An engineering project implementing an intelligent, context-aware study workspace. The application processes long-form academic texts, extracts structured study data, generates exam revision variables, and dynamically manages user sessions. It features background synchronization pipelines for tracking user interactions.
- Backend: Flask (Python REST API architecture)
- Frontend: Responsive HTML5, CSS3 Grid, JavaScript (Fetch API asynchronous data handling)
- AI Core Inference: OpenRouter Cloud Infrastructure Pipeline (Free Tier Core Engine)
- Automation Pipeline: Make.com Webhook Relays
- Data Storage: Google Sheets Synchronized Database Matrix
- Asynchronous Text Ingestion: Processes unstructured raw text nodes safely via local sessions.
- Context-Aware Summarization: Synthesizes dense data vectors into actionable high-yield bullet elements.
- Automated Exam Parameter Formulation: Generates structured revision metrics matching professor evaluation goals.
- Contextual Retrieval-Augmented Chatbot (RAG): Restricts the LLM conversational landscape strictly to the student's loaded document metrics.
- Auto-Tracking Relays: Multi-thread asynchronous background triggers log variables directly to Google Sheets using Make.com pipelines.
- Clone the repository:
git clone <your-repository-url>