genai-article-generator

Overview

This project is designed to demonstrate the use of Retrieval-Augmented Generation (RAG) and LangChain technology to build a context-aware newspaper article generator. The tool retrieves relevant information from Wikipedia based on a user query, generates a brief article in three paragraphs, and creates a catchy headline for the article.

Features

• Retrieval-Augmented Generation (RAG): Retrieves contextually relevant information from Wikipedia to enhance the quality and relevance of generated articles.
• LangChain Integration: Utilizes LangChain's Runnable components to create a modular and composable sequence of operations for article generation.
• Text Generation and Summarization: Employs GPT-2 for text generation and a summarization model for headline creation.

Installation

1. Clone the Repository

git clone https://github.com/greshmashaji/genai-article-generator.git
cd genai-article-generator

2. Create a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

pip install -r requirements.txt

Usage

Run the Script:

python originalArticle.py

Code Explanation

fetch_wikipedia_summary(query)

Fetches a summary from Wikipedia based on the provided query.

generate_text(input_text)

Generates an article using GPT-2 and ensures it is split into three paragraphs.

generate_headline(input_text)

Generates a catchy headline using a summarization model.

WikipediaSummaryStep(Runnable)

Retrieves information from Wikipedia and adds it to the inputs.

ArticleGenerationStep(Runnable)

Generates an article using the retrieved information and ensures it is three paragraphs.

HeadlineGenerationStep(Runnable)

Generates a headline for the article using the summarization model.

create_newspaper_article(query)

Combines the custom steps using LangChain's RunnableSequence to create the final article and headline.