jOdZddlZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZdd lmZGd d eZd ed edefdZy)zMilvus RetrieverN)AnyDictListOptional)CallbackManagerForRetrieverRun)Document) Embeddings) BaseRetriever)model_validator)Milvusc eZdZUdZeed<dZeed<dZe e ee fed<dZ e e ee fed<dZ eed <dZe eed <eed <eed <ed ede de fdZ ddeede eeddfdZdedede deefdZy)MilvusRetrieveraoMilvus API retriever. See detailed instructions here: https://python.langchain.com/docs/integrations/retrievers/milvus_hybrid_search/ Setup: Install ``langchain-milvus`` and other dependencies: .. code-block:: bash pip install -U pymilvus[model] langchain-milvus Key init args: collection: Milvus Collection Instantiate: .. code-block:: python retriever = MilvusCollectionHybridSearchRetriever(collection=collection) Usage: .. code-block:: python query = "What are the story about ventures?" retriever.invoke(query) .. code-block:: none [Document(page_content="In 'The Lost Expedition' by Caspian Grey...", metadata={'doc_id': '449281835035545843'}), Document(page_content="In 'The Phantom Pilgrim' by Rowan Welles...", metadata={'doc_id': '449281835035545845'}), Document(page_content="In 'The Dreamwalker's Journey' by Lyra Snow..", metadata={'doc_id': '449281835035545846'})] Use within a chain: .. code-block:: python from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI prompt = ChatPromptTemplate.from_template( """Answer the question based only on the context provided. Context: {context} Question: {question}""" ) llm = ChatOpenAI(model="gpt-3.5-turbo-0125") def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser() ) chain.invoke("What novels has Lila written and what are their contents?") .. code-block:: none "Lila Rose has written 'The Memory Thief,' which follows a charismatic thief..." embedding_functionLangChainCollectioncollection_nameNcollection_propertiesconnection_argsSessionconsistency_level search_paramsstore retrieverbefore)modevaluesreturnct|d|d|d|d|d|d<|djd|di |d <|S) z&Create the Milvus store and retriever.rrrrrrparamr) search_kwargsr)r as_retriever)clsrs o/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/retrievers/milvus.pycreate_retrieverz MilvusRetriever.create_retriever`ss! ' ( $ % * + $ % & '  w%Wo::"F?$;<; { texts metadatasc<|jj||y)zAdd text to the Milvus store Args: texts (List[str]): The text metadatas (List[dict]): Metadata dicts, must line up with existing store N)r add_texts)selfr%r&s r"r(zMilvusRetriever.add_textsps UI.r$query run_managerkwargsc \|jj|fd|ji|S)Nr+)rinvoke get_child)r)r*r+r,s r"_get_relevant_documentsz'MilvusRetriever._get_relevant_documents{s9%t~~$$  *446 :@  r$)N)__name__ __module__ __qualname____doc__r __annotations__rstrrrrrrrrdictr r r classmethodr#rr(rrr0r$r"rrsBH#"0OS06:8DcN3:04OXd38n-4&s&$(M8D>( M(# d s $ CG /#Y /+3DJ+? /  /    4    h  r$rargsr,rcLtjdtt|i|S)zDeprecated MilvusRetreiver. Please use MilvusRetriever ('i' before 'e') instead. Args: *args: **kwargs: Returns: MilvusRetriever zfMilvusRetreiver will be deprecated in the future. Please use MilvusRetriever ('i' before 'e') instead.)warningswarnDeprecationWarningr)r:r,s r"MilvusRetreiverr?s* MM ? D +F ++r$)r4r<typingrrrrlangchain_core.callbacksrlangchain_core.documentsrlangchain_core.embeddingsr langchain_core.retrieversr pydanticr 'langchain_community.vectorstores.milvusr rr?r9r$r"rGsL,,C-03$: s ms l,3,#,/,r$