🚀 AI Research Assistant

AI Research Assistant is a next-gen platform for immersive, AI-driven document analysis. Upload PDFs of research papers or reports, ask complex questions, and get precise, source-backed answers—all within a sleek, 3D glassmorphic UI.

🌟 Key Features

Feature	Description
🖼️ 3D Immersive UI	Glassmorphism cards, floating panels, and parallax effects for a modern UX
📄 PDF Intelligence	Chunk, embed, and index PDFs for lightning-fast retrieval
🤖 Gemini AI Powered	Google Gemini via google-generativeai for top-tier RAG responses
❓ Smart Q&A	Multi-turn conversation, source citations, and context retention
📊 Performance Metrics	Live dashboard for ingestion time, latency, token usage, and confidence scores
🔄 Session History	Persistent chat logs and easy session resets
📱 Responsive Design	Fully functional on desktop and mobile

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🛠️ Tech Stack

backend:
  framework: FastAPI + Uvicorn
  language: Python 3.12+
  vector_db: ChromaDB
  embeddings: HuggingFace
  ai_sdk: google-generativeai (Gemini)
  orchestration: LangChain
frontend:
  framework: Streamlit
  styling: CSS (Glassmorphism, 3D transforms)
infrastructure:
  container: Docker-ready
  deployment: Vercel (frontend) + any cloud VM (backend)

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🚀 Quick Start

1. Clone & Setup

git clone https://github.com/your-username/AI-Research-Assistant.git
cd AI-Research-Assistant
python -m venv venv
source venv/bin/activate        # macOS/Linux
venv\\Scripts\\activate       # Windows
pip install --upgrade pip
pip install -r requirements.txt

2. Configure API Key

1. Copy .env.example to .env:

   cp .env.example .env

2. Add your Google Gemini API key:

   GEMINI_API_KEY=your_api_key_here

3. Run Locally

• Backend: uvicorn app:app --reload --port 8000
• Frontend: streamlit run streamlit_app.py --server.port 8501

Now browse http://localhost:8501 and start analyzing documents!

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🖥️ Usage Overview

1. Upload PDF: Click "Choose a PDF" and select your file.
2. Process: Hit "Process Document" to chunk and embed.
3. Ask Gemini: Type your question and click "Ask Gemini".
4. Review: View answers with inline citations, confidence, and performance stats.
5. History & Reset: Browse past Q&A or click "Clear Document" to start anew.

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📈 Metrics Dashboard

• Ingest Time: ~5s for 20-page PDF
• Query Latency: <2s per request on free-tier
• Token Efficiency: Optimized chunking to reduce cost

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🤝 Contributing

1. 🌱 Fork the repository
2. 🛠️ Create a branch: git checkout -b feature/my-new-feature
3. 📝 Commit: git commit -m "Add awesome feature"
4. 📤 Push: git push origin feature/my-new-feature
5. 🔀 Open PR and describe your changes

Contributions should include tests and adhere to PEP8.

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📜 License

This project is licensed under the MIT License. See LICENSE for details.

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Built with ❤️ by the AI Research Community