◈ RAGChat

Chat with your PDF documents using AI. Upload a PDF, ask anything — answers are grounded in your document.

Tech Stack

• Frontend/UI — Streamlit
• Embedding — paraphrase-multilingual-MiniLM-L12-v2 (SentenceTransformers)
• Vector DB — FAISS
• LLM — Llama 3.3 via Groq API (free)

Features

• Upload PDF directly from the browser
• Automatic chunking & embedding
• Similarity search with FAISS
• Context-grounded answers (RAG)
• Supports English & Indonesian

Project Structure

rag/
├── streamlit_app.py      # Main UI
├── vector_store.py       # FAISS index
├── retriever.py          # Similarity search
├── chain.py              # Groq LLM chain
├── requirements.txt
└── src/
    ├── loader.py         # PDF loader
    └── embedding.py      # Chunking & embedding

Running Locally

1. Clone the repo

git clone https://github.com/givenglorious/rag.git
cd rag

2. Install dependencies

pip install -r requirements.txt

3. Create .streamlit/secrets.toml

GROQ_API_KEY = "gsk_xxxxxxxxxx"

4. Run

streamlit run streamlit_app.py

Deploy to Streamlit Cloud

1. Push to GitHub
2. Go to share.streamlit.io → New app
3. Select repo, set main file: streamlit_app.py
4. Under Advanced settings → Secrets, add:

GROQ_API_KEY = "gsk_xxxxxxxxxx"

5. Deploy!

Getting a Free Groq API Key

1. Sign up at console.groq.com
2. Create a new API key
3. Paste it into Streamlit secrets