An AI-powered academic advisor that works for any college. Tell it your career goal and completed courses — it tells you exactly what to study next.
Built with RAG (Retrieval-Augmented Generation) using LangChain, FAISS, and LLaMA 3.3 70B.
- You tell it: your career goal + courses you've completed + credit limit
- It tells you: exactly which courses to take next semester and why
- It shows you: a full roadmap from where you are to your career goal
- It never recommends a course you're not eligible for
The sample data inside Data/sample_courses.json is from VTU (India).
But you can use this for your own college by adding your course data.
See how to add your college's data.
| Tool | Purpose |
|---|---|
| LangChain | RAG pipeline |
| FAISS | Vector search |
HuggingFace all-MiniLM-L6-v2 |
Embeddings |
| Groq + LLaMA 3.3 70B | AI reasoning |
| Streamlit | Web UI |
User Input
│
├── Career Goal (e.g. "I want to be an ML Engineer")
├── Completed Courses (e.g. CS101, CS201)
└── Credit Limit (e.g. 15 credits)
│
▼
RAG Pipeline
│
├── courses.json → FAISS Vector Database
│ └── Embeds all course topics and outcomes
│
├── career.json → Career Requirements Lookup
│ └── Finds required skills for your goal
│
▼
LLaMA 3.3 70B (via Groq)
│
└── Matches your completed courses + career goal
└── Recommends exactly what to study next
│
▼
Output
│
├── Recommended courses for next semester
├── Why each course is recommended
└── Full roadmap to your career goal
1. Clone the repo
git clone https://github.com/BiswasNehaa/universal-academic-advisor.git
cd universal-academic-advisor2. Install dependencies
pip install -r requirements.txt3. Add your API key
Create a .env file and add:
GROQ_API_KEY=your_key_here
Get a free key at console.groq.com
4. Build the vector database
python app/ingest.py5. Run the app
streamlit run app/app.pyThe easiest way to contribute is to add your college's course data.
See the contribution guide to get started. All experience levels welcome.
Nehaa Biswas — 3rd year CS student