title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
Rag Mini Wikipedia Demo	💬	yellow	purple	gradio	4.36.1	app.py	false	apache-2.0

RAG Project: Enhanced Information Retrieval for LLMs

This project demonstrates a Retrieval-Augmented Generation (RAG) application for improved question answering using Large Language Models (LLMs). RAG overcomes LLM limitations by enabling access to specific information not included in their training data.

Key Features:

• Uses rag-mini-wikipedia dataset for factual information retrieval.
• Employs all-MiniLM-L6-v2 for sentence encoding and FAISS for efficient similarity search.
• Leverages meta-llama/Llama-2-7b-chat-hf model for response generation.
• Built with Gradio 4.38.1 (a user interface library for machine learning).

Benefits:

• More informative responses by incorporating external knowledge.
• Ideal for applications like smart Q&A chatbots in corporate knowledge bases.

Running the Application:

1. Ensure you have the required libraries installed (refer to the project's requirements).
2. Open a terminal and navigate to the project directory.
3. Login to your Hugging Face account with appropriate token:

huggingface-cli login

4. Run the application using the following command:

python app.py

This will launch the Gradio interface where you can interact with the RAG model.

Further details:

Research notebook in the 'Research' folder explores chunking, prompt development, and future directions. Read it !