DoogieBot

This project is a chatbot application built with Next.js (frontend) and FastAPI (backend). It allows users to interact with documents and a Large Language Model (LLM). The project initially focused on addressing markdown formatting issues in chatbot responses but has evolved into a full-fledged application.

Project Overview

This application provides a user interface for:

• Managing documents (uploading, processing, and retrieving)
• Configuring LLM settings
• Interacting with an LLM through a chat interface
• Managing user accounts

Technology Stack

• Frontend: Next.js, React, Tailwind CSS
• Backend: FastAPI, Python
• Database: (Inferred from migrations - likely PostgreSQL or similar)
• LLM Integration: Supports multiple LLMs (OpenAI, Anthropic, Ollama, etc.)
• Vector Database: (Inferred from code - likely FAISS)
• Deployment: Docker, Docker Compose

Production Setup (Docker Compose)

The production setup uses Docker Compose to run the application in a containerized environment.

docker-compose.prod.yml Configuration:

• Defines a single service named app.
• Builds the application from the root Dockerfile.
• Exposes ports 3000 (frontend) and 8000 (backend).
• Uses bind mounts for the application code:
  • ./:/app: Mounts the entire project directory.
  • Excludes build artifacts and dependencies: /app/frontend/.next, /app/frontend/node_modules, /app/backend/__pycache__.
• Sets environment variables:
  • NODE_ENV=production
  • PYTHONPATH=/app
  • FASTAPI_ENV=production
  • Database connection settings (if applicable)
  • LLM service API keys (OpenAI, Anthropic, etc.)
  • Secret key and other security-related settings.
• Uses /app/entrypoint.prod.sh as the entrypoint.
• Restarts the service unless stopped (restart: unless-stopped).

entrypoint.prod.sh Script:

• Installs backend and frontend dependencies.
• Runs database migrations using Alembic.
• Builds the frontend for production (npm run build).
• Starts the backend server using Uvicorn:
  • uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4 --timeout-keep-alive 300
• Starts the frontend server using npm run start.
• Handles shutdown signals (SIGTERM, SIGINT) to gracefully stop the services.

Running in Production:

1. Set the necessary environment variables in a .env file or directly in your shell. You'll need to provide API keys for the LLM services you want to use and a strong SECRET_KEY.
2. Run docker compose -f docker-compose.prod.yml up --build to build and start the application.

Development Setup

1. Clone the repository: git clone <repository_url>
2. Navigate to the project directory: cd doogie6
3. Install backend dependencies:

   cd backend
   pip install -r requirements.txt

4. Install frontend dependencies:

   cd ../frontend
   npm install

5. Run database migrations:

   cd ../backend
   python -m alembic upgrade head

6. Start the development servers:
   • You can use the docker-compose.yml file for a combined development environment. This will automatically rebuild and reload on code changes.
   • Run docker compose up --build

Memory Bank

This project utilizes a memory bank system to maintain context and documentation across sessions. The memory bank consists of Markdown files located in the memory-bank/ directory. These files contain information about the project's goals, architecture, context, and progress. Key files include:

• projectbrief.md: Defines the core requirements and goals of the project.
• productContext.md: Explains the purpose and functionality of the application.
• activeContext.md: Tracks the current focus, recent changes, and next steps.
• systemPatterns.md: Describes the system architecture and design patterns.
• techContext.md: Lists the technologies used and development setup.
• progress.md: Summarizes the current status and known issues.