a ag.@sZdZddlmZddlZddlZddlmZddlmZm Z m Z m Z ddl m Z ddlmZmZmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZmZmZddl m!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) BaseRetriever) VectorStore) ConfigDictFieldmodel_validator)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'] rBrBZinput_documentsrAr2r.N) r get_noop_managerr!inspect signaturerE parametersrrun get_childr%r#r*rGrBZ _run_managerrAZaccepts_run_managerdocsZanswerr+r+r,_calls    zBaseRetrievalQA._callr csdSrCr+rDr+r+r, _aget_docsszBaseRetrievalQA._aget_docsz)Optional[AsyncCallbackManagerForChainRun]cs|p t}||j}dt|jjv}|rB|j||dIdH}n||IdH}|jj||| dIdH}|j r|j |d|iS|j |iSdS)rHrBrINrJr.) r rKr!rLrMrTrNrZarunrPr%r#rQr+r+r,_acalls   zBaseRetrievalQA._acall)NNN)r;N)N)N)__name__ __module__ __qualname____doc____annotations__r!r#r%rZ model_configpropertyr-r/ classmethodr:r>rrErSrTrUr+r+r+r,rs>      " rz0.1.17c@sZeZdZUdZeddZded<dddd d d Zdd dd d dZe ddddZ dS) RetrievalQAaChain for question-answering against an index. This class is deprecated. 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" "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 retrieverr r r?r@cCs|jj|d|idS) Get docs.r2config)r_ZinvokerPrDr+r+r,rEs zRetrievalQA._get_docsr cs|jj|d|idIdHS)r`r2raN)r_ZainvokerPrDr+r+r,rTs zRetrievalQA._aget_docsr'cCsdS)Return the chain type.Z retrieval_qar+r)r+r+r, _chain_typeszRetrievalQA._chain_typeN) rVrWrXrYrr_rZrErTr[rdr+r+r+r,r]s -  r]c@seZdZUdZedddZded<dZded<d Zd ed <ee d Z d ed<e dde dddddZ e dde dddddZd dddddZd dddddZed d d!d"Zd#S)$ VectorDBQArT vectorstore)r^aliasrintk similarityr search_type)default_factoryrF search_kwargsbefore)moderr)valuesr(cCstd|S)NzR`VectorDBQA` is deprecated - please use `from langchain.chains import RetrievalQA`)warningswarn)r9rqr+r+r,raise_deprecation9szVectorDBQA.raise_deprecationcCs,d|vr(|d}|dvr(td|d|S)zValidate search type.rl)rkmmrsearch_type of not allowed.) 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