3fiS.dZddlmZddlZddlmZddlmZddlm Z ddl m Z m Z m Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZmZmZddlmZddlmZddl m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)e dddGddeZ*e dddGdde*Z+e dddGdde*Z,y) 7Chain for question-answering against a vector database.) annotationsN)abstractmethod)Any) deprecated)AsyncCallbackManagerForChainRunCallbackManagerForChainRun Callbacks)Document)BaseLanguageModel)PromptTemplate) BaseRetriever) VectorStore) ConfigDictFieldmodel_validator)override)Chain)BaseCombineDocumentsChain)StuffDocumentsChain)LLMChain load_qa_chain)PROMPT_SELECTORz0.2.13z1.0zThis class is deprecated. Use the `create_retrieval_chain` constructor instead. See migration guide here: https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/)sinceremovalmessagecFeZdZUdZded< dZded<dZded<d Zd ed < ed d d Z e ddZ e ddZ e d ddZe d ddZe ddZ d ddZe d dZ d d!dZy)"BaseRetrievalQAz)Base class for question-answering chains.rcombine_documents_chainquerystr input_keyresult output_keyFboolreturn_source_documentsTforbid)populate_by_namearbitrary_types_allowedextrac|jgS)z Input keys.)r#selfs h/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_classic/chains/retrieval_qa/base.py input_keyszBaseRetrievalQA.input_keys8scD|jg}|jrg|d}|S)z Output keys.source_documents)r%r')r. _output_keyss r/ output_keyszBaseRetrievalQA.output_keys=s/(  ' '>\>+=>Lr1Nc |xstj|}td|||d|xsi}tdgd}t |d||} |d| |d|S) zInitialize from LLM.)llmprompt callbacks page_contentzContext: {page_content})input_variablestemplatecontext) llm_chaindocument_variable_namedocument_promptr9)r r9)r get_promptrr r) clsr7r8r9llm_chain_kwargskwargs_promptr>r@r s r/from_llmzBaseRetrievalQA.from_llmEs;O66s;   %2  )+,/ #6#,+ #  $;   r1c >|xsi}t|fd|i|}|dd|i|S)zLoad chain from chain type. chain_typer rAr)rCr7rIchain_type_kwargsrE_chain_type_kwargsr s r/from_chain_typezBaseRetrievalQA.from_chain_typegsE/4""/ # !# !#  M+BMfMMr1cyz,Get documents to do question answering over.NrAr.question run_managers r/ _get_docszBaseRetrievalQA._get_docsxsr1c|xstj}||j}dtj|j j v}|r|j ||}n|j |}|jj|||j}|jr|j|d|iS|j|iS)fRun get_relevant_text and llm on input query. If chain has 'return_source_documents' as 'True', returns the retrieved documents as well under the key 'source_documents'. Example: ```python res = indexqa({"query": "This is my query"}) answer, docs = res["result"], res["source_documents"] ``` rQrQinput_documentsrPr9r3) r get_noop_managerr#inspect signaturerR parametersr run get_childr'r%r.inputsrQ _run_managerrPaccepts_run_managerdocsanswers r/_callzBaseRetrievalQA._calls #S&@&Q&Q&S $..) W..t~~>II I  >>( >ED>>(+D--11 ",,.2   ' 'OOV-?F F((r1c KywrNrArOs r/ _aget_docszBaseRetrievalQA._aget_docssscK|xstj}||j}dtj|j j v}|r|j ||d{}n|j |d{}|jj|||jd{}|jr|j|d|iS|j|iS7|7d72w)rTrQrUNrVr3) rrXr#rYrZrfr[r arunr]r'r%r^s r/_acallzBaseRetrievalQA._acalls #X&E&V&V&X $..) W..t?JJ J  |LLD22D3388 ",,.9    ' 'OOV-?F F((M2 s6A+C0-C*.C0C,3C0;C.!)!)7!)  !)F;;5 ;  ;;?C!)!)<!)  !)r1rz0.1.17cheZdZUdZedZded< d dZ d dZe d dZ y ) RetrievalQAa@Chain for question-answering against an index. This class is deprecated. See below for an example implementation using `create_retrieval_chain`: ```python from langchain_classic.chains import create_retrieval_chain from langchain_classic.chains.combine_documents import ( create_stuff_documents_chain, ) from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI retriever = ... # Your retriever model = 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(model, prompt) chain = create_retrieval_chain(retriever, question_answer_chain) chain.invoke({"input": query}) ``` Example: ```python from langchain_openai import OpenAI from langchain_classic.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 retrieverc\|jj|d|jiS) Get docs.r9config)r|invoker]rOs r/rRzRetrievalQA._get_docs s4~~$$ !6!6!89%  r1cxK|jj|d|jid{S7w)r~r9rN)r|ainvoker]rOs r/rfzRetrievalQA._aget_docssB^^++ !6!6!89,    s 1:8:cy)Return the chain type. retrieval_qarAr-s r/ _chain_typezRetrievalQA._chain_type$r1Nrmrprjr") rqrrrsrtrr|rurRrfrwrrAr1r/rzrzso-^ %T2I}2    0        5    r1rzceZdZUdZeddZded< dZded< d Zd ed < ee Z d ed< e de ddZ e ddZ ddZeddZy) VectorDBQArT vectorstore)r{aliasrintk similarityr" search_type)default_factoryro search_kwargsbefore)modecBd|vr|d}|dvrd|d}t||S)zValidate search type.r)rmmrsearch_type of not allowed.) ValueError)rCvaluesrmsgs r/validate_search_typezVectorDBQA.validate_search_type?s< F " /K"77' }MB o% r1cH|jdk(r5|jj|fd|ji|j}|S|jdk(r5|jj |fd|ji|j}|Sd|jd}t |)r~rrrrr)rrsimilarity_searchrrmax_marginal_relevance_searchr)r.rPrQrbrs r/rRzVectorDBQA._get_docsJs   | +54##55&&$$D    &A4##AA&&$$D $D$4$4#5]CCS/ !r1c$Kd}t|w)r~z!VectorDBQA does not support async)NotImplementedError)r.rPrQrs r/rfzVectorDBQA._aget_docscs2!#&&scy)r vector_db_qarAr-s r/rzVectorDBQA._chain_typemrr1N)rdictrjrrmrpr)rqrrrsrtrrrurrrrrrxrrrRrfrwrrAr1r/rr*sB$TGKG( AsJ+#K#E$)$$?M>?(#$0   0''5 '  'r1r)-rt __future__rrYabcrtypingrlangchain_core._apirlangchain_core.callbacksrr r langchain_core.documentsr langchain_core.language_modelsr langchain_core.promptsr langchain_core.retrieversrlangchain_core.vectorstoresrpydanticrrrtyping_extensionsrlangchain_classic.chains.baser/langchain_classic.chains.combine_documents.baser0langchain_classic.chains.combine_documents.stuffrlangchain_classic.chains.llmr+langchain_classic.chains.question_answeringr8langchain_classic.chains.question_answering.stuff_promptrrrzrrAr1r/rs="* .<13377&/UP1ET   T f)ef)f)R   T M/MM`   T ===r1