AI · B.Tech 3rd / 4th year · major · 6–8 weeks guided

Intelligent Campus Helpdesk with RAG and Tool-Using Agent

End-to-end GenAI project: ingest college PDFs, retrieve with citations, call tools for timetable/fees, and expose a FastAPI + React helpdesk. Built so a panel can ask “where did this answer come from?”

Student project kit

₹14,999/-₹8,999/-

1–4 students · 6–8 weeks guided. College title can be adapted on the counselling call.

Audience

Who it is for

CSE / IT / AI&DS teams who need a current AI major project (RAG + tools), not only a static chatbot screenshot.

A demoable agent: upload a circular → ask a question → see the paragraph cited → optional tool call → audit log. README a guide can run on a laptop.

Branches

CSEITAI & DSAIML

Team size: 1–4 students

Problem

Existing vs proposed

Students wait in office queues for circulars, exam dates, and fee rules that already exist as PDFs. Generic ChatGPT answers without sources and cannot query live college data.

A retrieval-augmented helpdesk with an admin corpus, embedding search, citation cards, and allow-listed tools (timetable SQL, fee table). Empty-hit behaviour and an eval set of 40 campus questions.

Build

Modules

Python 3FastAPIEmbeddings + ChromaTool callingReactJWTPostgreSQLDocker
  1. 1. Corpus & admin

    • PDF/DOCX ingest, chunking, metadata (dept, year)
    • Admin replace/delete documents
    • PII redaction note
  2. 2. Retrieve with citations

    • Embeddings, top-k, score threshold
    • Answer only from hits; show snippet + file name
    • Eval set and failure cases
  3. 3. Tools & agent loop

    • Timetable and fee lookup tools
    • Allow-list, timeouts, stop rules
    • Trace of plan → tool → final reply
  4. 4. Student app

    • Login, chat UI, history
    • Thumbs up/down for retrieval quality
    • Export a support ticket

Demo

What you can show

  • Citations on every grounded answer
  • Tool-using path the viva can step through
  • Admin corpus, JWT roles, Docker runbook

Viva

Panel topics

  • RAG vs fine-tuning
  • Hallucination controls
  • Chunk size trade-offs
  • Prompt injection

Deliverables

Included with the kit

  • Complete source code (ZIP + GitHub-style repo)
  • Database schema and sample data / dataset notes
  • README.md — how to run, stack, modules, and demo path (Windows + college lab)
  • Viva question bank with short answers
  • Two mentor reviews: architecture, then final demo

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