README.md

metadata

title: NLP Assignment RAG App
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: main.py
pinned: false
python_version: '3.12'
app_build_command: uv sync

NLP Assignment – Research Paper RAG Assistant

This app retrieves and summarizes academic papers related to your research query using a Retrieval-Augmented Generation (RAG) pipeline built with:

• FAISS vector search (index.faiss, index.pkl)
• SentenceTransformer CrossEncoder reranker
• Google Gemini (via LangChain) as the LLM
• Gradio UI frontend
• uv package manager (modern, fast, Python-native)

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🚀 How to Run Locally

You can run this project locally using uv (recommended) or plain Python.

🧩 Prerequisites

• Python 3.12 or higher
• uv installed:

  pip install uv

1. Clone the repository

git clone https://huggingface.co/spaces/njayman/nlp-assignment
cd nlp-assignment

2. Add your FAISS index files

Make sure your FAISS index folder is structured like this:

faiss_index/
├── index.faiss
└── index.pkl

3. Set up environment

Set your Gemini API key (replace with your own key):

Linux/macOS:

export GOOGLE_API_KEY="your_google_api_key_here"

Windows (PowerShell):

$env:GOOGLE_API_KEY="your_google_api_key_here"

Or add a .env file

GOOGLE_API_KEY="your_google_api_key_here"

4. Create a virtual env and install dependencies using uv

uv venv
uv sync

5. Run the Gradio app

uv run main.py

Then open the link printed in your terminal — usually:

http://127.0.0.1:7860