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]

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:

# 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:

{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})