How to summarize text in a single LLM call

LLMs can summarize and otherwise distill desired information from text, including large volumes of text. In many cases, especially for models with larger context windows, this can be adequately achieved via a single LLM call.

LangChain implements a simple pre-built chain that "stuffs" a prompt with the desired context for summarization and other purposes. In this guide we demonstrate how to use the chain.

Load chat model

Let's first load a chat model:

Select chat model:

OpenAI

Anthropic

Azure

Google

AWS

Cohere

NVIDIA

Fireworks AI

Groq

Mistral AI

Together AI

Databricks

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

Load documents

Next, we need some documents to summarize. Below, we generate some toy documents for illustrative purposes. See the document loader how-to guides and integration pages for additional sources of data. The summarization tutorial also includes an example summarizing a blog post.

from langchain_core.documents import Document

documents = [
    Document(page_content="Apples are red", metadata={"title": "apple_book"}),
    Document(page_content="Blueberries are blue", metadata={"title": "blueberry_book"}),
    Document(page_content="Bananas are yelow", metadata={"title": "banana_book"}),
]

API Reference:Document

Load chain

Below, we define a simple prompt and instantiate the chain with our chat model and documents:

from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("Summarize this content: {context}")
chain = create_stuff_documents_chain(llm, prompt)

API Reference:create_stuff_documents_chain | ChatPromptTemplate

Invoke chain

Because the chain is a Runnable, it implements the usual methods for invocation:

result = chain.invoke({"context": documents})
result

'The content describes the colors of three fruits: apples are red, blueberries are blue, and bananas are yellow.'

Streaming

Note that the chain also supports streaming of individual output tokens:

for chunk in chain.stream({"context": documents}):
    print(chunk, end="|")

|The| content| describes| the| colors| of| three| fruits|:| apples| are| red|,| blueberries| are| blue|,| and| bananas| are| yellow|.||

Next steps

See the summarization how-to guides for additional summarization strategies, including those designed for larger volumes of text.

See also this tutorial for more detail on summarization.