N)N)rlrmN)rlrm)rrrrr+propertyr/r0 classmethodr:r@rBrrrNrar]rerirgrsrvrrrrr,sF  A3r) __future__rloggingenumrtypingrrrrrr r r r Zlangchain_core.documentsr Zlangchain_core.vectorstoresrZlangchain_core.embeddingsrZpymongo.collectionrr!rr getLoggerrloggerrXrrrrrs  ,