DocuMind

Designed to solve the privacy risks of uploading sensitive financial/legal documents to cloud LLMs like GPT-4. DocuMind is an advanced, privacy-focused AI assistant that allows users to chat with their documents and the web. Built with a "local-first" philosophy, it leverages Ollama to run powerful LLMs directly on your machine, ensuring your data never leaves your control.

Whether you need to summarize a research paper, extract insights from a financial report, or query a GitHub repository, DocuMind provides a seamless, chat-based interface to interact with your content.

Features

• 📄 Chat with PDFs: Upload PDF documents and ask questions. The system uses RAG (Retrieval-Augmented Generation) to provide accurate answers with source citations.
• 🌍 Universal Ingestion: Paste URLs from Wikipedia, GitHub, or any other website. DocuMind scrapes and processes the content, making it instantly queryable.
• 🎧 Audio Summaries: Generate podcast-style audio summaries of your documents to listen on the go.
• 🔒 Local & Private: Powered by Ollama, all processing happens locally. No API keys required, no data leakage.
• 🧠 Model Agnostic: Switch between different LLMs (Llama 3, Mistral, Gemma) on the fly directly from the chat interface.
• ⚡ Real-time Streaming: Enjoy a smooth, typewriter-style chat experience with low latency.
• 🎨 Modern UI: A clean, responsive interface built with Next.js and Shadcn UI, featuring dark mode support and syntax highlighting for code.

Technology Stack

Frontend

• Framework: Next.js 16 (App Router)
• Styling: Tailwind CSS
• Components: Shadcn UI
• Icons: Lucide React
• Markdown: react-markdown with react-syntax-highlighter

Backend

• API: FastAPI
• Vector Store: ChromaDB
• LLM Orchestration: LangChain
• PDF Processing: PyMuPDF (fitz)
• Web Scraping: beautifulsoup4 & WebBaseLoader
• Audio Generation: gTTS (Google Text-to-Speech)

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v18 or higher)
• Python (v3.10 or higher)
• Ollama: Download and install from ollama.com.
  • Pull a model: ollama pull llama3 (or your preferred model).

Installation

1. Clone the Repository

git clone https://github.com/iamdanwi/pdf-assitant.git
cd documind

2. Backend Setup

Navigate to the server directory and set up the Python environment.

cd server
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Note: If requirements.txt is missing, install the core dependencies:

pip install fastapi uvicorn[standard] langchain-ollama langchain-community chromadb pymupdf python-multipart httpx gTTS beautifulsoup4

3. Frontend Setup

Navigate to the client directory and install dependencies.

cd ../client
npm install

Usage

Running the Application

1. Start the Backend Server:

   # In /server directory
   source .venv/bin/activate
   uvicorn app.main:app --reload

   The API will be available at http://localhost:8000.

2. Start the Frontend Client:

   # In /client directory
   npm run dev

   The application will be available at http://localhost:3000.

How to Use

1. Select a Model: Use the dropdown in the chat input to select your installed Ollama model.
2. Add Content:
   • Upload PDF: Click the paperclip icon to upload a document.
   • Add URL: Click "Add from URL" to paste a link (e.g., a Wikipedia article).
3. Chat: Type your questions. The AI will answer based on the uploaded context.
4. Listen: Click "Generate Audio" to hear a summary of the active document.
5. New Chat: Click the + icon in the sidebar to clear the context and start fresh.

Project Structure

documind/
├── client/                 # Next.js Frontend
│   ├── app/
│   │   ├── components/     # React Components (ChatInterface, MessageBubble)
│   │   ├── hooks/          # Custom Hooks (useChat)
│   │   └── lib/            # Utilities
│   └── public/
├── server/                 # FastAPI Backend
│   ├── app/
│   │   ├── api/            # API Routes (chat, ingest, audio)
│   │   ├── core/           # Configuration
│   │   └── services/       # Business Logic (Vector Store)
│   └── static/             # Generated audio files
└── README.md

Contributing

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

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.