蘺hEdZddlmZddlZddlZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZddlmZddlmZdd lmZdd lmZdd lmZmZmZmZdd lm Z dd l!m"Z"ddl#m$Z$ddl%m&Z&m'Z'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4eee5e5fefZ6dddZ7d&dZ8GddeZ9Gd d!e*Z:Gd"d#e:Z;Gd$d%e:Z>L  ) A'+?*@@Lr=r@inputsDict[str, Any] run_managerrList[Document]cdS Get docs.NrIr`r@rmros r; _get_docsz*BaseConversationalRetrievalChain._get_docs|sr=$Optional[CallbackManagerForChainRun]c|ptj}|d}|jpt}||d}|r2|}|j|||}n|}dtj|j j v} | r| |||} n| ||} i} |j #t| dkr|j | |j <nV|} |jr|| d<|| d<|jjd | |d| } | | |j <|jr| | d<|jr|| d <| S Nr@r')r@r' callbacksro)ror)input_documentsryrirjrI)rget_noop_managerrTr< get_childrMruninspect signatureru parametersrUlenrOcopyrQrLrRrSr`rmro _run_managerr@rTchat_history_strry new_questionaccepts_run_managerdocsoutput new_inputsrNs r;_callz&BaseConversationalRetrievalChain._calls #S&@&Q&S&S *%0E4E++F>,BCC  $$..00I266!0@I7LL$L W.t~>>I I   8>>,L>QQDD>>,77D!#  ) 5#d))q..&*&DF4? # #J% 6)5 :&)9J~ &0T,0 $ 0F0F0H0HLVF'-F4? #  ' .)-F% &  ) 8+7F' ( r=rc KdSrrrIrts r; _aget_docsz+BaseConversationalRetrievalChain._aget_docss r=)Optional[AsyncCallbackManagerForChainRun]cK|ptj}|d}|jpt}||d}|r8|}|j|||d{V}n|}dtj|j j v} | r| |||d{V} n| ||d{V} i} |j #t| dkr|j | |j <n\|} |jr|| d<|| d<|jjd | |d| d{V} | | |j <|jr| | d<|jr|| d <| Srx)rr{rTr<r|rMarunr~rrrrUrrOrrQrLrRrSrs r;_acallz'BaseConversationalRetrievalChain._acalls #X&E&V&X&X *%0E4E++F>,BCC  $$..00I!%!8!=!=!0@I">""LL$L W.t??J J   ?v<XXXXXXXXDDv>>>>>>>>D!#  ) 5#d))q..&*&DF4? # #J% 6)5 :&)9J~ &7427 $ 0F0F0H0HLVF'-F4? #  ' .)-F% &  ) 8+7F' ( r= file_pathUnion[Path, str]Nonecv|jrtdt|dS)Nz7Chain not saveable when `get_chat_history` is not None.)rTr7supersave)r`r __class__s r;rz%BaseConversationalRetrievalChain.saves:   XVWW W  Yr=)r)r]rf)rbrcr)rdr@r*rmrnrorr)rp)rmrnrorvr)rnr@r*rmrnrorr)rp)rmrnrorr)rn)rrr)r)rCrDrErFrGrOrQrRrSrTr\propertyrargrlrrurrrr __classcell__)rs@r;rKrKBs++1111<    6JE""""":%*))))L&+++++DHLLLLL0,,,,$........,,,X, 26    X ^=A)))))V^BF)))))V          r=rKcneZdZUdZded< dZded< d'd Zd(dZd)dZe e dddddfd*d&Z dS)+ConversationalRetrievalChainaWChain for having a conversation based on retrieved documents. This chain takes in chat history (a list of messages) and new questions, and then returns an answer to that question. The algorithm for this chain consists of three parts: 1. Use the chat history and the new question to create a "standalone question". This is done so that this question can be passed into the retrieval step to fetch relevant documents. If only the new question was passed in, then relevant context may be lacking. If the whole conversation was passed into retrieval, there may be unnecessary information there that would distract from retrieval. 2. This new question is passed to the retriever and relevant documents are returned. 3. The retrieved documents are passed to an LLM along with either the new question (default behavior) or the original question and chat history to generate a final response. Example: .. code-block:: python from langchain.chains import ( StuffDocumentsChain, LLMChain, ConversationalRetrievalChain ) from langchain_core.prompts import PromptTemplate from langchain_community.llms import OpenAI combine_docs_chain = StuffDocumentsChain(...) vectorstore = ... retriever = vectorstore.as_retriever() # This controls how the standalone question is generated. # Should take `chat_history` and `question` as input variables. template = ( "Combine the chat history and follow up question into " "a standalone question. Chat History: {chat_history}" "Follow up question: {question}" ) prompt = PromptTemplate.from_template(template) llm = OpenAI() question_generator_chain = LLMChain(llm=llm, prompt=prompt) chain = ConversationalRetrievalChain( combine_docs_chain=combine_docs_chain, retriever=retriever, question_generator=question_generator_chain, ) r retrieverNz Optional[int]max_tokens_limitrrpr)ct|}jretjtrKfd|D}t |d|}|jkr|dz}|||z}|jk|d|S)NcXg|]&}jj|j'SrI)rL llm_chain_get_num_tokens page_content).0docr`s r; zKConversationalRetrievalChain._reduce_tokens_below_limit..*s@'1AA#BRSSr=r.)rrr0rLrsum)r`rnum_docstokens token_counts` r;_reduce_tokens_below_limitz7ConversationalRetrievalChain._reduce_tokens_below_limit$st99  0Z  #%8& & 0FfYhY/00K 555A vh//  555IXIr=r@r*rmrnrorc|j||}||S)rsry)rget_relevant_documentsr|rr`r@rmrors r;ruz&ConversationalRetrievalChain._get_docs5sG~44  5 5 7 75  ..t444r=rcK|j||d{V}||S)rsrN)raget_relevant_documentsr|rrs r;rz'ConversationalRetrievalChain._aget_docsBsi^;;  5 5 7 7<        ..t444r=stuffFllmrcondense_question_promptr chain_typeverboserPcondense_question_llmOptional[BaseLanguageModel]combine_docs_chain_kwargsOptional[Dict]ryrkwargsrrKc  t|pi}t|f|||d|} |p|} t| |||} |d|| | |d| S)a Convenience method to load chain from LLM and retriever. This provides some logic to create the `question_generator` chain as well as the combine_docs_chain. Args: llm: The default language model to use at every part of this chain (eg in both the question generation and the answering) retriever: The retriever to use to fetch relevant documents from. condense_question_prompt: The prompt to use to condense the chat history and new question into a standalone question. chain_type: The chain type to use to create the combine_docs_chain, will be sent to `load_qa_chain`. verbose: Verbosity flag for logging to stdout. condense_question_llm: The language model to use for condensing the chat history and new question into a standalone question. If none is provided, will default to `llm`. combine_docs_chain_kwargs: Parameters to pass as kwargs to `load_qa_chain` when constructing the combine_docs_chain. callbacks: Callbacks to pass to all subchains. **kwargs: Additional parameters to pass when initializing ConversationalRetrievalChain )rrry)rpromptrry)rrLrMryrIr"r!) clsrrrrrrrryr doc_chain_llmcondense_question_chains r;from_llmz%ConversationalRetrievalChain.from_llmOsH%>$C!!  !   (   %+"*+ # # #  s (6       r=)rrpr)rprr)rrrrrrrr*rrPrrrrryrrrr)rK) rCrDrErFrGrrrur classmethodr rrIr=r;rrs//b.&*****U" 5 5 5 5 5 5 5 5 8P!=A48#9 9 9 9 [9 9 9 r=rceZdZUdZedZded<dZded<eeZ d ed <e d)d Z e d*dZ d+dZd,dZeedddfd-d(ZdS).ChatVectorDBChainr vectorstore)aliasrinttop_k_docs_for_contextrAdict search_kwargsr)r*cdS)Nzchat-vector-dbrIr_s r; _chain_typezChatVectorDBChain._chain_typesr=valuesr c.tjd|S)Nzj`ChatVectorDBChain` is deprecated - please use `from langchain.chains import ConversationalRetrievalChain`)warningswarn)rrs r;raise_deprecationz#ChatVectorDBChain.raise_deprecations#  U    r=r@rmrnrorrpcx|di}i|j|}|jj|fd|ji|S)rsvectordbkwargsk)r2rrsimilarity_searchr)r`r@rmror full_kwargss r;ruzChatVectorDBChain._get_docss_ $4b99>+>~> 1t1   3 7B   r=rc$Ktd)rsz(ChatVectorDBChain does not support async)NotImplementedErrorrts r;rzChatVectorDBChain._aget_docss""LMMMr=rNrrrrrrrryrrrrKc h|pi}t|f||d|}t|||} |d||| |d|S)zLoad chain from LLM.)rry)rrry)rrLrMryrIr) rrrrrrryrrrs r;rzChatVectorDBChain.from_llms%>$C!!  !  (   #+4 # # # s #(6       r=)r)r*)rr r)r rr)rrrrrrrr*rrryrrrr)rK)rCrDrErFrrrGrrrrrrrrurrr rrIr=r;rrs44$u=999K9999"#####%555M5555    X ^     NNNN 8P!48#    [   r=r)r'r(r)r*)=rF __future__rr~rabcrpathlibrtypingrrr r r r r rlangchain_core.documentsrlangchain_core.language_modelsrlangchain_core.messagesrlangchain_core.promptsrlangchain_core.pydantic_v1rrrrlangchain_core.retrieversrlangchain_core.runnablesrlangchain_core.vectorstoresrlangchain.callbacks.managerrrrlangchain.chains.baser'langchain.chains.combine_documents.baser(langchain.chains.combine_documents.stuffr1langchain.chains.conversational_retrieval.promptsr langchain.chains.llmr!#langchain.chains.question_answeringr"r*CHAT_TURN_TYPEr1r<r?rKrrrIr=r;rs00""""""JJJJJJJJJJJJJJJJJJJJ------<<<<<<//////555555NNNNNNNNNNNN333333333333333333 ('''''MMMMMMHHHHHHVVVVVV))))))======uS#X 34 } 5 5 $11111 111g g g g g ug g g T] ] ] ] ] #C] ] ] @G G G G G 8G G G G G r=