DORA is an educational meta search engine inspired by Dora the Explorer. It is designed to help students and researchers discover academic material from multiple learning sources through one unified search experience.
- Next.js
- Tailwind CSS
- Flutter
- Spring Boot
- Spring Data JPA
- REST API
- PostgreSQL
- Jsoup
- YouTube API
- GitHub API
- arXiv API
- Wikipedia API
- Maven
- Git
- Postman
We are starting with a modular monorepo:
backend/holds the Spring Boot application.frontend/holds the Next.js web client.mobile/holds the Flutter mobile application.docs/stores architecture and planning notes.shared/keeps cross-project contracts and shared schemas.crawler/is reserved for controlled educational web crawling jobs.indexer/is reserved for indexing and ranking experiments.
The first version should be a modular monolith, not microservices. That gives us:
- faster development with one backend deployment
- clear module boundaries for future extraction
- simpler local setup for a student project
- enough structure to support API search, crawling, and local indexing
- The user searches from the Next.js frontend.
- The frontend sends the query to the Spring Boot REST API.
- The backend combines results from PostgreSQL, external APIs, and selected crawled sources.
- The ranking layer scores results using relevance, source priority, and recency.
- The frontend renders grouped results such as papers, videos, tutorials, articles, and source code.
flowchart TD
A[User Browser] --> B[Next.js Frontend UI]
B --> C[Spring Boot Backend REST API]
C --> D[Search Service]
C --> E[API Fetch Service]
C --> F[Crawler Service]
C --> G[Ranking Service]
D --> H[(PostgreSQL Database)]
F --> H
E --> I[YouTube API]
E --> J[GitHub API]
E --> K[arXiv API]
E --> L[Wikipedia API]
G --> D
H --> D
D --> C
C --> B
See docs/architecture.md for the detailed system design.