tj.dZddlmZddlZddlmZddlmZddlm Z ddl m Z m Z m Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZmZmZddlmZddlmZddl m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)e ddddGddeZ*e ddddGdde*Z+e ddddGdd e*Z,dS)!7Chain for question-answering against a vector database.) annotationsN)abstractmethod)Any) deprecated)AsyncCallbackManagerForChainRunCallbackManagerForChainRun Callbacks)Document)BaseLanguageModel)PromptTemplate) BaseRetriever) VectorStore) ConfigDictFieldmodel_validator)override)Chain)BaseCombineDocumentsChain)StuffDocumentsChain)LLMChain load_qa_chain)PROMPT_SELECTORz0.2.13z2.0.0zlangchain.agents.create_agentzuBuild new RAG flows with `create_agent` and a retrieval tool. See https://docs.langchain.com/oss/python/langchain/rag)sinceremoval alternativeaddendumceZdZUdZded< dZded<dZded<d Zd ed < ed d d Z e d0dZ e d0dZ e d1d2dZe d3d4d"Zed5d'Z d6d7d+Zed8d-Z d6d9d/ZdS):BaseRetrievalQAz)Base class for question-answering chains.rcombine_documents_chainquerystr input_keyresult output_keyFboolreturn_source_documentsTforbid)populate_by_namearbitrary_types_allowedextrareturn list[str]c|jgS)z Input keys.)r$selfs h/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/chains/retrieval_qa/base.py input_keyszBaseRetrievalQA.input_keys8sc0|jg}|jrg|d}|S)z Output keys.source_documents)r&r()r1 _output_keyss r2 output_keyszBaseRetrievalQA.output_keys=s/(  ' ?>\>+=>Lr4Nllmr promptPromptTemplate | None callbacksr llm_chain_kwargs dict | Nonekwargsrc |ptj|}td|||d|pi}tdgd}t |d||} |d| |d|S) zInitialize from LLM.)r9r:r< page_contentzContext: {page_content})input_variablestemplatecontext) llm_chaindocument_variable_namedocument_promptr<)r!r<)r get_promptrr r) clsr9r:r<r=r?_promptrErGr!s r2from_llmzBaseRetrievalQA.from_llmEs;O6s;;    %2   )+,/   #6#,+ # # # s $;     r4stuff chain_typechain_type_kwargsc <|pi}t|fd|i|}|dd|i|S)zLoad chain from chain type.rNr!rHr)rJr9rNrOr?_chain_type_kwargsr!s r2from_chain_typezBaseRetrievalQA.from_chain_typegsW/4""/ # # !# !# #  sMM+BMfMMMr4question run_managerr list[Document]cdSz,Get documents to do question answering over.NrHr1rSrTs r2 _get_docszBaseRetrievalQA._get_docsxsr4inputsdict[str, Any]!CallbackManagerForChainRun | Nonecx|ptj}||j}dtj|jjv}|r|||}n||}|j||| }|j r |j |d|iS|j |iS)fRun get_relevant_text and llm on input query. If chain has 'return_source_documents' as 'True', returns the retrieved documents as well under the key 'source_documents'. Example: ```python res = indexqa({"query": "This is my query"}) answer, docs = res["result"], res["source_documents"] ``` rTrTinput_documentsrSr<r6) r get_noop_managerr$inspect signaturerY parametersr!run get_childr(r&r1rZrT _run_managerrSaccepts_run_managerdocsanswers r2_callzBaseRetrievalQA._calls #S&@&Q&S&S $.) W.t~>>I I   ,>>( >EEDD>>(++D-11 ",,..2    ' GOV-?F F((r4rc KdSrWrHrXs r2 _aget_docszBaseRetrievalQA._aget_docss r4&AsyncCallbackManagerForChainRun | NonecK|ptj}||j}dtj|jjv}|r|||d{V}n||d{V}|j||| d{V}|j r |j |d|iS|j |iS)r^rTr_Nr`r6) rrbr$rcrdrorer!arunrgr(r&rhs r2_acallzBaseRetrievalQA._acalls #X&E&V&X&X $.) W.t??J J   3|LLLLLLLLDD22222222D388 ",,..9          ' GOV-?F F((r4)r-r.)NNN) r9r r:r;r<r r=r>r?rr-r )rMN) r9r rNr#rOr>r?rr-r rSr#rTr r-rU)N)rZr[rTr\r-r[rSr#rTrr-rU)rZr[rTrpr-r[)__name__ __module__ __qualname____doc____annotations__r$r&r(r model_configpropertyr3r8 classmethodrLrRrrYrmrorsrHr4r2r r s4366660IJ$)))))-: $L    X X)-#(,     [ B")- NNNN[N ;;;^;:>!)!)!)!)!)F;;;^;?C!)!)!)!)!)!)!)r4r z0.1.17c^eZdZUdZedZded<dd ZddZe ddZ dS) 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 retrieverrSr#rTr r-rUcb|j|d|iS) Get docs.r<config)rinvokergrXs r2rYzRetrievalQA._get_docs s:~$$ !6!6!8!89%   r4rcrK|j|d|id{VS)rr<rN)rainvokergrXs r2rozRetrievalQA._aget_docss\^++ !6!6!8!89,         r4cdS)Return the chain type. retrieval_qarHr0s r2 _chain_typezRetrievalQA._chain_type$ ~r4Nrtrur-r#) rvrwrxryrrrzrYror|rrHr4r2rrs--^ %uT222I2222          Xr4rceZdZUdZeddZded< dZded< d Zd ed < ee Z d ed< e de ddZ ed dZd!dZed"dZdS)# VectorDBQArT vectorstore)raliasrintk similarityr# search_type)default_factoryr[ search_kwargsbefore)modevaluesdictr-rcPd|vr!|d}|dvrd|d}t||S)zValidate search type.r)rmmrsearch_type of not allowed.) ValueError)rJrrmsgs r2validate_search_typezVectorDBQA.validate_search_type?sF F " " /K"777B BBB oo% r4rSrTr rUc|jdkr |jj|fd|ji|j}nE|jdkr |jj|fd|ji|j}nd|jd}t ||S)rrrrrr)rrsimilarity_searchrrmax_marginal_relevance_searchr)r1rSrTrkrs r2rYzVectorDBQA._get_docsJs  | + +54#5&$DD   & &A4#A&$DD DD$4CCCCS// ! r4rc(Kd}t|)rz!VectorDBQA does not support async)NotImplementedError)r1rSrTrs r2rozVectorDBQA._aget_docscs2!#&&&r4cdS)r vector_db_qarHr0s r2rzVectorDBQA._chain_typemrr4N)rrr-rrtrur)rvrwrxryrrrzrrrrrr}rrrYror|rrHr4r2rr*sBA$uTGGGKGGGG( AJJJJ+#K####E$)E$$?$?$?M????_(###[$#X0''''Xr4r)-ry __future__rrcabcrtypingrlangchain_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_promptrr rrrHr4r2rs==""""""****** .-----<<<<<<1111113333333333337777777777&&&&&&//////UUUUUUPPPPPP111111EEEEEETTTTTT   / > f)f)f)f)f)ef)f)f)R   / > MMMMM/MMM`   / > ==========r4