Ah.tSrSSKJr SSKrSSKJr SSKJrJr SSK J r SSK J r J r Jr SSKJr SS KJr SS KJr SS KJr SS KJr SS KJrJrJr SSKJr SSKJ r SSK!J"r" SSK#J$r$ SSK%J&r& SSK'J(r( \ "SSSS9"SS\55r)\ "SSSS9"SS\)55r*\ "SSSS9"SS\)55r+g)7Chain for question-answering against a vector database.) annotationsN)abstractmethod)AnyOptional) 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. See migration guide here: https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/)sinceremovalmessagec`\rSrSr%SrS\S'SrS\S'SrS\S 'S rS \S '\ "S S SS9r \ SSj5r \ SSj5r \SSSjj5r\SSSjj5r\SSj5rS S!Sjjr\S"Sj5rS S#SjjrSrg)$BaseRetrievalQAz)Base class for question-answering chains.rcombine_documents_chainquerystr input_keyresult output_keyFboolreturn_source_documentsTforbid)populate_by_namearbitrary_types_allowedextracUR/$)zInput keys. :meta private: )r$selfs Z/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/chains/retrieval_qa/base.py input_keysBaseRetrievalQA.input_keys7s cNUR/nUR(a/UQSPnU$)zOutput keys. :meta private: source_documents)r&r()r/ _output_keyss r0 output_keysBaseRetrievalQA.output_keys?s/ (  ' '>\>+=>Lr3Nc U=(d [R"U5n[SUUUS.U=(d 0D6n[S/SS9n[ USUUS9n U"SU US.UD6$) zInitialize from LLM.)llmprompt callbacks page_contentzContext: {page_content})input_variablestemplatecontext) llm_chaindocument_variable_namedocument_promptr<)r!r<)r get_promptrrr) clsr:r;r<llm_chain_kwargskwargs_promptrArCr!s r0from_llmBaseRetrievalQA.from_llmJs;O66s;   %2  )+,/ #6#,+ #  $;   r3c HU=(d 0n[U4SU0UD6nU"SSU0UD6$)zLoad chain from chain type. chain_typer!rDr)rFr:rMchain_type_kwargsrH_chain_type_kwargsr!s r0from_chain_typeBaseRetrievalQA.from_chain_typelsE/4""/ # !# !#  M+BMfMMr3cgz,Get documents to do question answering over.NrDr/question run_managers r0 _get_docsBaseRetrievalQA._get_docs}sr3cU=(d [R"5nXRnS[R"UR 5R ;nU(aUR XCS9nOUR U5nURRUUUR5S9nUR(aURUSU0$URU0$)0Run 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: .. code-block:: python res = indexqa({'query': 'This is my query'}) answer, docs = res['result'], res['source_documents'] rVrVinput_documentsrUr<r5) r get_noop_managerr$inspect signaturerW parametersr!run get_childr(r&r/inputsrV _run_managerrUaccepts_run_managerdocsanswers r0_callBaseRetrievalQA._calls #S&@&Q&Q&S ..) W..t~~>II I  >>(>ED>>(+D--11 ",,.2   ' 'OOV-?F F((r3c # g7frSrDrTs r0 _aget_docsBaseRetrievalQA._aget_docsssc# U=(d [R"5nXRnS[R"UR 5R ;nU(aUR XCS9IShvN nOUR U5IShvN nURRUUUR5S9IShvN nUR(aURUSU0$URU0$N~NfN77f)rZrVr[Nr\r5) r r^r$r_r`rmrar!arunrcr(r&rds r0_acallBaseRetrievalQA._acalls #X&E&V&V&X ..) W..t?JJ J  LLD22D3388 ",,.9    ' 'OOV-?F F((M2 s6A3C:5C46C:C60C:C84C:6C:8C:rD)returnz list[str])NNN) r:r r;zOptional[PromptTemplate]r<r rGOptional[dict]rHrrsr)stuffN) r:r rMr#rNrtrHrrsrrUr#rVr rslist[Document])N)redict[str, Any]rVz$Optional[CallbackManagerForChainRun]rsrxrUr#rVr rsrw)rerxrVz)Optional[AsyncCallbackManagerForChainRun]rsrx)__name__ __module__ __qualname____firstlineno____doc____annotations__r$r&r(r model_configpropertyr1r7 classmethodrJrPrrWrjrmrq__static_attributes__rDr3r0rrs4660IsJ$)T)- $L   ,0#+/   )   )       B",0 N NN* N  N  NN ;;0 ;  ;;=A!)!):!)  !)F;;5 ;  ;;BF!)!)?!)  !)!)r3rz0.1.17cp\rSrSr%Sr\"SS9rS\S'S SjrS Sjr \ SS j5r S r g ) RetrievalQAaChain for question-answering against an index. This class is deprecated. See below for an example implementation using `create_retrieval_chain`: .. code-block:: python from langchain.chains import create_retrieval_chain from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI retriever = ... # Your retriever llm = ChatOpenAI() system_prompt = ( "Use the given context to answer the question. " "If you don't know the answer, say you don't know. " "Use three sentence maximum and keep the answer concise. " "Context: {context}" ) prompt = ChatPromptTemplate.from_messages( [ ("system", system_prompt), ("human", "{input}"), ] ) question_answer_chain = create_stuff_documents_chain(llm, prompt) chain = create_retrieval_chain(retriever, question_answer_chain) chain.invoke({"input": query}) Example: .. code-block:: python from langchain_community.llms import OpenAI from langchain.chains import RetrievalQA from langchain_community.vectorstores import FAISS from langchain_core.vectorstores import VectorStoreRetriever retriever = VectorStoreRetriever(vectorstore=FAISS(...)) retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever) T)excluder retrievercVURRUSUR50S9$) Get docs.r<config)rinvokercrTs r0rWRetrievalQA._get_docss4~~$$ !6!6!89%  r3cr# URRUSUR50S9IShvN $N7f)rr<rN)rainvokercrTs r0rmRetrievalQA._aget_docssA^^++ !6!6!89,    s .757cg)Return the chain type. retrieval_qarDr.s r0 _chain_typeRetrievalQA._chain_type'r3rDNrvryrsr#) rzr{r|r}r~rrrrWrmrrrrDr3r0rrso+Z %T2I}2    0        5    r3rc\rSrSr%Sr\"SSS9rS\S'SrS\S 'S r S \S '\"\ S 9r S\S'\ "SS9\ SSj55rSSjrSSjr\SSj5rSrg) VectorDBQAi-rT vectorstore)raliasrintk similarityr# search_type)default_factoryrx search_kwargsbefore)modecJSU;aUSnUS;aSUS3n[U5eU$)zValidate search type.r)rmmrsearch_type of not allowed.) ValueError)rFvaluesrmsgs r0validate_search_typeVectorDBQA.validate_search_typeBs< F " /K"77' }MB o% r3cLURS:Xa5URR"U4SUR0URD6nU$URS:Xa5URR "U4SUR0URD6nU$SURS3n[ U5e)rrrrrr)rrsimilarity_searchrrmax_marginal_relevance_searchr)r/rUrVrhrs r0rWVectorDBQA._get_docsMs   | +##55&&$$D    &##AA&&$$D $D$4$4#5]CCS/ !r3c$# Sn[U5e7f)rz!VectorDBQA does not support async)NotImplementedError)r/rUrVrs r0rmVectorDBQA._aget_docses2!#&&scg)r vector_db_qarDr.s r0rVectorDBQA._chain_typeorr3rDN)rdictrsrrvryr)rzr{r|r}r~rrrrrrrrrrrWrmrrrrDr3r0rr-sB$TGKG( AsJ+#K#E$)$$?M>?(#$0   0''5 '  'r3r),r~ __future__rr_abcrtypingrrlangchain_core._apirlangchain_core.callbacksr r r langchain_core.documentsr langchain_core.language_modelsr langchain_core.promptsrlangchain_core.retrieversrlangchain_core.vectorstoresrpydanticrrrlangchain.chains.baser'langchain.chains.combine_documents.baser(langchain.chains.combine_documents.stuffrlangchain.chains.llmr#langchain.chains.question_answeringr0langchain.chains.question_answering.stuff_promptrrrrrDr3r0rs=" * .<13377'MH)=L   T l)el)l)^   T K/KK\   T <<<r3