🤖 QuickRAG - FAQ Bot

An intelligent Retrieval-Augmented Generation (RAG) chatbot for answering FAQs using domain-specific datasets. The bot indexes FAQs into Qdrant Cloud (vector DB) and retrieves relevant context to generate accurate answers with Groq LLM.

Built with modular Python code, this project demonstrates end-to-end RAG pipelines — from embeddings to vector search to response generation.

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🏗️ Architecture

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📊 Key Features

• 🔎 Semantic Search: Retrieves relevant FAQ context from Qdrant Cloud
• 📂 Domain-Specific Indexing: Import datasets (CSV/HuggingFace) and index easily
• ⚡ Groq-powered LLM: Fast and concise answer generation
• 🛠️ Local & Cloud Support: Choose between in-memory DB (quick test) or Qdrant Cloud
• 🖥️ Notebook Demo: End-to-end workflow in Jupyter Notebook
• 📸 Screenshots included for quick visualization

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🧰 Tech Stack

• Python 🐍
• SentenceTransformers (MiniLM embeddings)
• Qdrant Cloud (vector DB)
• Groq API (answer generation)
• Pandas, NumPy
• Jupyter Notebook for demo

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📷 Screenshots

Indexing Data	Query Answering	Multi-Query Demo

All screenshots are stored in the assets/ folder.

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🚀 Run Locally

1. Clone the repository

git clone https://github.com/nv2105/QuickRAG-FAQ-Bot.git
cd QuickRAG-FAQ-Bot

2. Install dependencies

pip install -r requirements.txt

3. Add API Keys

Create a .env file in the project root:

QDRANT_URL=your_qdrant_cluster_url
QDRANT_API_KEY=your_qdrant_api_key
GROQ_API_KEY=your_groq_api_key   # optional but recommended

4. Run the notebook demo

jupyter notebook QuickRAG_Demo.ipynb

📁 Project Structure

QuickRAG-FAQ-Bot/
│── assets/                 # Screenshots & diagrams
│── data/                   # FAQ CSV datasets
│── src/
│   ├── rag.py              # Main RAG pipeline
│   ├── embeddings.py       # Embedding generator
│   ├── db_qdrant.py        # Qdrant Cloud integration
│   ├── db_local.py         # In-memory DB (testing)
│   ├── load_data.py        # Indexing script
│   ├── config.py           # Env key loader
│   └── __init__.py
│── QuickRAG_Demo.ipynb     # Jupyter Notebook demo
│── requirements.txt
│── README.md

👨‍💻 Author

Naman Vora

Final Year CSE Student | Aspiring Data Analyst 📫 LinkedIn↗ • GitHub↗

📄 License

This project is open source and available under the MIT License.↗