a ag$@sdZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZmZddlmZdd lmZmZmZdd lmZdd lmZd d ddZe ddddGdddeZdS)zLCombine documents by doing a first pass and then refining on more documents.) annotations)AnyDictListTuple) deprecated) Callbacks)Document)BasePromptTemplateformat_documentPromptTemplate) ConfigDictFieldmodel_validator)BaseCombineDocumentsChain)LLMChainr returncCstdgddS)N page_contentz{page_content})input_variablestemplater rrw/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/combine_documents/refine.py_get_default_document_promptsrz0.3.1z1.0zThis class is deprecated. Please see the migration guide here for a recommended replacement: https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/)ZsinceZremovalmessagecs&eZdZUdZded<ded<ded<ded<eedZd ed <d Zd ed <e ddfdd Z e dddZ e ddedddddZe ddedddddZd4dd dd!d"d#d$Zd5dd dd!d"d%d&Zddd!d'd(d)Zd*dd+d,d-d.Zddd+d/d0d1Ze ddd2d3ZZS)6RefineDocumentsChaina& 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_stepsz List[str]rcstj}|jr|dg}|S)z2Expect input key. :meta private: intermediate_steps)super output_keysr%)selfZ _output_keys __class__rrr(ds z RefineDocumentsChain.output_keysTZforbid)Zarbitrary_types_allowedextrabefore)moderr)valuesrcCsd|vr|d|d<|d=|S)zFor backwards compatibility.Zreturn_refine_stepsr%r)clsr/rrrget_return_intermediate_stepsts z2RefineDocumentsChain.get_return_intermediate_stepscCspd|vrtd|djj}d|vrHt|dkr>|d|d<qltdn$|d|vrltd|dd||S) z4Get default document variable name, if not provided.rz"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)r0r/Zllm_chain_variablesrrr"get_default_document_variable_name}s     z7RefineDocumentsChain.get_default_document_variable_nameNzList[Document]rzTuple[str, dict])docs callbackskwargsrc Ks|j|fi|}|jjfd|i|}|g}|ddD]<}|||}i||}|jjfd|i|}||q:|||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. r8r2N)_construct_initial_inputsrZpredict_construct_refine_inputsrappend_construct_result r)r7r8r9inputsres refine_stepsdoc base_inputsrrr combine_docss   z!RefineDocumentsChain.combine_docsc s|j|fi|}|jjfd|i|IdH}|g}|ddD]B}|||}i||}|jjfd|i|IdH}||q@|||S)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. r8Nr2)r:rZapredictr;rr<r=r>rrr acombine_docss   z"RefineDocumentsChain.acombine_docs)rAr@rcCs|jrd|i}ni}||fS)Nr&)r%)r)rAr@Zextra_return_dictrrrr=s z&RefineDocumentsChain._construct_resultr zDict[str, Any])rBr@rcCs|jt||j|j|iS)N)r r r#r!)r)rBr@rrrr;sz-RefineDocumentsChain._construct_refine_inputs)r7r9rc s\d|dji|djfdd|jjD}|j|jjfi|i}i||}|S)Nrrcsi|]}||qSrr).0kZ base_inforr zBRefineDocumentsChain._construct_initial_inputs..)rupdatemetadatar#rr format)r)r7r9Z document_inforCr?rrHrr:s z.RefineDocumentsChain._construct_initial_inputscCsdS)NZrefine_documents_chainr)r)rrr _chain_typesz RefineDocumentsChain._chain_type)N)N)__name__ __module__ __qualname____doc____annotations__rrr#r%propertyr(rZ model_configr classmethodr1r6rDrEr=r;r:rN __classcell__rrr*rrs< 2   rN)rR __future__rtypingrrrrZlangchain_core._apirZlangchain_core.callbacksrZlangchain_core.documentsr Zlangchain_core.promptsr r Zlangchain_core.prompts.promptr ZpydanticrrrZ'langchain.chains.combine_documents.baserZlangchain.chains.llmrrrrrrrs"