Nexus AI: AI-Powered Document & Multimedia Q&A

Nexus AI is a full-stack application that enables users to upload PDF documents, audio, and video files, and seamlessly ask questions against their content. It leverages state-of-the-art Large Language Models (LLMs) and embeddings to provide highly contextual, time-stamped answers.

📸 Application Showcase

1. Interactive Q&A Workspace

Multi-column verified research workspace featuring RAG question answering, time-stamped citations, document transcript inspection, and an embedded multimedia player.

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2. Verified Intelligence Dashboard

Platform hub featuring quick source uploads, verified statistics, and streamlined access to synthesized document collections.

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3. Profile & Theme Settings

Comprehensive settings panel with customizable user profile, preset avatar selector, strict verification controls, and multi-theme switcher (Light, Dark, and System).

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🚀 Features

• Multi-Format Support: Upload PDF (.pdf), Audio (.mp3, .wav), and Video (.mp4, .mkv).
• Local AI Pipelines:
  • Transcribes audio/video completely locally using OpenAI Whisper.
  • Generates fast document embeddings using HuggingFace MiniLM.
  • Employs FAISS for rapid local vector and semantic search.
• Intelligent Q&A Chatbot: Powered by LangChain and Groq API (Llama 3).
• Time-Stamped Citations: Chatbot highlights exact timestamps from media that you can click to jump straight to that moment in the video/audio player.
• Real-time AI Summaries: Automatically generates summaries of all uploaded content in the background.
• Theme Customization: Seamless switching between Light, Dark, and System themes with persistent local preferences.
• Full Responsiveness: Mobile-optimized layouts with swipeable tab bars, sticky action controls, and responsive split columns.

🏗 Architecture

• Backend: Python 3.11, FastAPI, SQLAlchemy, LangChain, FAISS.
• Frontend: React 18, Vite, Tailwind CSS, Framer Motion.
• Database: SQLite (RDBMS).

🛠 Prerequisites

• Python 3.11+
• Node.js 18+
• Docker & Docker Compose (For containerized deployment)

💻 Local Setup Instructions

1. Backend Setup

1. Open a terminal and navigate to the backend folder:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file inside backend/ and add your Groq API key:

   GROQ_API_KEY=your_groq_api_key_here
   SECRET_KEY=super_secure_secret_string
   DATABASE_URL=sqlite:///./sql_app.db

5. Start the backend server:

   uvicorn app.main:app --reload

2. Frontend Setup

1. Open a new terminal and navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Start the Vite development server:

   npm run dev

4. Open http://localhost:5173 in your browser.

🐳 Running with Docker Compose

You can launch the entire stack via Docker:

docker-compose up --build -d

• The frontend will be available at http://localhost:5173
• The backend API will be available at http://localhost:8000

🧪 Testing

The backend features an automated testing suite using pytest and httpx. To run tests and view coverage:

cd backend
venv\Scripts\activate
pip install pytest-cov httpx
pytest --cov=app --cov-report=term

📖 API Documentation

Once the backend is running, FastAPI automatically generates interactive documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Core API Endpoints

• POST /api/auth/register - Create a new user account
• POST /api/auth/login - Authenticate and receive a JWT token
• POST /api/documents/upload - Upload a PDF, audio, or video file
• GET /api/documents/ - List all uploaded documents and summaries
• DELETE /api/documents/{id} - Delete a document
• GET /api/media/{id} - Stream media files for playback
• POST /api/chat/ - Ask a question to the AI chatbot
• GET /api/chat/history - Retrieve chat history