---
title: ML Research Paper RAG Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 8501
tags:
- streamlit
- machine-learning
- research
- rag
- chatbot
pinned: false
short_description: AI-powered chatbot for ML research papers
---

# 📄 ML Research Paper RAG Chatbot

An intelligent research assistant that helps you discover, understand, and explore Machine Learning research papers from ArXiv using Retrieval-Augmented Generation (RAG).

## 🎯 What is this?

This chatbot uses advanced AI to help you:
- 🔍 **Find relevant research papers** on any ML topic
- 📚 **Get detailed explanations** from published research
- 💡 **Understand complex concepts** with cited sources
- 🎓 **Stay updated** with ML research trends

## ✨ Features

- **Multi-LLM Support**: Choose between Anthropic Claude, Google Gemini, or Groq
- **Smart Retrieval**: FAISS vector store with semantic search and reranking
- **Research-Focused**: Only provides answers based on actual papers (no hallucinations)
- **Citation-Backed**: All responses cite source papers with metadata
- **Interactive UI**: Clean Streamlit interface with helpful guides

## 🚀 Quick Start Guide

### For First-Time Users

1. **Start a conversation** by typing a question in the chat box
2. **Try example queries** using the quick action buttons
3. **Explore results** by expanding the "View Retrieved Documents" section
4. **Adjust settings** in the sidebar for fine-tuned results

### Example Queries

```
✅ Find papers on handling imbalanced datasets
✅ What methods are used for fraud detection in ML?
✅ Explain the attention mechanism in transformers
✅ List recent papers about reinforcement learning
✅ How does batch normalization improve training?
```

## 💡 Tips for Best Results

### Ask Better Questions
- ✅ **Be specific**: "fraud detection in credit cards" > "fraud"
- ✅ **Use ML terminology**: "convolutional neural networks" > "image AI"
- ✅ **Ask for comparisons**: "Compare CNN vs RNN for sequences"

### Understand the Responses
- 📚 All answers are based on research papers in the database
- 🔍 Check "View Retrieved Documents" to see sources
- ⚠️ If documents seem irrelevant, try rephrasing

### Advanced Usage
- ⚙️ Adjust retrieval settings (base_k, rerank_k) for more/fewer papers
- 🎨 Switch LLM providers for different response styles
- 📅 Filter by year or category for focused results

## 🗂️ Dataset

Uses **CShorten/ML-ArXiv-Papers** from Hugging Face:
- Curated Machine Learning research papers from ArXiv
- Includes titles, abstracts, metadata, and citations
- Regularly updated with new publications

## ⚙️ Configuration

### LLM Providers
1. **Anthropic Claude** (Recommended for quality)
   - claude-3-5-sonnet-20241022 (Best balance)
   - claude-3-5-haiku-20241022 (Fast)
   
2. **Google Gemini** (Good for free tier)
   - gemini-2.5-flash (Fast and efficient)
   
3. **Groq** (Fastest inference)
   - llama-4-maverick-17b (Open source)

### Retrieval Settings
- **base_k**: Initial papers fetched (4-30, default: 20)
- **rerank_k**: Final papers after reranking (1-12, default: 8)
- **Dynamic k**: Auto-adjust based on query
- **Reranking**: Improve relevance with cross-encoder

## 🔧 Setup (For Developers)

### Prerequisites
```bash
pip install -r requirements.txt
```

### API Keys
Create a `.env` file:
```env
ANTHROPIC_API_KEY=your-key-here
GEMINI_API_KEY=your-key-here
GROQ_API_KEY=your-key-here
```

### Run Locally
```bash
streamlit run streamlit_app.py
```

## 📊 How It Works

1. **User Query** → Semantic embedding created
2. **Vector Search** → FAISS retrieves similar papers
3. **Reranking** → Cross-encoder scores relevance
4. **LLM Generation** → AI generates answer from papers
5. **Response** → Cited answer with source papers

## 🔒 Important Notes

- ✅ Answers are **based only on research papers** in the database
- ✅ System won't make up information from general knowledge
- ✅ If no relevant papers found, it will tell you
- ❌ Not a replacement for reading the full papers
- ⚠️ Always verify critical information with original sources

## 🤝 Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements.

## 📄 License

This project is open source and available under the MIT License.

---

**Ready to explore ML research?** Start by asking a question! 🚀