ARGObot: Academic Advising Chatbot Powered with AI Agent

ARGObot is a custom-built Retrieval-Augmented Generation (RAG) chatbot designed to answer university-related questions using either OpenAI or Google Gemini. It reads from official student handbooks and optionally supplements answers using Google Search.

Features

• Switchable backend (OpenAI or Gemini)
• LangGraph for stateful chat memory
• RAG pipeline using LangChain + ChromaDB
• Clean Streamlit interface
• Modular file structure
• Secure .env-based configuration

---

Project Structure

src/
├── agents/             # Tools and prompt templates
├── chains/             # RAG pipelines
├── config/             # Environment and secrets
├── interface/          # Streamlit UI
├── state.py            # LangGraph state and workflow
└── main.py             # Unified app entrypoint

---

Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/argobot.git
cd argobot

2. Install Requirements

pip install -r requirements.txt

3. Configure Environment

Create a .env file based on .env.example:

MODEL_PROVIDER=openai
OPENAI_API_KEY=your-openai-key
GOOGLE_API_KEY=your-gemini-key
PDF_PATH=resources/22_studenthandbook-22-23_f2.pdf

4. Run the App

streamlit run src/main.py

---

How It Works

1. Loads a PDF knowledge base (e.g., UWF Student Handbook)
2. Splits it into text chunks using LangChain
3. Embeds and stores chunks in a vector store
4. Uses LangGraph to manage conversation state
5. Responds via OpenAI or Gemini based on your configuration

---

Technologies Used

• LangChain
• LangGraph
• Streamlit
• OpenAI
• Google Gemini
• ChromaDB

---

Security Note

Do not share your .env or credentials.json publicly. Always use .env.example for version control.

---

License

MIT License — free for personal and commercial use.

---

Author

Maryam Taeb
Contact me for collaboration or custom deployments.