gh-bdZddlmZmZddlmZddlmZddlm Z ddl m Z ddl m Z mZddlmZmZdd lmZmZdd lmZmZmZdd lmZmZmZmZmZdd lm Z d d eedde dedee deede!de!dee"e!efeffdZ#edddGddeZ$d S)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_namellmpromptrrrrreturnct||pt|p t}dtdtffd }t jdi|id|z|z|zdS)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}) inputsrcRfd|DS)Nc38K|]}t|VdSN)r ).0doc_document_prompts e/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/langchain/chains/combine_documents/stuff.py zDcreate_stuff_documents_chain..format_docs..TsB' '  C!1 2 2' ' ' ' ' ' )join)r r&rrs r' format_docsz1create_stuff_documents_chain..format_docsSsG!&&' ' ' ' 45' ' '    r) format_inputs)run_namestuff_documents_chain)rrr dictstrrassign with_config) rrrrrr_output_parserr+r&s `` @r'create_stuff_documents_chainr5snV3444&A*A"7o&7&7N D S         "KK&zStuffDocumentsChain.s 7r))default_factoryrrz rTforbid)arbitrary_types_allowedextrabefore)modevaluesrc|djj}d|vr.t|dkr |d|d<n4td|d|vrtd|dd||S)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)clsrDllm_chain_variabless r'"get_default_document_variable_namez6StuffDocumentsChain.get_default_document_variable_names%[18H #6 1 1&''1,,3Fq3I/00 3 ./7JJJ Uf5M.NUU?RUU r)cbfdjjD}tj|zS)Nc*g|]}|jk |Sr/)r)r$kselfs r' z2StuffDocumentsChain.input_keys..s-   A9T4T4TA4T4T4Tr))r; input_keyssuper)rP extra_keys __class__s` r'rRzStuffDocumentsChain.input_keyssE    ~0   ww!J..r)docskwargsc fd|D}fd|D}j||j<|S)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 c:g|]}t|jSr/)r r)r$r%rPs r'rQz3StuffDocumentsChain._get_inputs..s&RRRcsD,@AARRRr)cBi|]\}}|jjjv||Sr/)r;rrG)r$rOvrPs r' z3StuffDocumentsChain._get_inputs..s<   1DN)999 q999r))itemsrr*r)rPrVrW doc_stringsr s` r' _get_inputszStuffDocumentsChain._get_inputsst SRRRTRRR          /3.E.J.J;.W.Wt*+ r)c |j|fi|}|jjjdi|}|j|S)aVReturn 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. r/)r_r;rformat_get_num_tokens)rPrVrWr rs r' prompt_lengthz!StuffDocumentsChain.prompt_lengthsQ "!$11&11-&-7777~--f555r)N callbacksc J|j|fi|}|jjdd|i|ifS)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. rdr/)r_r;predictrPrVrdrWr s r' combine_docsz StuffDocumentsChain.combine_docssC"!$11&11%t~%DD DVDDbHHr)cZK|j|fi|}|jjdd|i|d{VifS)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. rdNr/)r_r;apredictrgs r' acombine_docsz!StuffDocumentsChain.acombine_docssY"!$11&11,T^,KKyKFKKKKKKKKROOr)cdS)Nr.r/)rPs r' _chain_typezStuffDocumentsChain._chain_types&&r)r#) __name__ __module__ __qualname____doc__r__annotations__rrr r1rr model_configr classmethodr0rrLpropertylistrRrr_rintrcrtuplerhrkrm __classcell__)rUs@r'r:r:cs_##J$*/%77+++O'SI$$$$?: $L _(###[$#0/DI/////X/ X#$46$x.6C6HSM6666*<@IINI/8IKNI sDy IIII&<@PPNP/8PKNP sDy PPPP$'S'''X'''''r)r:)%rqtypingrrlangchain_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.llmrr1r0r5r:r/r)r'rs0== ******......------<<<<<<KKKKKKKKFFFFFFFFBBBBBBBB7777777777*)))))15488"/H4H4H4 H4 H4,- H4 01 H4  H4 H4d38nc!"H4H4H4H4V    X m'm'm'm'm'3m'm'm'm'm'r)