📄 QUEST AI Assistant

An AI-powered Retrieval-Augmented Generation (RAG) application that allows users to ask questions directly from official QUEST (Quaid-e-Awam University of Engineering, Science and Technology) documents.

The app uses FAISS for vector search, HuggingFace embeddings, and Groq LLMs, wrapped in a modern Streamlit UI with a clean light-mode AI chat interface.

---

🚀 Features

• 🔍 Semantic search over official QUEST documents
• 📚 FAISS vector database for fast retrieval
• 🤖 Groq-powered LLM responses
• 🧠 BAAI bge-base-en-v1.5 embeddings
• 💬 Modern AI chat-style UI (light mode, vibrant design)
• 🧾 Source-aware context injection
• ⚡ Fast, accurate, and production-ready RAG pipeline

---

🧠 How It Works (RAG Pipeline)

1. Documents Ingestion
   QUEST prospectus and official PDFs are chunked and embedded

2. Vector Storage
   Embeddings are stored in a FAISS index

3. Query Processing
   User question is embedded using the same model

4. Retrieval
   FAISS returns the most relevant document chunks

5. Generation
   Groq LLM generates answers strictly from retrieved context

---

🛠 Tech Stack

Component	Technology
Frontend	Streamlit
Vector Store	FAISS
Embeddings	HuggingFace BAAI/bge-base-en-v1.5
LLM Provider	Groq
Frameworks	LangChain
Language	Python

---

📂 Project Structure

.
├── app.py                # Main Streamlit application
├── faiss.index           # FAISS vector index
├── metadata.pkl          # Document metadata & mappings
├── README.md             # Project documentation
├── RAG.ipynb             # Jupyter Notebook for RAG pipeline
└── requirements.txt      # Python dependencies

---

⚙️ Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/your-username/quest-rag-assistant.git
cd quest-rag-assistant

2️⃣ Create Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Add Groq API Key

Create the file .streamlit/secrets.toml:

GROQ_API_KEY = "your_groq_api_key_here"

---

▶️ Run the Application

streamlit run app.py

Open in your browser:

http://localhost:8501

---

🧪 Example Queries

• What is the admission criteria for BE programs?
• What is the fee structure for undergraduate students?
• Which departments are offered at QUEST?
• What are the eligibility requirements?

---

🔒 Safety & Accuracy

• ✅ The assistant ONLY uses retrieved document context
• ✅ If information is missing, it responds with:

  Not found in documents.
  

• ✅ No hallucinated answers are generated

---

🎨 UI Highlights

• Light-mode focused design
• Vibrant modern AI interface
• Chat-style question and answer flow
• Clean typography and spacing

---

📈 Future Improvements

• Streaming token-by-token responses
• Conversation memory
• Clickable citations per source
• Hybrid retrieval (BM25 + FAISS)
• Docker deployment
• Multi-PDF support

---

⭐ Acknowledgements

• FAISS for fast similarity search
• HuggingFace for open-source embeddings
• Groq for high-speed LLM inference
• Streamlit for rapid UI development

---

📜 License

This project is intended for educational and research purposes.

---

⭐ If you find this project useful, please consider giving it a star!