Document Chat Application

This application allows users to upload documents and chat with AI models about the content of those documents. The application uses semantic search to find relevant content in documents to provide accurate responses.

Features

• Document upload and processing (PDF, TXT)
• Chat with AI about document contents
• Semantic search for accurate document retrieval
• Model selection with adjustable parameters
• Code highlighting and markdown support

Project Structure

The application consists of:

• Backend: Flask API that processes documents and connects to LLM services
• Frontend: React application that provides the user interface
• LLM Service: Integration with Ollama for language model capabilities

Running with Docker (Recommended)

Prerequisites

• Docker
• Docker Compose (usually included with Docker Desktop)

Steps to Run

1. Clone the repository

   git clone <repository-url>
   cd arun-ai-lab

2. Build and start the containers

   docker-compose up -d

   Note: Required Ollama models (text-embedding-bge-m3, deepseek-r1-distill-qwen-32b-mlx) will be automatically downloaded on first startup. This may take some time depending on your internet connection.

3. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5001/api

Managing the Docker Application

• Stop the application

  docker-compose down

• View logs

  docker-compose logs
  docker-compose logs -f  # Follow logs in real-time

• Rebuild after changes

  docker-compose build
  docker-compose up -d

For more detailed Docker instructions, see DOCKER_README.md.

Running Locally (Alternative)

Backend Setup

1. Navigate to the backend directory

   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. Run the server

   python app.py

   The backend will be available at http://localhost:5001

Frontend Setup

1. Navigate to the frontend directory

   cd frontend

2. Install dependencies

   npm install

3. Start the development server

   npm start

   The frontend will be available at http://localhost:3000

LLM Service Setup

This application requires an Ollama service running locally:

1. Install Ollama Follow instructions at Ollama.ai

2. Pull required models

   ollama pull text-embedding-bge-m3
   ollama pull deepseek-r1-distill-qwen-32b-mlx

3. Start Ollama The service should be available at http://localhost:1234

License

MIT License