U h.@sVdZddlmZddlZddlZddlmZddlmZm Z m Z m Z ddl m Z ddlmZmZmZddlmZdd lmZdd lmZdd lmZmZdd lmZdd lmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*e ddddGddde Z+e ddddGddde+Z,e ddddGddde+Z-dS)7Chain for question-answering against a vector database.) annotationsN)abstractmethod)AnyDictListOptional) deprecated)AsyncCallbackManagerForChainRunCallbackManagerForChainRun Callbacks)Document)BaseLanguageModel)PromptTemplate)Fieldroot_validator) BaseRetriever) VectorStore)Chain)BaseCombineDocumentsChain)StuffDocumentsChain)LLMChain load_qa_chain)PROMPT_SELECTORz0.2.13z1.0zThis class is deprecated. Use the `create_retrieval_chain` constructor instead. 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If chain has 'return_source_documents' as 'True', returns the retrieved documents as well under the key 'source_documents'. Example: .. code-block:: python res = indexqa({'query': 'This is my query'}) answer, docs = res['result'], res['source_documents'] rErEZinput_documentsrDr5r1N) r get_noop_managerr inspect signaturerH parametersrrun get_childr$r"r/rJrEZ _run_managerrDZaccepts_run_managerdocsZanswerr)r)r*_calls   zBaseRetrievalQA._callr csdSrFr)rGr)r)r* _aget_docsszBaseRetrievalQA._aget_docsz)Optional[AsyncCallbackManagerForChainRun]cs|p t}||j}dt|jjk}|rB|j||dIdH}n||IdH}|jj||| dIdH}|j r|j |d|iS|j |iSdS)rKrErLNrMr1) r rNr rOrPrWrQrZarunrSr$r"rTr)r)r*_acalls   zBaseRetrievalQA._acall)NNN)r>N)N)N)r%r&r'__doc____annotations__r r"r$r+propertyr0r2 classmethodr=rArrHrVrWrXr)r)r)r*rs6      " rz0.1.17c@sZeZdZUdZeddZded<dddd d d Zdd dd d dZe ddddZ dS) RetrievalQAaChain for question-answering against an index. This class is deprecated. See below for an example implementation using `create_retrieval_chain`: .. code-block:: python from langchain.chains import create_retrieval_chain from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI retriever = ... # Your retriever llm = ChatOpenAI() system_prompt = ( "Use the given context to answer the question. " "If you don't know the answer, say you don't know. " "Use three sentence maximum and keep the answer concise. " "Context: {context}" ) prompt = ChatPromptTemplate.from_messages( [ ("system", system_prompt), ("human", "{input}"), ] ) question_answer_chain = create_stuff_documents_chain(llm, prompt) chain = create_retrieval_chain(retriever, question_answer_chain) chain.invoke({"input": query}) Example: .. code-block:: python from langchain_community.llms import OpenAI from langchain.chains import RetrievalQA from langchain_community.vectorstores import FAISS from langchain_core.vectorstores import VectorStoreRetriever retriever = VectorStoreRetriever(vectorstore=FAISS(...)) retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever) T)excluder retrieverrr rBrCcCs|jj|d|idS) Get docs.r5config)r_ZinvokerSrGr)r)r*rHs zRetrievalQA._get_docsr cs|jj|d|idIdHS)r`r5raN)r_ZainvokerSrGr)r)r*rWs zRetrievalQA._aget_docsr,cCsdS)Return the chain type.Z retrieval_qar)r.r)r)r* _chain_typeszRetrievalQA._chain_typeN) r%r&r'rYrr_rZrHrWr[rdr)r)r)r*r]s -  r]c@seZdZUdZedddZded<dZded<d Zd ed <ee d Z d ed<e dddddddZ e dddddddZ d dddddZd dddddZed ddd Zd!S)" VectorDBQArT vectorstore)r^aliasrintk similarityr search_type)default_factoryrI search_kwargs)prer)valuesr-cCstd|S)NzR`VectorDBQA` is deprecated - please use `from langchain.chains import RetrievalQA`)warningswarn)r<rpr)r)r*raise_deprecation8szVectorDBQA.raise_deprecationcCs,d|kr(|d}|dkr(td|d|S)zValidate search type.rl)rkmmrsearch_type of not allowed.) 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