a ag#- @sdZddlmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZmZddlmZmZdd lmZmZdd lmZmZmZdd lmZmZmZm Z m!Z!dd l"m#Z#d d eedeeeeeee$e$eee$efefdddZ%eddddGddde Z&d S)z7Chain that combines documents by stuffing into context.)AnyDictListOptionalTuple) 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_name)llmpromptrrrrreturncsdt||pt|pt}ttdfdd }tjfi|ijdd|B|B|BjddS)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}) )inputsr csfdd|DS)Nc3s|]}t|VqdSN)r.0doc)_document_promptv/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/combine_documents/stuff.py TszDcreate_stuff_documents_chain..format_docs..)join)r!r&rrr'r( format_docsSsz1create_stuff_documents_chain..format_docsZ format_inputs)Zrun_namestuff_documents_chain)rrr dictstrrZassignZ with_config)rrrrrrZ_output_parserr,r'r+r(create_stuff_documents_chains 7  r0z0.2.13z1.0zThis class is deprecated. Use the `create_stuff_documents_chain` constructor instead. See migration guide here: https://python.langchain.com/docs/versions/migrating_chains/stuff_docs_chain/)ZsinceZremovalmessagecseZdZUdZeed<edddZeed<e ed<dZ e ed <e d d d Z e d deeedddZeee dfdd ZeeeedddZeeeeedddZd"eeeeee efdddZd#eeeeee efdddZee dd d!ZZ S)$StuffDocumentsChaina-Chain that combines documents by stuffing into context. This chain takes a list of documents and first combines them into a single string. It does this by formatting each document into a string with the `document_prompt` and then joining them together with `document_separator`. It then adds that new string to the inputs with the variable name set by `document_variable_name`. Those inputs are then passed to the `llm_chain`. Example: .. code-block:: python from langchain.chains import StuffDocumentsChain, 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}" ) llm_chain = LLMChain(llm=llm, prompt=prompt) chain = StuffDocumentsChain( llm_chain=llm_chain, document_prompt=document_prompt, document_variable_name=document_variable_name ) llm_chaincCstSr")rr'r'r'r(zStuffDocumentsChain.)default_factoryrrz rTZforbid)Zarbitrary_types_allowedextrabefore)mode)valuesr cCs`|djj}d|vr8t|dkr.|d|d<q\tdn$|d|vr\td|dd||S)zGet default document variable name, if not provided. If only one variable is present in the llm_chain.prompt, we can infer that the formatted documents should be passed in with this variable name. r3rrzQdocument_variable_name must be provided if there are multiple llm_chain_variableszdocument_variable_name z- was not found in llm_chain input_variables: )rinput_variableslen ValueError)clsr:Zllm_chain_variablesr'r'r("get_default_document_variable_names    z6StuffDocumentsChain.get_default_document_variable_name)r cs"fddjjD}tj|S)Ncsg|]}|jkr|qSr')r)r$kselfr'r( sz2StuffDocumentsChain.input_keys..)r3 input_keyssuper)rCZ extra_keys __class__rBr(rEs zStuffDocumentsChain.input_keys)docskwargsr c s>fdd|D}fdd|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 csg|]}t|jqSr')rrr#rBr'r(rDr5z3StuffDocumentsChain._get_inputs..cs$i|]\}}|jjjvr||qSr')r3rr<)r$rAvrBr'r( sz3StuffDocumentsChain._get_inputs..)itemsrr*r)rCrIrJZ doc_stringsr!r'rBr( _get_inputss  zStuffDocumentsChain._get_inputscKs2|j|fi|}|jjjfi|}|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. )rNr3rformatZ_get_num_tokens)rCrIrJr!rr'r'r( prompt_lengthsz!StuffDocumentsChain.prompt_lengthN)rI callbacksrJr cKs,|j|fi|}|jjfd|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. rQ)rNr3ZpredictrCrIrQrJr!r'r'r( combine_docssz StuffDocumentsChain.combine_docscs2|j|fi|}|jjfd|i|IdHifS)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. rQN)rNr3ZapredictrRr'r'r( acombine_docssz!StuffDocumentsChain.acombine_docscCsdS)Nr-r'rBr'r'r( _chain_typeszStuffDocumentsChain._chain_type)N)N)!__name__ __module__ __qualname____doc__r__annotations__rrr r/rrZ model_configr classmethodrrr@propertyrrEr r.rNrintrPrrrSrTrU __classcell__r'r'rGr(r2cs< %     r2)'rYtypingrrrrrZlangchain_core._apirZlangchain_core.callbacksrZlangchain_core.documentsr Zlangchain_core.language_modelsr Zlangchain_core.output_parsersr r Zlangchain_core.promptsr rZlangchain_core.runnablesrrZpydanticrrrZ'langchain.chains.combine_documents.baserrrrrZlangchain.chains.llmrr/r0r2r'r'r'r(s<      K