// SYSTEM_ARCHITECTURE :: VERSION_2.0

System Architecture

A comprehensive overview of MarunGPT's technical infrastructure, component architecture, and system design principles.

Architecture Overview

MarunGPT is built on a modular, scalable architecture designed to handle complex academic queries with high accuracy and performance. The system leverages modern RAG (Retrieval-Augmented Generation) techniques to provide contextually relevant responses from Marmara University's extensive data resources.

LAYER_1 :: FRONTEND
UI_COMPONENTS
LAYER_2 :: API_GATEWAY
REST_API
LAYER_3 :: PROCESSING
RAG_ENGINE
DATA_LAYER :: STORAGE
VECTOR_DB
DOCUMENT_STORE

Core Components

Each component is designed with scalability, reliability, and performance in mind. The modular architecture allows for independent updates and optimizations.

Data Layer

ACTIVE

Centralized storage and retrieval system for university data resources. Handles document indexing, vector embeddings, and semantic search capabilities.

Processing Engine

OPTIMIZED

Advanced NLP processing unit that handles query understanding, context extraction, and response generation with high accuracy.

API Gateway

STABLE

RESTful interface layer that manages request routing, authentication, and rate limiting for secure access to the system.

Security Layer

ENFORCED

Multi-layered security framework ensuring data privacy, access control, and compliance with academic data protection standards.

Cache System

ACTIVE

High-performance caching mechanism that reduces latency and improves response times for frequently accessed queries.

Integration Layer

CONNECTED

Seamless integration with university systems, databases, and external APIs to provide comprehensive data access.

Technical Specifications

Detailed technical specifications and performance metrics of the MarunGPT architecture.

  • ARCHITECTURE:MODULAR_RAG
  • PROCESSING:ASYNC_PARALLEL
  • STORAGE:VECTOR_DATABASE
  • API_PROTOCOL:REST_JSON
  • SECURITY:TLS_1.3
  • UPTIME:99.9%