Great — since Firefox requires HTTPS for microphone access on non-localhost pages, the cleanest solution is to put OpenWebUI behind a local HTTPS reverse proxy.

And the easiest, least painful way to do that — by far — is:

⭐ Use Caddy

Caddy automatically creates and manages certificates, including LAN certificates with no warnings when using mkcert.

You don’t have to touch Nginx configs or OpenWebUI settings.

I’ll give you two working setups depending on how secure and clean you want things.

---

✅ Option A — Easiest possible: Use mkcert + Caddy (recommended)

This gives you real HTTPS that Firefox accepts with no warnings.

Step 1 — Install mkcert

sudo apt install mkcert libnss3-tools -y
mkcert -install

Step 2 — Install Caddy

sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https
curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | sudo tee /usr/share/keyrings/caddy-stable-archive-keyring.gpg >/dev/null
curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | sudo tee /etc/apt/sources.list.d/caddy-stable.list
sudo apt update
sudo apt install caddy

Step 3 — Create a domain for local use

Pick something like:

ougway.local

Add it to your hosts file:

sudo nano /etc/hosts

Add this line:

192.168.x.x   ougway.local

(Replace with your PC’s actual LAN IP from hostname -I.)

Save.

Step 4 — Create TLS cert for that domain

mkcert ougway.local

That produces:

• ougway.local.pem
• ougway.local-key.pem

Move them to Caddy’s cert directory:

sudo mv ougway.local.pem /etc/ssl/caddy/
sudo mv ougway.local-key.pem /etc/ssl/caddy/

Step 5 — Create a Caddyfile

sudo nano /etc/caddy/Caddyfile

Put this inside:

ougway.local {
    tls /etc/ssl/caddy/ougway.local.pem /etc/ssl/caddy/ougway.local-key.pem
    reverse_proxy http://127.0.0.1:8080
}

Save.

Step 6 — Restart Caddy

sudo systemctl restart caddy

Step 7 — Access OpenWebUI in Firefox via:

👉 https://ougway.local

Now microphone access will work.

Firefox sees it as a fully trusted, secure HTTPS site.

---

🔥 Option B — Even faster (but Firefox will show a warning)

If you don't care about warnings, Caddy can generate its own cert automatically:

Simple Caddyfile:

https://ougway.local {
    reverse_proxy http://127.0.0.1:8080
}

Firefox will complain because the cert isn't trusted unless you use mkcert — so Option A is the clean way.

---

🎤 Once HTTPS is active

Firefox will now allow:

• microphone
• camera
• screen sharing
• WebRTC models
• voice chat in OpenWebUI

All over LAN, no exceptions needed.

---

If you want, I can automatically generate:

• the exact Caddyfile
• the mkcert commands
• the hosts entry
• and the right domain name

Just tell me what LAN IP ougway is using:

👉 Run: hostname -I

Paste the result, and I’ll finish the config for you in one shot.

Ah — I know exactly what you’re talking about.
You didn’t hear “1.1.1.1 programmer interface.”
You heard:

⭐ “Automatic 1111”

(spelled: AUTOMATIC1111 or A1111)
This is the most popular Stable Diffusion web UI on the planet.

NOTHING to do with Cloudflare 1.1.1.1.
NOTHING to do with networking.
And definitely NOTHING to do with certificates or HTTPS.

You’re totally fine — no SSL setup required.

---

✅ What AUTOMATIC1111 actually is

AUTOMATIC1111 = a web-based interface for running Stable Diffusion on Linux, Windows, or Mac.

It gives you:

• text-to-image
• image-to-image
• inpainting
• controlnet
• LoRA support
• model downloading
• all UI interactions in a browser

It is separate from OpenWebUI (your LLM interface), but you can integrate the two later.

---

🎯 Why people recommend AUTOMATIC1111

Because:

• it’s the easiest and most feature-rich SD interface
• it supports thousands of models
• it works extremely well on Linux
• huge community support
• all tutorials assume it

It’s basically the “LM Studio” of the Stable Diffusion world.

---

🔧 How you install AUTOMATIC1111 on Ubuntu

I’ll give you the simple version:

1. Install dependencies:

sudo apt update
sudo apt install python3 python3-venv python3-pip git -y

2. Clone the repo:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

3. (Optional) Enable your NVIDIA GPU

Make sure your NVIDIA driver is installed:

nvidia-smi

If you see your GPU, you're good.

4. Start the web UI:

./webui.sh

This will:

• download Stable Diffusion 1.5 (default)
• set up venv and dependencies
• launch the interface

5. Open in browser:

👉 http://localhost:7860

That's it.
You now have a full Stable Diffusion lab.

---

⚠️ Where OpenWebUI fits into this

OpenWebUI is your LLM web interface, not your diffusion interface.

However — they can work together:

• OpenWebUI can call AUTOMATIC1111 through an API endpoint
• You can generate images by prompt inside your OpenWebUI chats
• The two tools can be linked via "Tools" / "Extensions"
• This is optional and can be set up later

Right now, you don’t need any integration to simply run Stable Diffusion.

---

⭐ So to confirm your exact question:

> “What is the automatic 1.1.1.1 programmer interface he’s mentioning?”

He is referring to:

👉 AUTOMATIC1111 Stable Diffusion Web UI

Not DNS.
Not certificates.
Not 1.1.1.1.
Not networking.
Not mkcert.
Not Caddy.

It is simply the #1 Stable Diffusion interface.

---

If you want, I can help you:

• Install AUTOMATIC1111
• Install ComfyUI (the more advanced node-based SD interface)
• Integrate OpenWebUI ↔ AUTOMATIC1111 so your LLM can generate images
• Install new SDXL / SD3 / Juggernaut models
• Add ControlNet
• Set up LoRA training or DreamBooth

Just tell me what direction you want next.

Do you want:

✔ Automatic1111 (easiest, best for now),

or

✔ ComfyUI (more powerful, node-based, future standard)?

Either works with your system.

Yeah, RAG is absolutely something you can do with OpenWebUI, and yes: you can make that “extended database” available to any model you hook up (Ollama, future Qwen3, GPT via API, etc.). It’s mostly model-agnostic.

Let’s keep it high-level now so it’s easy to plug in later when you’re fresh.

---

1. What “RAG” actually is in this setup

In OpenWebUI terms, RAG is basically:

> Base model (llama / qwen / GPT / whatever)
> +
> Document index (your PDFs, notes, text, etc., embedded & searchable)
> +
> Glue logic that:
> – turns your question into a semantic search,
> – pulls the top N relevant chunks,
> – stuffs them into the prompt as context,
> – then calls the model.

So: the “database” is really an indexed knowledge base, and openwebUI just feeds that into whichever model you picked.

---

2. Big picture of how it works in OpenWebUI

The flow looks like:

1. You create a Knowledge Base (KB) and upload stuff:
   – PDFs (your Flower Lattice primer, Spiral Accord, etc.)
   – Markdown, text exports, maybe your AI logs.
2. OpenWebUI:
   – splits docs into chunks,
   – embeds them (vector embeddings),
   – stores in its internal DB (often sqlite + a vec index).
3. You then create a model profile that says:
   – “Use backend X (e.g. llama2:latest or qwen2.5:7b or future Qwen3)”
   – “Use knowledge base Y as context source.”
4. When you chat with that profile:
   – It looks up relevant bits from the KB
   – Sends those + your question into the model.

Same mechanism works no matter which model is at the back end.

---

3. How to hook any model to a KB (conceptually)

Once you’re back at it, the steps will look roughly like this (names might differ slightly per version, but structure is the same):

A. Create a Knowledge Base

1. In the OpenWebUI sidebar, look for something like:
   – Workspace → Knowledge or
   – Documents / Knowledge Base / Collections.
2. Click New / +.
3. Name it something like:
   – Flower_Lattice_Core
4. Upload files:
   – Your PDFs, markdown exports, etc.
5. Let it finish indexing (it’ll chew on them once).

Now you have a KB.

---

B. Bind a model profile to that KB

1. Go to Models / Profiles / Custom Models in the UI.
2. Create a new profile:
   – Name: Qwen-FieldLab or Llama2-SpiralKB
   – Backend: choose Ollama and then a specific model:
   • e.g. llama2:latest, later qwen3:8b, etc.
3. Somewhere in that profile setup there’ll be an option like:
   – Knowledge / Context Source / RAG Source
   – Select your KB: Flower_Lattice_Core.
4. Save.

Now that profile = model + KB.

Start a new chat using that profile, and it will automatically RAG your questions against the KB, no matter which client (browser, LAN machine) you use.

---

4. Can any model use the same KB?

Yes.

You can:

• Use one KB across many profiles, each with a different backend:
• Llama2-FieldLab (Ollama → llama2)
• Qwen3-FieldLab (Ollama → qwen3, when ready)
• Echo-GPT-FieldLab (OpenAI GPT via API, if/when you wire that)
• Or create multiple KBs (e.g. “Math”, “Field Notes”, “Temple Logs”) and mix & match them per profile.

The KB doesn’t care which model is reading it; it just serves chunks.

---

5. Where this fits in your ecosystem

For your world:

• Tokenspace / Postgres / Ougway is your serious long-term, hand-crafted memory system.
• OpenWebUI RAG can be the “quick & dirty” layer:
• throw in PDFs, compiled logs, experiment notes,
• query them with local Ollama models or remote GPT,
• treat it as a scratchpad knowledge index.

Later, if you want, we can:

• mirror that RAG index into Postgres/pgvector,
• or have Ougway ingest the same sources directly.

But for now, OpenWebUI’s built-in KB is enough to get something useful running without custom code.

---

6. When you’re ready to implement it

When you circle back (after SSH / A1111 / model downloads), we can do a concrete pass like:

• “Let’s make a KB from Flower Lattice Scientific Primer (PDF)”
• “Let’s wire it to llama3.1:8b”
• “Let’s test with a few specific questions and see if it cites the right sections.”

One session, we can go from zero to “LLM can quote your own docs back at you” with this stack.

For now, you’re in a good place:
Docker sorted, OpenWebUI up, Ollama talking, LAN access working, and you know that yes, RAG can sit in front of any of these models when you want it.