Skip to content

README.md

Latest commit

 

History

History
59 lines (43 loc) · 2.43 KB

README.md

File metadata and controls

59 lines (43 loc) · 2.43 KB

Learning module with conversational educational system - Backend Module

This repository contains the Backend Module developed as the primary functional layer for the Bachelor's Engineering Thesis: "Learning module with conversational educational system".

Academic Context

  • University: Warsaw University of Technology (Politechnika Warszawska)
  • Faculty: Faculty of Mathematics and Information Science (MiNI)
  • Supervisor: dr inż. Anna Wróblewska
  • Authors: Anna Ostrowska, Gabriela Majstrak, Jan Opala

Technical Stack & Architecture

The backend is built as a high-performance REST API using FastAPI, designed to utilize the RAG (Retrieval-Augmented Generation) pipeline.

Core Technologies:

  • Vector Database: ChromaDB for efficient semantic search and context retrieval.
  • OCR & Parsing: Adaptive document processing.
  • LLM Orchestration: Integration with OpenAI (GPT-4o-mini) and Google Gemini for generation, and Voyage AI for embeddings.
  • Task Scheduling: APScheduler automatically synchronizes course materials from Moodle.

Key Features

1. Advanced Document Ingestion

The system features a custom-built ingestion pipeline (parser_utils.py) that includes:

  • Adaptive OCR
  • Context-Aware Chunking
  • Math Normalization

2. Intelligent Services

  • RAG Pipeline: Uses vector search to provide factually grounded answers strictly based on course content.
  • Quiz Generation: Automated assessment creation based on Bloom's Taxonomy to evaluate student understanding.
  • Smart Sync: Periodic background jobs that check for new course materials without manual teacher intervention.

Repository Structure

  • app/api/routes/: Define API endpoints for chat, quiz, and dashboard features.
  • app/services/: Core business logic, including the RAG engine and embedding management.
  • app/core/parser_utils.py: Technical implementation of the parsing and OCR logic.
  • Dockerfile & docker-compose.yaml: Full containerization setup for easy deployment and scalability.

Deployment (Docker)

  1. Ensure your .env file is configured with the necessary API keys.
  2. Build and start the system:
docker compose up -d --build

The API will be available at http://localhost:8000 with interactive Swagger documentation at /docs.

Developed as the primary technical part of the diploma process at Warsaw University of Technology.