langchain.chains.combine_documents.stuff.create_stuff_documents_chain¶

langchain.chains.combine_documents.stuff.create_stuff_documents_chain(llm: Runnable[Union[PromptValue, str, Sequence[BaseMessage]], Union[BaseMessage, str]], prompt: BasePromptTemplate, *, output_parser: Optional[BaseOutputParser] = None, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = '\n\n') → Runnable[Dict[str, Any], Any][source]¶

Create a chain for passing a list of Documents to a model.

Parameters

  • 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

# 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})