Wikipedia-Based Question Answering System using RAG

Wikipedia-Based Question Answering System using RAG check out this project deployed on hugging face : https://huggingface.co/spaces/smrup/RAG_using_Wikipedia_based_QA

This project retrieves Wikipedia content, processes it into smaller text chunks, and allows users to query information using FAISS-based retrieval and a Question Answering (QA) model.

📌 Features ✅ Fetches Wikipedia content for any given topic. ✅ Splits the content into manageable text chunks. ✅ Uses Sentence Transformers to generate vector embeddings. ✅ Implements FAISS for fast similarity search. ✅ Answers user questions using Roberta-based QA model.

📂 Project Structure wikipedia-qa/ │── app.py # Main script to run the project │── utils.py # Utility functions (text processing, Wikipedia retrieval) │── models.py # Model handling (loading, embedding, FAISS search) │── requirements.txt # Dependencies │── README.md # Project documentation

🛠 How It Works 1️⃣ User enters a Wikipedia topic → Wikipedia content is retrieved. 2️⃣ Text is split into smaller chunks → Helps with efficient search. 3️⃣ Chunks are embedded using Sentence Transformers → Converts text into vectors. 4️⃣ FAISS index is created → Enables fast retrieval of relevant text. 5️⃣ User asks a question → The most relevant chunks are retrieved. 6️⃣ QA model extracts the answer → Using Roberta-based SQuAD2 model.

📌 Example Usage Enter a topic to learn about: Artificial Intelligence Ask a question about the topic: What is AI? 🔹 Retrieved Chunks:

AI is the simulation of human intelligence in machines.

It includes learning, reasoning, and self-correction.

AI is used in various applications like chatbots, robotics, etc.

🔹 Answer:

"AI is the simulation of human intelligence in machines."

📦 Dependencies The following libraries are required: wikipedia-api transformers sentence-transformers faiss-cpu numpy