Add PDF document service with AI extraction and per-app settings

- New `features/doc-service` FastAPI microservice: PDF upload, async
  text extraction (pdfplumber), AI classification via Anthropic/Ollama/
  LM Studio, per-user categories, file download
- Alembic migration isolated with `alembic_version_doc_service` table
- Main backend: httpx proxy routers for /api/documents/* and
  /api/documents/categories/*, admin settings API at /api/settings/*
- Runtime config in /config/doc_service_config.json (shared Docker
  volume); api_key masking on reads; atomic write with os.replace()
- Frontend: DocumentsPage, DocumentAdminSettingsPage, updated AppsPage
  launcher hub, simplified Nav (removed Settings link), new routes
- docker-compose: doc-service service, doc_data + app_config volumes,
  removed internal:true from backend-net for outbound AI API calls
- Fix pre-commit hook: probe Docker socket path so git subprocess picks
  up Docker Desktop on macOS
- Fix security_check.py: use sys.executable for bandit so venv python
  is used instead of system python

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

features/doc-service/app/services/ai/openai_compat.py

@@ -0,0 +1,36 @@
+"""
+OpenAI-compatible provider for Ollama and LM Studio.
+Both expose an OpenAI-compatible /v1/chat/completions endpoint.
+"""
+import json
+
+from openai import AsyncOpenAI
+
+from app.services.ai.base import AIProvider, SYSTEM_PROMPT, USER_PROMPT_TEMPLATE
+
+
+class OpenAICompatProvider(AIProvider):
+    def __init__(self, config: dict) -> None:
+        self._client = AsyncOpenAI(
+            base_url=config["base_url"],
+            api_key=config.get("api_key", "not-required"),
+        )
+        self._model = config["model"]
+
+    async def classify_document(self, text: str) -> dict:
+        response = await self._client.chat.completions.create(
+            model=self._model,
+            temperature=0,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": USER_PROMPT_TEMPLATE.format(text=text[:100_000])},
+            ],
+        )
+        raw = response.choices[0].message.content.strip()
+        return _parse_json(raw)
+
+
+def _parse_json(raw: str) -> dict:
+    if raw.startswith("```"):
+        raw = raw.split("\n", 1)[1].rsplit("```", 1)[0]
+    return json.loads(raw)