Reducing installation redundancy/conflicts

General
1

Hi everyone

[I’ve put this in General because it references multiple topics - sorry if this is the wrong place]

I’m not the most experienced of coders, and I’ve found that as I’ve gone through installing the Local AI Package, Archon and so on, I’ve found that there’s a fair bit of duplication in the programs installed and so on. If I’ve tried running both of them at the same time then I’ve found that I get port conflicts, etc.

I also was looking into being able to have a centralised local machine running everything, and being able to connect into it from my other computers and devices (rather than having a cloud solution)

I’ve been looking into Tailscale to connect all my machines, and I’ve noticed I can also give separate addresses to docker containers. So my idea is this:

  • Just have one container per app
  • give each container it’s own Tailscale address
  • set the hosts variables in Archon and the Local AI Package to reference these addresses

Does this sound like a workable solution? That way if I wanted to have multiple local supabase projects I could give each one it’s own IP, or If I wanted to set up my own MCP servers for internal use then it would make everything a lot easier.

Perplexity seems to think this might be a good idea, but I thought I’d run it past you all!

Perplexity’s response:

Your approach to consolidating the containers from both projects (Local AI Packaged and Archon) and using Tailscale to assign independent IP addresses to each container is practical and efficient. Here’s an analysis of your plan and suggestions for implementation:

Analysis of Your Plan

  1. Consolidation of Containers:

    • Both projects use Docker as their deployment method, making it feasible to merge overlapping components into a single container per package. This reduces redundancy and avoids resource waste.
    • For example, services like Supabase, Ollama, and Open WebUI in Local AI Packaged could be streamlined with similar components in Archon, particularly if Archon uses Supabase for its vector database or similar LLMs.

  2. Tailscale Integration:

    • Tailscale’s ability to assign independent IP addresses to Docker containers is ideal for resolving port conflicts and enabling seamless communication between containers across devices. This approach ensures that your projects remain portable and accessible from any machine in your network.

  3. Centralized Endpoint Management:

    • By assigning Tailscale IPs to each container, you can configure your projects to reference these IPs for endpoints, ports, and services. This eliminates hardcoded localhost or machine-specific configurations.

Implementation Steps

Step 1: Consolidate Containers

  • Review the Docker Compose files of both projects (Local AI Packaged[2] and Archon[3]) to identify overlapping services.
  • Combine services into a unified Docker Compose file:
    • Ensure that each service has unique names and ports.
    • Use environment variables to manage configurations dynamically.

Step 2: Configure Tailscale for Docker

  • Pull the Tailscale Docker image:
 docker pull tailscale/tailscale:latest
  • Add Tailscale as a service in your Docker Compose file:
    version: "3.7"
services:
  tailscale-service:
    image: tailscale/tailscale:latest
    environment:
      - TS_AUTHKEY=<your-auth-key>
    volumes:
      - ./tailscale/state:/var/lib/tailscale
    cap_add:
      - NET_ADMIN
    devices:
      - /dev/net/tun:/dev/net/tun
    restart: unless-stopped
  app-service:
    image: <your-app-image>
    network_mode: service:tailscale-service
  • Assign unique tags or routes for each container as needed using TS_EXTRA_ARGS[4].

Step 3: Update Project Configurations

  • Modify endpoint configurations in both projects to reference Tailscale IPs instead of localhost or machine-specific addresses.
  • Example for Local AI Packaged:
    Update .env variables like OLLAMA_HOST or SUPABASE_HOSTNAME with the respective Tailscale IPs[2].
  • Example for Archon:
    Configure workbench/env_vars.json or Streamlit settings with Tailscale IPs for database and service endpoints[3].

Step 4: Test Communication Across Devices

  • Deploy the unified setup on one machine.
  • Use Tailscale to verify that containers can communicate across devices using assigned IPs.
  • Test workflows in both projects to ensure seamless integration.

Advantages of Your Approach

  1. Resource Optimization:
    Consolidating containers reduces duplication, saving system resources.

  2. Scalability:
    With Tailscale IPs, you can scale your setup across multiple devices without reconfiguring endpoints manually.

  3. Port Conflict Resolution:
    Independent IP addresses eliminate port conflicts between concurrent instances.

  4. Flexibility:
    You can run components of either project on any device while maintaining connectivity.

Potential Challenges

  1. Configuration Complexity:
    Merging Docker Compose files and managing environment variables could be intricate but manageable with proper documentation.

  2. Network Performance:
    Using Tailscale adds an abstraction layer that might slightly impact latency, though typically negligible.

  3. Dependency Management:
    Ensure that all dependencies (e.g., Python libraries, Supabase schemas) align between the two projects during consolidation.

Conclusion

Your plan is well-grounded and aligns with best practices for containerized deployments across distributed systems. By leveraging Tailscale’s capabilities alongside Docker, you can create a robust, scalable infrastructure for your AI projects.

Sources
[1] Actions · coleam00/local-ai-packaged GitHub - coleam00/local-ai-packaged: Run all your local AI together in one package - Ollama, Supabase, n8n, Open WebUI, and more!
[2] local-ai-packaged/README.md at main · coleam00/local-ai-packaged local-ai-packaged/README.md at main · coleam00/local-ai-packaged · GitHub
[3] GitHub - coleam00/Archon: Archon is an AI agent that is able to create other AI agents using an advanced agentic coding workflow and framework knowledge base to unlock a new frontier of automated agents. GitHub - coleam00/Archon: Archon is an AI agent that is able to create other AI agents using an advanced agentic coding workflow and framework knowledge base to unlock a new frontier of automated agents.
[4] Using Tailscale with Docker · Tailscale Docs Using Tailscale with Docker · Tailscale Docs

1 Like
2

I haven’t used Tailscale myself (though it is on my “list”), but this sounds like a good plan to me!