a bgO@sdZddlZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZdd lmZGd d d eZeeed d dZdS)zMilvus RetrieverN)AnyDictListOptional)CallbackManagerForRetrieverRun)Document) Embeddings) BaseRetriever)model_validator)Milvusc@seZdZUdZeed<dZeed<dZe e ee fed<dZ e e ee fed<dZ eed <dZe eed <eed <eed <ed dee e dddZdeee eeddddZeee eedddZdS)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_functionZLangChainCollectioncollection_nameNcollection_propertiesconnection_argsSessionconsistency_level search_paramsstore retrieverbefore)mode)valuesreturncCsHt|d|d|d|d|d|d<|djd|did |d <|S) z&Create the Milvus store and retriever.r rrrrrparamr)Z search_kwargsr)r Z as_retriever)clsrrs/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/retrievers/milvus.pycreate_retriever`s z MilvusRetriever.create_retriever)texts metadatasrcCs|j||dS)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)selfrr rrrr!ps zMilvusRetriever.add_texts)query run_managerkwargsrcKs|jj|fd|i|S)Nr$)rZinvokeZ get_child)r"r#r$r%rrr_get_relevant_documents{sz'MilvusRetriever._get_relevant_documents)N)__name__ __module__ __qualname____doc__r__annotations__rstrrrrrrrrdictr r r classmethodrrr!rrr&rrrrr s, D   r )argsr%rcOstdtt|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%rrrMilvusRetreivers r3)r*r0typingrrrrZlangchain_core.callbacksrZlangchain_core.documentsrZlangchain_core.embeddingsrZlangchain_core.retrieversr Zpydanticr Z'langchain_community.vectorstores.milvusr r r3rrrrs      v