🤖 SmartBot

A powerful Streamlit-based application that enables intelligent conversations with your documents using Advanced RAG (Retrieval-Augmented Generation) technology and Google's Gemini AI.

✨ Features

• 📄 Multi-Format Support: PDF, Word (DOCX), PowerPoint (PPTX), HTML, Text/Markdown, and Images
• 🧠 Smart Retrieval: Uses FAISS vector store with sentence transformers for accurate document retrieval
• 💬 Conversational AI: Powered by Google's Gemini Flash for intelligent responses
• 📚 Source References: View exact document snippets used to generate answers
• 🎨 Modern UI: Clean, intuitive interface with custom styling
• 💾 Session Memory: Maintains conversation context throughout your session

🧠 Advanced Features

• 🔍 OCR Capability: Extract text from scanned documents and images using Tesseract
• 🔄 Hybrid Search: Combines semantic search (embeddings) with BM25 keyword matching
• 🎯 Query Decomposition: Breaks complex queries into sub-questions for better accuracy
• 📊 Document Reranking: Uses cross-encoder models to rank retrieved documents by relevance
• 📝 Smart Summaries: Generate brief, detailed, or comprehensive document summaries
• ❓ Follow-up Questions: Auto-generates contextual follow-up questions

🚀 Quick Start

Installation

1. Clone the repository

git clone https://github.com/AjayChikate/SmartBot.git
cd smartbot

2. Install dependencies

pip install -r requirements.txt

3. Set up environment variables

Create a .env file in the project root:

GOOGLE_API_KEY=your_google_api_key_here
TESSERACT_CMD=C:/Program Files/Tesseract-OCR/tesseract.exe
POPPLER_PATH=C:/Program Files/poppler/Library/bin

4. Run the application

streamlit run app.py

🎯 Usage

1. Upload Documents: Use the sidebar to upload one or more documents in supported formats
2. Enable OCR: Toggle OCR if you have scanned documents or images with text
3. Process: Click "Process Documents" to build the knowledge base
4. Chat: Ask questions about your documents in natural language
5. View Sources: Expand the source references to see which document sections were used
6. Follow ups: Click follow-up questions to dive deeper
7. Summary: Generate document summaries (Brief/Detailed/Comprehensive)

🏗️ Project Structure

SmartBot/
│
├── app.py                 # Main Streamlit application
├── rag.py                 # RAG pipeline (chunking, vectorstore, conversation chain)
├── processor.py           # Document processing orchestrator
├── extraction.py          # Text extraction for various file formats
├── ocr.py                 # OCR functionality using Tesseract
├── htmlTemplates.py       # CSS and HTML templates for UI
├── requirements.txt       # Project dependencies
├── .env                   # Environment variables (not in repo)
└── README.md            

📊 Metrics

• Embedding Model: BAAI/bge-small-en-v1.5 (384 dimensions)
• Reranking Model: cross-encoder/ms-marco-MiniLM-L-6-v2
• Vector Store: Chroma (SQLite-backed)
• LLM: Google Gemini Flash

🚀 Performance Tips

1. GPU Acceleration: Enable for faster embeddings

   • Requires CUDA-capable NVIDIA GPU
   • ~3-5x faster embedding generation

2. Chunking Strategy:

   • Smaller chunks (200-300): Better precision
   • Larger chunks (500-600): Better context

3. Retrieval Count:

   • Small documents: k=4-5
   • Large documents: k=8-10

4. Reranking: Disable for <5 retrieved docs

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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