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Docker Installation Guide for BambooAI

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pgalko edited this page May 2, 2025 · 2 revisions

Cross-platform instructions for Windows, Mac, and Linux

Introduction

BambooAI can be easily deployed using Docker, providing a consistent and isolated environment that simplifies installation across different operating systems. Docker containerization offers several advantages:

  • Simplified Deployment: Run BambooAI without complex setup or dependency management
  • Environment Isolation: Keep BambooAI and its dependencies separate from your system
  • Security: Execute LLM-generated code within a sandboxed container for enhanced safety
  • Consistency: Ensure BambooAI runs the same way across different machines
  • Easy Updates: Quickly update to newer versions without reinstallation

This guide presents two options for deploying BambooAI with Docker, depending on your needs.

Option 1 : Using Published Docker image

Quick setup using the pre-built Docker Hub image. Ideal for users who want a simple installation without building from source. You'll download configuration files, create directories, and run the container with minimal technical steps required. Like all Docker solutions, provides containerized sandboxing of code execution for enhanced security.

Prerequisites:

  1. Create a new directory for BambooAI:

    Mac/Linux:

    mkdir bambooai
cd bambooai

    Windows (PowerShell):

    mkdir bambooai
cd bambooai

  2. Create the necessary subdirectories:

    Mac/Linux:

    mkdir -p web_app/storage/favourites web_app/storage/threads web_app/temp web_app/logs

    Windows (PowerShell):

    mkdir web_app\storage\favourites, web_app\storage\threads, web_app\temp, web_app\logs

  3. Download example configuration files:

    Mac/Linux:

    # Download .env example
curl -o web_app/.env https://raw.githubusercontent.com/pgalko/BambooAI/main/.env.example
# Edit LLM_CONFIG.json with your settings

# Download LLM_CONFIG example
curl -o web_app/LLM_CONFIG.json https://raw.githubusercontent.com/pgalko/BambooAI/main/LLM_CONFIG_sample.json
# Edit LLM_CONFIG.json with your settings

    Windows (PowerShell):

    # Download .env example
Invoke-WebRequest -Uri https://raw.githubusercontent.com/pgalko/BambooAI/main/.env.example -OutFile web_app\.env
# Edit .env with your settings

# Download LLM_CONFIG example
Invoke-WebRequest -Uri https://raw.githubusercontent.com/pgalko/BambooAI/main/LLM_CONFIG_sample.json -OutFile web_app\LLM_CONFIG.json
# Edit .env with your settings

    Edit these files with your settings after downloading.

  4. Create a docker-compose.yml file:

    Mac/Linux:

    touch docker-compose.yml

    Windows (PowerShell):

    New-Item -Path . -Name "docker-compose.yml" -ItemType "file"

    Add the following content to docker-compose.yml:

    services:
  bambooai-webapp:
    image: pgalko/bambooai:latest
    container_name: bambooai
    volumes:
      # Mount configuration files
      - ./web_app/.env:/app/web_app/.env
      - ./web_app/LLM_CONFIG.json:/app/web_app/LLM_CONFIG.json
      
      # Mount persistent storage directories
      - ./web_app/storage:/app/web_app/storage
      - ./web_app/temp:/app/web_app/temp
      - ./web_app/logs:/app/web_app/logs
    
    ports:
      - "5000:5000"
    
    restart: unless-stopped

  5. Run the Docker container:

    docker-compose up -d

Access web interface at http://localhost:5000 Note: The Docker setup preserves all configuration and data between container restarts. If you encounter any issues, check the container logs with docker-compose logs -f.

Option 2 : Build and Use a Custom Docker Image

Create a Docker image using the cloned repository. This approach gives you visibility into the codebase and configuration before building. Clone the repository, configure your environment, and build the image locally. This option maintains the same containerized sandboxing security benefits while allowing you to inspect the code and make any modifications if needed.

Prerequisites:

  1. Clone repository:

    git clone https://github.com/pgalko/BambooAI.git
cd BambooAI

  2. Configure environment:

    Mac/Linux:

    cp .env.example web_app/.env
# Edit .env with your settings

    Windows (PowerShell):

    Copy-Item -Path .env.example -Destination web_app\.env
# Edit .env with your settings

  3. Configure LLM agents, models and parameters:

    Mac/Linux:

    cp LLM_CONFIG_sample.json web_app/LLM_CONFIG.json
# Edit LLM_CONFIG.json with your settings

    Windows (PowerShell):

    Copy-Item -Path LLM_CONFIG_sample.json -Destination web_app\LLM_CONFIG.json
# Edit LLM_CONFIG.json with your settings
    • Edit web_app/LLM_CONFIG.json with your desired combination of agents and models
    • Ensure all necessary API keys are present in your .env file

  4. Build and run the Docker container using the docker-compose file from the repo:

    docker-compose up -d

Access web interface at http://localhost:5000 Note: The Docker setup preserves all configuration and data between container restarts. If you encounter any issues, check the container logs with docker-compose logs -f.

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