feat: home-gateway 초기 구성 — Mac mini에서 GPU 서버로 전면 이전

OrbStack 라이선스 만료로 Mac mini Docker 서비스를 GPU 서버로 통합.
nginx → Caddy 전환, 12개 서브도메인 자동 HTTPS, fail2ban Caddy JSON 연동.

주요 변경:
- home-caddy: Caddy 리버스 프록시 (Let's Encrypt 자동 HTTPS)
- home-fail2ban: Caddy JSON 로그 기반 보안 모니터링
- home-ddns: Cloudflare DDNS (API 키 .env 분리)
- gpu-hub-api/web: AI 백엔드 라우터 + 웹 UI (gpu-services에서 이전)
- AI 런타임(Ollama) 내부망 전용, 외부는 gpu-hub 인증 게이트웨이 경유

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

hub-api/services/proxy_ollama.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import json
+import logging
+from collections.abc import AsyncGenerator
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+async def stream_chat(
+    base_url: str,
+    model: str,
+    messages: list[dict],
+    **kwargs,
+) -> AsyncGenerator[str, None]:
+    """Proxy Ollama chat streaming, converting NDJSON to OpenAI SSE format."""
+    payload = {
+        "model": model,
+        "messages": messages,
+        "stream": True,
+        **{k: v for k, v in kwargs.items() if v is not None},
+    }
+
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        async with client.stream(
+            "POST",
+            f"{base_url}/api/chat",
+            json=payload,
+        ) as resp:
+            if resp.status_code != 200:
+                body = await resp.aread()
+                error_msg = body.decode("utf-8", errors="replace")
+                yield _error_event(f"Ollama error: {error_msg}")
+                return
+
+            async for line in resp.aiter_lines():
+                if not line.strip():
+                    continue
+                try:
+                    chunk = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+
+                if chunk.get("done"):
+                    # Final chunk — send [DONE]
+                    yield "data: [DONE]\n\n"
+                    return
+
+                content = chunk.get("message", {}).get("content", "")
+                if content:
+                    openai_chunk = {
+                        "id": "chatcmpl-gateway",
+                        "object": "chat.completion.chunk",
+                        "model": model,
+                        "choices": [
+                            {
+                                "index": 0,
+                                "delta": {"content": content},
+                                "finish_reason": None,
+                            }
+                        ],
+                    }
+                    yield f"data: {json.dumps(openai_chunk)}\n\n"
+
+
+async def complete_chat(
+    base_url: str,
+    model: str,
+    messages: list[dict],
+    **kwargs,
+) -> dict:
+    """Non-streaming Ollama chat, returns OpenAI-compatible response."""
+    payload = {
+        "model": model,
+        "messages": messages,
+        "stream": False,
+        **{k: v for k, v in kwargs.items() if v is not None},
+    }
+
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        resp = await client.post(f"{base_url}/api/chat", json=payload)
+        resp.raise_for_status()
+        data = resp.json()
+
+    return {
+        "id": "chatcmpl-gateway",
+        "object": "chat.completion",
+        "model": model,
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": data.get("message", {}).get("content", ""),
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": data.get("prompt_eval_count", 0),
+            "completion_tokens": data.get("eval_count", 0),
+            "total_tokens": data.get("prompt_eval_count", 0)
+            + data.get("eval_count", 0),
+        },
+    }
+
+
+async def generate_embedding(
+    base_url: str,
+    model: str,
+    input_text: str | list[str],
+) -> dict:
+    """Ollama embedding, returns OpenAI-compatible response."""
+    texts = [input_text] if isinstance(input_text, str) else input_text
+
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        resp = await client.post(
+            f"{base_url}/api/embed",
+            json={"model": model, "input": texts},
+        )
+        resp.raise_for_status()
+        data = resp.json()
+
+    embeddings_data = []
+    raw_embeddings = data.get("embeddings", [])
+    for i, emb in enumerate(raw_embeddings):
+        embeddings_data.append({
+            "object": "embedding",
+            "embedding": emb,
+            "index": i,
+        })
+
+    return {
+        "object": "list",
+        "data": embeddings_data,
+        "model": model,
+        "usage": {"prompt_tokens": 1, "total_tokens": 1},
+    }
+
+
+def _error_event(message: str) -> str:
+    error = {
+        "id": "chatcmpl-gateway",
+        "object": "chat.completion.chunk",
+        "model": "error",
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"content": f"[Error] {message}"},
+                "finish_reason": "stop",
+            }
+        ],
+    }
+    return f"data: {json.dumps(error)}\n\ndata: [DONE]\n\n"