Artificial Intelligence has transformed how we process and digest information. One of the most practical use cases is automated document summarization — a tool that helps professionals condense long reports, research papers, and documents into concise and meaningful summaries. In this tutorial, we’ll walk through how to build an AI-powered summarizer using LangChain, OpenAI’s GPT models, and Python.
Setting Up the Environment
Before coding, make sure Python 3.9 or higher is installed. Then, set up a new virtual environment and install dependencies:
bashpython -m venv ai_summarizer source ai_summarizer/bin/activate # or ai_summarizer\Scripts\activate on Windowspip install openai langchain python-dotenv
Create a .env file in your project directory to securely store your OpenAI API key:
bashOPENAI_API_KEY=your_openai_api_key_here
Loading and Preprocessing Text
Your summarizer needs text input. You can feed it text from a file, web page, or PDF. Let's start simple with a .txt file.
pythonfrom langchain.document_loaders import TextLoader loader = TextLoader("example_report.txt")documents = loader.load()
LangChain splits long documents into small chunks (tokens) to improve summarization accuracy.
pythonfrom langchain.text_splitter import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)texts = splitter.split_documents(documents)
Creating the Summarization Chain
We’ll now connect the text chunks to OpenAI’s model through LangChain’s summarization chain.
pythonfrom langchain.chains.summarize import load_summarize_chain from langchain.llms import OpenAI llm = OpenAI(temperature=0.3)chain = load_summarize_chain(llm, chain_type="map_reduce")summary = chain.run(texts)print(summary)
This “map-reduce” approach allows the model to summarize individual text chunks and then combine them into a cohesive global summary.
Enhancing the Summarizer
You can customize the summarizer for specific use cases:
- [object Object]
- [object Object]
- [object Object]
Deploying as an API
To make this summarizer accessible as a service, deploy it using FastAPI:
pythonfrom fastapi import FastAPI, UploadFile import uvicorn app = FastAPI()@app.post("/summarize")asyncdefsummarize(file: UploadFile): content =awaitfile.read() text = content.decode("utf-8") loader = TextLoader.from_text(text) docs = splitter.split_documents(loader.load()) summary = chain.run(docs)return{"summary": summary}if __name__ =="__main__": uvicorn.run(app, host="0.0.0.0", port=8000)
With just a few lines, you now have a working AI summarization API you can integrate into apps, dashboards, or automation systems.