a bg@s|ddlmZddlmZmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZddlmZmZGdd d eZd S) ) annotations)AnyDictListOptionalcast)uuid4)CallbackManagerForRetrieverRun)Document) BaseRetriever) ConfigDictmodel_validatorc@seZdZUdZdZded<ded<ded<dZd ed <d Zd ed <ded<dZded<e dde dddddZ e ddZ ddddddZdddddd d!dd!dd"d#d$ZdS)%WeaviateHybridSearchRetrieverz{`Weaviate hybrid search` retriever. See the documentation: https://weaviate.io/blog/hybrid-search-explained Nrclientstr index_nametext_keyg?floatalphaintkz List[str] attributesTboolcreate_schema_if_missingbefore)modezDict[str, Any])valuesreturncCsz ddl}Wnty&tdYn0t|d|jsR|d}tdt||ddurhg|d<tt|d |d|ddr|d |dd gd gd d }|dj |d s|dj ||S)Nrz_Could not import weaviate python package. Please install it with `pip install weaviate-client`.rz5client should be an instance of weaviate.Client, got rrrTrtext)nameZdataTypeztext2vec-openai)class propertiesZ vectorizer) weaviate ImportError isinstanceZClient ValueErrortypegetrrappendZschemaexistsZ create_class)clsrr#rZ class_objr,/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/retrievers/weaviate_hybrid_search.pyvalidate_client"s,     z-WeaviateHybridSearchRetriever.validate_client)Zarbitrary_types_allowedzList[Document])docskwargsrc Ksddlm}|jjz}g}t|D]\\}}|jp2i}|j|ji|} d|vrZ|d|} n |t} | | |j | | | q"Wdn1s0Y|S)zUpload documents to Weaviate.r)get_valid_uuidZuuidsN) Z weaviate.utilr1rbatch enumeratemetadatar page_contentrZadd_data_objectrr)) selfr/r0r1r2Zidsidocr4Zdata_propertiesZ_idr,r,r- add_documentsJs    *z+WeaviateHybridSearchRetriever.add_documentsF) where_filterscorehybrid_search_kwargsr zOptional[Dict[str, object]])query run_managerr:r;r<rc Cs|jj|j|j}|r"||}|r4|ddg}|dur@i}|j|fd|ji| |j  }d|vr~t d|dg}|dd|jD]"} | |j} |t| | d q|S) aDLook up similar documents in Weaviate. query: The query to search for relevant documents of using weviate hybrid search. where_filter: A filter to apply to the query. https://weaviate.io/developers/weaviate/guides/querying/#filtering score: Whether to include the score, and score explanation in the returned Documents meta_data. hybrid_search_kwargs: Used to pass additional arguments to the .with_hybrid() method. The primary uses cases for this are: 1) Search specific properties only - specify which properties to be used during hybrid search portion. Note: this is not the same as the (self.attributes) to be returned. Example - hybrid_search_kwargs={"properties": ["question", "answer"]} https://weaviate.io/developers/weaviate/search/hybrid#selected-properties-only 2) Weight boosted searched properties - Boost the weight of certain properties during the hybrid search portion. Example - hybrid_search_kwargs={"properties": ["question^2", "answer"]} https://weaviate.io/developers/weaviate/search/hybrid#weight-boost-searched-properties 3) Search with a custom vector - Define a different vector to be used during the hybrid search portion. Example - hybrid_search_kwargs={"vector": [0.1, 0.2, 0.3, ...]} https://weaviate.io/developers/weaviate/search/hybrid#with-a-custom-vector 4) Use Fusion ranking method Example - from weaviate.gql.get import HybridFusion hybrid_search_kwargs={"fusion": fusion_type=HybridFusion.RELATIVE_SCORE} https://weaviate.io/developers/weaviate/search/hybrid#fusion-ranking-method r;Z explainScoreNrerrorszError during query: dataZGet)r5r4)rr=r(rrZ with_whereZwith_additionalZ with_hybridrZ with_limitrdor&poprr)r ) r6r=r>r:r;r<Z query_objresultr/resrr,r,r-_get_relevant_documents_s$,  z5WeaviateHybridSearchRetriever._get_relevant_documents)__name__ __module__ __qualname____doc__r__annotations__rrrr classmethodr.r Z model_configr9rEr,r,r,r-r s&     !rN) __future__rtypingrrrrruuidrZlangchain_core.callbacksr Zlangchain_core.documentsr Zlangchain_core.retrieversr Zpydanticr r rr,r,r,r-s