_chain

Help on function create_stuff_documents_chain in module langchain.chains.combine_documents.stuff:

ccrreeaattee__ssttuuffff__ddooccuummeennttss__cchhaaiinn(llm: langchain_core.runnables.base.Runnable[typing.Union[langchain_core.prompt_values.PromptValue, str, typing.Sequence[typing.Union[langchain_core.messages.base.BaseMessage, typing.Tuple[str, str], str, typing.Dict[str, typing.Any]]]], typing.Union[langchain_core.messages.base.BaseMessage, str]], prompt: langchain_core.prompts.base.BasePromptTemplate, *, output_parser: Optional[langchain_core.output_parsers.base.BaseOutputParser] = None, document_prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None, document_separator: str = '\n\n') -> langchain_core.runnables.base.Runnable[typing.Dict[str, typing.Any], typing.Any]
    Create a chain for passing a list of Documents to a model.
    
    Args:
        llm: Language model.
        prompt: Prompt template. Must contain input variable "context", 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.
    
    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_name="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})