metadata

title: RAGtim Bot - Raktim's AI Assistant
emoji: 🤖
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.34.0
app_file: app.py
pinned: false
license: mit

🤖 RAGtim Bot - Raktim's AI Assistant

An intelligent AI assistant powered by Hugging Face Transformers that answers questions about Raktim Mondol's research, expertise, and professional background.

🌟 Features

Complete Markdown Knowledge Base: Loads all portfolio content from markdown files
GPU-Accelerated Search: Uses sentence-transformers/all-MiniLM-L6-v2 for semantic similarity
Comprehensive Coverage: Research, publications, skills, experience, education, statistics
API Endpoints: Direct access to search and statistics
Real-time Chat: Interactive conversational interface

📚 Knowledge Base

This Space loads comprehensive information from:

about.md - Personal information, contact details, professional summary
research_details.md - Detailed research projects, methodologies, current work
publications_detailed.md - Complete publication details, technical contributions
skills_expertise.md - Comprehensive technical skills, tools, frameworks
experience_detailed.md - Professional experience, teaching, research roles
statistics.md - Statistical methods, biostatistics expertise, methodologies

🔍 What You Can Ask

Research projects and methodologies
Publications with technical details
Technical skills and programming expertise
Educational background and achievements
Professional experience and teaching roles
Statistical methods and biostatistics applications
Awards, recognition, and professional development
Contact information and collaboration opportunities

🚀 API Usage

Search API

import requests

response = requests.post(
    "https://raktimhugging-ragtim-bot.hf.space/api/search",
    json={"query": "What is Raktim's research about?", "top_k": 5}
)
results = response.json()

Stats API

response = requests.get("https://raktimhugging-ragtim-bot.hf.space/api/stats")
stats = response.json()

🔧 Technical Details

Model: sentence-transformers/all-MiniLM-L6-v2
Embedding Dimension: 384
Search Type: Semantic similarity with relevance scoring
Knowledge Sections: 50+ sections across 6 markdown files
GPU Acceleration: Automatic CUDA detection and usage

🌐 Integration

This Space can be integrated with:

Portfolio websites for intelligent chat assistance
Research collaboration platforms
Academic networking tools
Professional inquiry systems

📞 Contact

For questions about Raktim Mondol or collaboration opportunities:

Email: [email protected]
Portfolio: mondol.me
Institution: UNSW Sydney, School of Computer Science & Engineering

Built with: Gradio, Hugging Face Transformers, PyTorch Powered by: GPU-accelerated semantic search and comprehensive markdown knowledge base