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Ananya S
Ananya S

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🚀 I Built an AI-Powered Fest Assistant with Agents, RAG & Planning (Pragyan @ NITT)

#ai #python #langchain #rag

I deleted Instagram more than a year ago, and honestly, it saved me from a lot of distractions.

But there was an unexpected downside.

A lot of informal, real-time information — especially during college events — still lives there.

During our college fest, for example:

  • Event schedules
  • Last-minute updates
  • Food stall announcements
  • Informal activities

…everything gets posted on Instagram.

At the same time:

  • Fest details and significance are on the official website
  • Food stall info is on a separate app
  • The entire 3-day schedule is compressed into a few posts

There’s no single place to get a clear, structured view of everything.

And that’s when it hit me:

Most college fests have websites.
Some even have apps.

But none of them actually help you navigate the fest intelligently.

Most college fests have websites.
Some even have apps.

But none of them actually help you navigate the fest intelligently.

They give information.
They don’t give guidance.

But I wanted to build something smarter —
an AI assistant that actually understands queries, plans your day, and even helps you find teammates.

So, I built Pragyan Mentor Assistant — an AI-powered system for navigating a techno-managerial fest.

🎯 Problem

During college fests like Pragyan (NIT Trichy):
There are

  • There are dozens of events, workshops, and shows
  • Information is scattered across PDFs, sites, and posters

  • Users don’t know:

    • what to attend
    • what matches their interests
    • how to plan their time
    • who to team up with

👉 Traditional apps = static information
👉 I wanted intelligent interaction

💡 Solution

I built a multi-tool AI assistant that can:

  • 🎉 Answer questions about events, workshops, proshows
  • 🍔 Show food stalls & mess menu
  • 🧠 Recommend activities based on user intent
  • 📅 Plan your schedule
  • 🤝 Match you with like-minded participants/Suggest potential teammates (prototype)
  • 📚 Answer fest-related questions using RAG

🧠 System Design

Instead of a simple chatbot, I designed it as a tool-using agent system.

🔹 Tools

  • fetch_events
  • fetch_workshops
  • fetch_food_stall
  • fetch_mess_menu
  • pragyan_bot (RAG-based)
  • smart_recommender
  • planner
  • buddy_matcher

🔹 Agent Flow

  1. User query
  2. LLM decides:
  • Which tool to call
    1. Tool executes
    2. Response is generated in natural language

📚 Retrieval Approach

This system uses a hybrid retrieval strategy at the system level:

  • Structured retrieval (keyword-based)

    • Direct tool calls for events/workshops
    • Fast and deterministic

  • Semantic retrieval (RAG)

    • Vector search over fest documents
    • Handles open-ended queries

👉 This combination allows both precision and flexibility

📚 RAG (Retrieval Augmented Generation)

To handle fest knowledge:

  • Used:

    • Text files (events, shows, lectures, FAQs)

  • Built:

    • FAISS vector store

  • Retrieval:

    • Semantic search on query

  • Response:

    • Context-aware answers

🧠 Memory

Using:

  • InMemorySaver() (LangGraph)

👉 Enables:

  • remembering user preferences
  • better recommendations
  • conversational continuity

🤖 Smart Features

🎯 Recommendations

Understands intent like:

"What should I attend if I like tech and fun?"

📅 Planner Agent

"Plan my next 3 hours"

Generates a structured schedule based on:

  • time
  • interests
  • available events

🤝 Buddy Matching (Prototype)

Matches based on:

  • interests
  • level
  • context (e.g. case study competitions)

Uses a small dataset to demonstrate logic

🖥️ UI

Built with Streamlit:

  • Chat-based interface
  • Quick action buttons
  • Structured responses

🚀 Deployment

Deployed on Render (free tier)
Environment variables for API security

🎥 Demo

👉 https://www.loom.com/share/13f87025a9154a55b80fc240bfc91ba2

🛠️ Tech Stack

  • Python
  • LangChain
  • OpenAI API
  • FAISS
  • Streamlit
  • Render

⚠️ Challenges Faced

  • RAG retrieval quality (chunking + parsing issues)
  • Tool selection accuracy
  • Structuring multi-agent workflow
  • Deployment + API key handling

🔄 Ongoing Improvements

Some features I’m actively working on:

  1. Adding database-backed user profiles for real buddy matching
  2. Improving RAG with better retrieval and evaluation
  3. Expanding dataset coverage for more complete fest information
  4. Exploring true hybrid retrieval + reranking

📈 What I Learned

  • Building agents > building chatbots
  • RAG needs data structuring, not just embeddings
  • UI matters a lot for perceived intelligence
  • Deployment and debugging are part of the real challenge

🔗 Links

  • 🔒 Live demo available on request

💭 Final Thoughts

This project made me realize:

👉 The future isn’t just about LLMs
👉 It’s about systems built around them

If you have suggestions or ideas to improve this, I’d love to hear them!

Top comments (1)

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vibha_bhalerao_861b209f1a profile image
Vibha Bhalerao •

Great use case, as it makes the planning of activities convenient and customised to our interests.
Good work!👍✨️