first implementation

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Adrien
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# AI Teacher — Neurosurgeon RAG Learning Platform
A web application for neurosurgeons to upload medical textbooks (PDF), have them
embedded into a pgvector store, then select from a predefined topic list to receive
AI-generated cross-book summaries, and engage in grounded RAG chat.
## Architecture
```mermaid
graph TD
User["Neurosurgeon (Browser)"]
FE["Frontend\nVue.js 3 / Vite\n:5173"]
BE["Backend\nSpring Boot 4 / Spring AI\n:8080"]
DB["PostgreSQL + pgvector\n(provided)"]
LLM["LLM Provider\n(OpenAI / configurable)"]
User -->|HTTP| FE
FE -->|REST /api/v1/...| BE
BE -->|JDBC / pgvector| DB
BE -->|Embedding + Chat API| LLM
```
## Stack
- **Backend**: Spring Boot 4.0.5 + Spring AI 2.0.0-M4, Java 21, Maven
- **Frontend**: Vue.js 3 + Vite + TypeScript + Pinia + Axios
- **Database**: PostgreSQL 16 + pgvector extension
- **Auth**: HTTP Basic (single shared in-memory user)
## Quick Start
See [specs/001-neuro-rag-learning/quickstart.md](specs/001-neuro-rag-learning/quickstart.md) for full instructions.
### Local Dev
```bash
# Start the database
docker compose up -d
# Backend
cd backend
mvn spring-boot:run
# Frontend
cd frontend
npm install
npm run dev
```
### Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | OpenAI API key for embeddings and chat |
| `APP_PASSWORD` | Yes | Shared password for HTTP Basic auth |
| `DB_URL` | Yes | JDBC URL, e.g. `jdbc:postgresql://localhost:5432/aiteacher` |
| `DB_USERNAME` | Yes | Database username |
| `DB_PASSWORD` | Yes | Database password |