@hJ-4SrSSKJrJr SSKJr SSKJr SSKJ r SSK J r SSK J r Jr SSKJrJr SS KJrJr SS KJrJrJr SS KJrJrJrJrJr SS KJ r S S \\S.S\ S\S\\ S\\S\!S\!S\\"\!\4\44Sjjr#\"SSSS9"SS\55r$g )z7Chain that combines documents by stuffing into context.)AnyOptional) deprecated) Callbacks)Document)LanguageModelLike)BaseOutputParserStrOutputParser)BasePromptTemplateformat_document)RunnableRunnablePassthrough) ConfigDictFieldmodel_validator)DEFAULT_DOCUMENT_PROMPTDEFAULT_DOCUMENT_SEPARATOR DOCUMENTS_KEYBaseCombineDocumentsChain_validate_prompt)LLMChainN) output_parserdocument_promptdocument_separatordocument_variable_namellmpromptrrrrreturnc^^^[UT5 U=(d [mU=(d [5nS[S[4UUU4Sjjn[ R "S0TU0D6RSS9U-U-U-RSS9$)aCreate a chain for passing a list of Documents to a model. Args: llm: Language model. prompt: Prompt template. Must contain input variable "context" (override by setting document_variable), which will be used for passing in the formatted documents. output_parser: Output parser. Defaults to StrOutputParser. document_prompt: Prompt used for formatting each document into a string. Input variables can be "page_content" or any metadata keys that are in all documents. "page_content" will automatically retrieve the `Document.page_content`, and all other inputs variables will be automatically retrieved from the `Document.metadata` dictionary. Default to a prompt that only contains `Document.page_content`. document_separator: String separator to use between formatted document strings. document_variable_name: Variable name to use for the formatted documents in the prompt. Defaults to "context". Returns: An LCEL Runnable. The input is a dictionary that must have a "context" key that maps to a List[Document], and any other input variables expected in the prompt. The Runnable return type depends on output_parser used. Example: .. code-block:: python # pip install -U langchain langchain-community from langchain_community.chat_models import ChatOpenAI from langchain_core.documents import Document from langchain_core.prompts import ChatPromptTemplate from langchain.chains.combine_documents import create_stuff_documents_chain prompt = ChatPromptTemplate.from_messages( [("system", "What are everyone's favorite colors:\n\n{context}")] ) llm = ChatOpenAI(model="gpt-3.5-turbo") chain = create_stuff_documents_chain(llm, prompt) docs = [ Document(page_content="Jesse loves red but not yellow"), Document(page_content = "Jamal loves green but not as much as he loves orange") ] chain.invoke({"context": docs}) inputsrc@>TRU4SjUT55$)Nc3:># UHn[UT5v M g7fN)r ).0doc_document_prompts `/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/chains/combine_documents/stuff.py Dcreate_stuff_documents_chain..format_docs..Ts#' 5 C!1 2 25s)join)r r&rrs r' format_docs1create_stuff_documents_chain..format_docsSs)!&&' 45'   format_inputs)run_namestuff_documents_chain)rrr dictstrrassign with_config) rrrrrr_output_parserr+r&s `` @r'create_stuff_documents_chainr7snV34&A*A"7o&7N D S   ""K&StuffDocumentsChain.s 7r-)default_factoryrrz rTforbid)arbitrary_types_allowedextrabefore)modevaluesrcUSRRnSU;a&[U5S:Xa USUS'U$Sn[U5eUSU;aSUSSU3n[U5eU$)zGet default document variable name, if not provided. If only one variable is present in the llm_chain.prompt, we can infer that the formatted documents should be passed in with this variable name. r>rrzQdocument_variable_name must be provided if there are multiple llm_chain_variableszdocument_variable_name z- was not found in llm_chain input_variables: )rinput_variableslen ValueError)clsrHllm_chain_variablesmsgs r'"get_default_document_variable_name6StuffDocumentsChain.get_default_document_variable_names%[188HH #6 1&'1,3Fq3I/0 3!o% , -5H H)&1I*J)KL;;N:OQ S/ ! r-c>URRVs/sHoUR:wdMUPM nn[TU]U-$s snfr#)r> input_keysrsuper)selfk extra_keys __class__s r'rTStuffDocumentsChain.input_keyssN~~00 0!9T9T4TA0  w!J.. s AAdocskwargsc BUVs/sHn[X0R5PM nnUR5VVs0sH,upVXPRRR ;dM*XV_M. nnnUR RU5XpR'U$s snfs snnf)aConstruct inputs from kwargs and docs. Format and then join all the documents together into one input with name `self.document_variable_name`. Also pluck any additional variables from **kwargs. Args: docs: List of documents to format and then join into single input **kwargs: additional inputs to chain, will pluck any other required arguments from here. Returns: dictionary of inputs to LLMChain ) r ritemsr>rrKrr*r)rVr[r\r% doc_stringsrWvr s r' _get_inputsStuffDocumentsChain._get_inputss NRRTcs,@,@AT R  &NN))999 AD&  /3.E.E.J.J;.W**+ S sB(B$Bc UR"U40UD6nURRR"S0UD6nURR U5$)aReturn the prompt length given the documents passed in. This can be used by a caller to determine whether passing in a list of documents would exceed a certain prompt length. This useful when trying to ensure that the size of a prompt remains below a certain context limit. Args: docs: List[Document], a list of documents to use to calculate the total prompt length. Returns: Returns None if the method does not depend on the prompt length, otherwise the length of the prompt in tokens. r1)rar>rformat_get_num_tokens)rVr[r\r rs r' prompt_length!StuffDocumentsChain.prompt_lengthsK !!$1&1&&--77~~--f55r- callbacksc hUR"U40UD6nURR"SSU0UD604$)aStuff all documents into one prompt and pass to LLM. Args: docs: List of documents to join together into one variable callbacks: Optional callbacks to pass along **kwargs: additional parameters to use to get inputs to LLMChain. Returns: The first element returned is the single string output. The second element returned is a dictionary of other keys to return. rhr1)rar>predictrVr[rhr\r s r' combine_docs StuffDocumentsChain.combine_docss;"!!$1&1~~%%D DVDbHHr-c# UR"U40UD6nURR"SSU0UD6IShvN 04$N7f)aAsync stuff all documents into one prompt and pass to LLM. Args: docs: List of documents to join together into one variable callbacks: Optional callbacks to pass along **kwargs: additional parameters to use to get inputs to LLMChain. Returns: The first element returned is the single string output. The second element returned is a dictionary of other keys to return. rhNr1)rar>apredictrks r' acombine_docs!StuffDocumentsChain.acombine_docs sD"!!$1&1^^,,KyKFKKROOKs5A>Acg)Nr0r1)rVs r' _chain_typeStuffDocumentsChain._chain_types&r-r1r#)"__name__ __module__ __qualname____firstlineno____doc__r__annotations__rrr r3rr model_configr classmethodr2rrQpropertylistrTrrarintrfrtuplerlrprs__static_attributes__ __classcell__)rYs@r'r<r<cs#J$*/7+O'SI$$? $L (#$2/DI// X#$46$x.6C6HSM6. $I8nII I sDy  I0 $P8nPP P sDy  P*'S''r-r<)%rytypingrrlangchain_core._apirlangchain_core.callbacksrlangchain_core.documentsrlangchain_core.language_modelsrlangchain_core.output_parsersr r langchain_core.promptsr r langchain_core.runnablesr rpydanticrrr'langchain.chains.combine_documents.baserrrrrlangchain.chains.llmrr3r2r7r<r1r-r'rs= *.-<KFB77*15488"/H4 H4 H4,- H4 01 H4  H4 H4d38nc!"H4V   X t'3t't'r-