GlobalTech
AI & Machine Learning Project
Aegis RAG: Document Q&A System
FastAPI
Python
FAISS Vector DB
Gemini API
Project Overview
An advanced Retrieval-Augmented Generation (RAG) system utilizing FastAPI and local FAISS vector indices to query custom PDF documents with real-time semantic context injection.
Deliverables & Support Checklist
- Complete Source Code: Clean, well-commented code, database schemas, and setup configurations (Github/Zip format).
- Remote Setup Support: Complete environment installation and compiler/server startup runs done via AnyDesk or TeamViewer by a senior developer.
- Project Report & PPT template: Synced system flowcharts, UML diagrams, hardware/software specs, and presentation slides.
- Viva Voce Assistance: 1-on-1 code walkthrough explanation calls to prepare you for department evaluations and external examiners.
- Post-Submission Modifications: Lifetime bug-fixing support and code tweaks requested by project coordinators.
Viva-Voce Q&A Highlights
Q1: What is the primary architecture of this system?
A: It is structured as a standalone full-stack microservices simulation. It splits rendering blocks (HTML, modular JavaScript components) from backend logic handling algorithms, keeping standard RESTful conventions.
Q2: How is security handled inside this model?
A: Code modules execute standard local authentication. Security structures utilize cryptographic hashing (such as SHA-256 block hash references) and local password salts where required.
Buy Complete Project
₹9,999
Message on WhatsApp
No payment required now. Our developer will show you the running code live on Google Meet/AnyDesk. Pay only if satisfied!