metadata

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)

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