# Ollama — Local AI Reference

Ollama runs on Mike's workstation (DESKTOP-0O8A1RL) with GPU acceleration. Available to all team members via Tailscale.

## Models

| Model | Size | Use For |
|-------|------|---------|
| `qwen3:14b` | 9.3 GB | Summarization, classification, data extraction, drafting |
| `codestral:22b` | 12 GB | Code generation, refactoring suggestions, docstrings |
| `nomic-embed-text` | 274 MB | Embeddings only (used by GrepAI) |

## Endpoints

Auto-detect: any machine that has a local Ollama listening on `127.0.0.1:11434` uses local. Otherwise fall back to Mike's workstation over Tailscale.

```bash
# Preferred universal resolver — works on any machine
if curl -s -m 2 http://localhost:11434/api/tags >/dev/null 2>&1; then
    OLLAMA="http://localhost:11434"
else
    OLLAMA="http://100.92.127.64:11434"
fi
```

Rationale:
- **Mike's workstation (DESKTOP-0O8A1RL):** local matches, no change.
- **HOWARD-HOME:** also has a local Ollama with the canonical model set (confirmed 2026-04-22). Uses local — faster, zero Tailscale hop, no load on Mike's GPU.
- **Other team machines:** no local Ollama → falls back to Mike's over Tailscale.
- **Mike's machine offline:** graceful degradation — local users continue working; non-local users get a clean timeout.

Manual override (for testing or explicit preference): set `OLLAMA=http://100.92.127.64:11434` before the call.

Check reachability:
```bash
curl -s $OLLAMA/api/tags | jq -r '.models[].name'
```

If neither endpoint responds: verify Tailscale (`tailscale status`) and whether your local Ollama service is running.

## Access Control

- Port 11434 allowed ONLY from Tailscale subnet (100.0.0.0/8)
- NOT exposed to LAN, VPN, or internet
- Binding: `OLLAMA_HOST=0.0.0.0:11434` (firewall restricts)

## Calling Ollama

Use the `/api/chat` endpoint with `think:false` for qwen3 models. The older `/api/generate` endpoint on qwen3 puts output into thinking tokens that don't appear in the `response` field — you'll get an empty response if you use `/api/generate`.

Preferred one-liner:
```bash
python -c "
import urllib.request, json, sys, os
OLLAMA = os.environ.get('OLLAMA') or ('http://localhost:11434' if __import__('urllib.request').request.urlopen(urllib.request.Request('http://localhost:11434/api/tags'),timeout=2) else 'http://100.92.127.64:11434')
body = json.dumps({
  'model':'qwen3:14b',
  'messages':[{'role':'user','content': sys.argv[1]}],
  'stream':False,
  'think':False
}).encode()
res = json.loads(urllib.request.urlopen(urllib.request.Request(OLLAMA+'/api/chat', body), timeout=120).read())
print(res['message']['content'])
" "Your prompt here"
```

Or set `$OLLAMA` once from bash (see auto-detect formula above) and reuse it across calls.

For code suggestions, swap `qwen3:14b` for `codestral:22b`. Codestral doesn't need `think:false`.

Cold-start is ~30-50s on first call per model per session. Warm calls are 1-5s.

## When to Use Which Model

| Task | Model |
|------|-------|
| Summarize logs, diffs, session notes | qwen3:14b |
| Classify bug type, severity, category | qwen3:14b |
| Extract structured data from text | qwen3:14b |
| Draft commit message from diff | qwen3:14b |
| Suggest refactor for a function | codestral:22b |
| Docstring / comment generation | codestral:22b |

## Review Policy

- Low-stakes output (summary, classification, draft) — use directly
- Code suggestions from codestral — always review before applying
- Never use Ollama for: auth decisions, credential handling, production migrations, security review
