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---
title: UB Virtual Student Assistant (UB-VSA)
emoji: 🎓
colorFrom: pink
colorTo: blue
sdk: docker
pinned: false
short_description: AI assistant for University at Buffalo students.
---
# UB Virtual Student Assistant (UB-VSA)
**RAG-powered chatbot for UB admissions, visa & campus life questions**
UB-VSA is a **retrieval-augmented conversational agent** that gives accurate, source-grounded answers about the University at Buffalo, with special focus on the needs of international students (F-1/OPT, I-20, course selection, housing, etc.).
It combines:
| Layer | Tech |
|-------|------|
| **Retriever** | FAISS dense vectors (`BAAI/bge-small-en-v1.5`) + keyword hybrid search |
| **Reranker** | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
| **Fine-tuning** | LoRA / QLoRA adapters on `microsoft/phi-1_5` with RAFT |
| **API** | Flask (backend) |
| **UI** | Flask (+ vanilla JS) single-page chat |
---
## ✨ Features
* **Context-aware chat** – tracks the last few turns for coherent follow-ups.
* **Grounded citations** – every answer lists clickable sources.
* **Lightweight adapters** – fine-tuned with LoRA/QLoRA; base model stays frozen.
* **One-click deploy** – Dockerfile + `start.sh` spin up scraper, vector-store, API & UI.
* **Scalable vector DB** – FAISS index stored in `data/embeddings/`.
---
## 🖥️ Online Demo
> 👉 **Try it live:** https://huggingface.co/spaces/TeamSAS/UB_VSA
We’re on the free HF hardware tier; first response may take ~30 s while the container wakes up.
---
## 🏗️ Project Structure
```
buffalo_rag/
├── api/ # FastAPI routes & schemas
├── embeddings/ # Chunker + sentence‑transformer encoder
├── frontend/ # Flask templates & static JS/CSS
├── model/ # BuffaloRAG orchestration
├── scraper/ # Playwright / BeautifulSoup crawler
└── vector_store/ # FAISS DB + hybrid search logic
data/
└── embeddings/ # .pkl embeddings + faiss_index.pkl (generated)
Dockerfile
start.sh
main.py # CLI pipeline entry‑point
```
## Quick Start (Local)
```bash
git clone https://huggingface.co/spaces/TeamSAS/UB_VSA
cd UB_VSA
# 1. Python deps
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
export HUGGINGFACEHUB_API_TOKEN=hf_your_token_here
# 3. Build embeddings (only first time, ~5 min)
python main.py --build
# 4. Launch backend + frontend
python main.py --run
```
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