An intelligent multi-agent RAG (Retrieval-Augmented Generation) system designed to assist students in campus placement preparation through AI-powered Q&A.
- Multi-agent architecture for specialized query handling
- RAG-based responses grounded in placement preparation content
- Covers DSA, CS fundamentals (OS, DBMS, CN), and interview tips
- Clean conversational UI built with React
- Fast and lightweight — deployed on Vercel
| Layer | Technology |
|---|---|
| Frontend | React.js, JavaScript, CSS |
| AI Layer | RAG (Retrieval-Augmented Generation) |
| Agent Framework | Multi-agent orchestration |
| Deployment | Vercel |
| Build Tool | Vite |
git clone https://github.com/dharineesh-812/Multiagent-RAG.git
cd Multiagent-RAG
npm install
npm run dev- User submits a placement-related question
- The router agent identifies the query type (DSA / CS theory / HR)
- The relevant specialized agent retrieves context from the knowledge base
- RAG pipeline generates a grounded, accurate response
- Answer is displayed in the conversational UI
- ChromaDB vector store integration for semantic search
- LangChain agent orchestration
- PDF upload for custom study material ingestion
- Mock interview simulation mode
- Performance analytics dashboard