9|h$dZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZmZddlmZdd lmZmZmZdd lmZdd lmZdd Ze d ddGddeZy)zLCombine documents by doing a first pass and then refining on more documents.) annotations)AnyDictListTuple) deprecated) Callbacks)Document)BasePromptTemplateformat_documentPromptTemplate) ConfigDictFieldmodel_validator)BaseCombineDocumentsChain)LLMChainctdgdS)N page_contentz{page_content})input_variablestemplater h/var/www/html/test/engine/venv/lib/python3.12/site-packages/langchain/chains/combine_documents/refine.py_get_default_document_promptrs >*:EU VVrz0.3.1z1.0zThis class is deprecated. Please see the migration guide here for a recommended replacement: https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/)sinceremovalmessagec`eZdZUdZded< ded< ded< ded< eeZd ed < d Zd ed < e dfd Z e ddZ e deddZe deddZ d ddZ d ddZd dZd!dZ d"dZe d#dZxZS)$RefineDocumentsChaina& Combine documents by doing a first pass and then refining on more documents. This algorithm first calls `initial_llm_chain` on the first document, passing that first document in with the variable name `document_variable_name`, and produces a new variable with the variable name `initial_response_name`. Then, it loops over every remaining document. This is called the "refine" step. It calls `refine_llm_chain`, passing in that document with the variable name `document_variable_name` as well as the previous response with the variable name `initial_response_name`. Example: .. code-block:: python from langchain.chains import RefineDocumentsChain, LLMChain from langchain_core.prompts import PromptTemplate from langchain_community.llms import OpenAI # This controls how each document will be formatted. Specifically, # it will be passed to `format_document` - see that function for more # details. document_prompt = PromptTemplate( input_variables=["page_content"], template="{page_content}" ) document_variable_name = "context" llm = OpenAI() # The prompt here should take as an input variable the # `document_variable_name` prompt = PromptTemplate.from_template( "Summarize this content: {context}" ) initial_llm_chain = LLMChain(llm=llm, prompt=prompt) initial_response_name = "prev_response" # The prompt here should take as an input variable the # `document_variable_name` as well as `initial_response_name` prompt_refine = PromptTemplate.from_template( "Here's your first summary: {prev_response}. " "Now add to it based on the following context: {context}" ) refine_llm_chain = LLMChain(llm=llm, prompt=prompt_refine) chain = RefineDocumentsChain( initial_llm_chain=initial_llm_chain, refine_llm_chain=refine_llm_chain, document_prompt=document_prompt, document_variable_name=document_variable_name, initial_response_name=initial_response_name, ) rinitial_llm_chainrefine_llm_chainstrdocument_variable_nameinitial_response_name)default_factoryr document_promptFboolreturn_intermediate_stepsc@t|}|jr|dgz}|S)z2Expect input key. :meta private: intermediate_steps)super output_keysr))self _output_keys __class__s rr-z RefineDocumentsChain.output_keysds, w*  ) )'+?*@@LrTforbid)arbitrary_types_allowedextrabefore)modec$d|vr |d|d<|d=|S)zFor backwards compatibility.return_refine_stepsr)r)clsvaluess rget_return_intermediate_stepsz2RefineDocumentsChain.get_return_intermediate_stepsts+ !F *289N2OF. /,- rcd|vr td|djj}d|vr#t|dk(r |d|d<|Std|d|vrtd|dd||S) z4Get default document variable name, if not provided.r!z"initial_llm_chain must be providedr$rzWdocument_variable_name must be provided if there are multiple llm_chain input_variableszdocument_variable_name z- was not found in llm_chain input_variables: ) ValueErrorpromptrlen)r8r9llm_chain_variabless r"get_default_document_variable_namez7RefineDocumentsChain.get_default_document_variable_name}s f ,AB B$%89@@PP #6 1&'1,3Fq3I/0 !9 ./7JJ -f5M.N-OP??R>SU rc 0|j|fi|}|jjdd|i|}|g}|ddD]I}|j||}i||}|jjdd|i|}|j |K|j ||S)aCombine by mapping first chain over all, then stuffing into final chain. Args: docs: List of documents to combine callbacks: Callbacks to be passed through **kwargs: additional parameters to be passed to LLM calls (like other input variables besides the documents) Returns: The first element returned is the single string output. The second element returned is a dictionary of other keys to return. callbacksr<Nr)_construct_initial_inputsr!predict_construct_refine_inputsr"append_construct_result r.docsrCkwargsinputsres refine_stepsdoc base_inputss r combine_docsz!RefineDocumentsChain.combine_docss0//??,d$$,,KyKFKu 8 %C77SAK. .v.F/$''//N)NvNC    $  % %%lC88rc`K|j|fi|}|jjdd|i|d{}|g}|ddD]Q}|j||}i||}|jjdd|i|d{}|j |S|j ||S7r7+w)aAsync combine by mapping a first chain over all, then stuffing into a final chain. Args: docs: List of documents to combine callbacks: Callbacks to be passed through **kwargs: additional parameters to be passed to LLM calls (like other input variables besides the documents) Returns: The first element returned is the single string output. The second element returned is a dictionary of other keys to return. rCNr<r)rDr!apredictrFr"rGrHrIs r acombine_docsz"RefineDocumentsChain.acombine_docss 0//??3D**33RiR6RRu 8 %C77SAK. .v.F6--66UUfUUC    $  % %%lC88S Vs"5B.B*AB.B,*B.,B.c6|jrd|i}||fSi}||fS)Nr+)r))r.rNrMextra_return_dicts rrHz&RefineDocumentsChain._construct_results8  ) )!5| D %%%!# %%%rc^|jt||j|j|iSN)r$r r'r%)r.rOrMs rrFz-RefineDocumentsChain._construct_refine_inputss/  ' 'd>R>R)S  & &  rc d|dji}|j|dj|jjDcic]}||| }}|j |jj di|i}i||}|Scc}w)Nrrr)rupdatemetadatar'rr$format)r.rJrK base_infok document_inforPrLs rrDz.RefineDocumentsChain._construct_initial_inputss$T!W%9%9: a))*262F2F2V2VWQIaLW W  ' ')D)=)=)D)D)U})U +K*6* Xs Bcy)Nrefine_documents_chainr)r.s r _chain_typez RefineDocumentsChain._chain_types'r)return List[str])r9rrcrrX)rJList[Document]rCr rKrrcTuple[str, dict])rNrdrMr#rcrf)rOr rMr#rcDict[str, Any])rJrerKrrcrg)rcr#)__name__ __module__ __qualname____doc____annotations__rrr'r)propertyr-r model_configr classmethodr:rArQrTrHrFrDrb __classcell__)r0s@rr r sR0d /)QL*/4+O'S&+t+?  $L (#$(#$.<@9"9/89KN9 94<@9"9/89KN9 94& " .1   ((rr N)rcr)rk __future__rtypingrrrrlangchain_core._apirlangchain_core.callbacksr langchain_core.documentsr langchain_core.promptsr r langchain_core.prompts.promptrpydanticrrr'langchain.chains.combine_documents.baserlangchain.chains.llmrrr rrrr{sfR"))*.-F877*W   Y B(4B(B(r