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/base.py

@@ -0,0 +1,31 @@
+from abc import ABC, abstractmethod
+
+SYSTEM_PROMPT = (
+    "You are a financial document analysis assistant. "
+    "Given the text extracted from a PDF document, return ONLY a JSON object "
+    "with no markdown, no code fences, and no explanation."
+)
+
+USER_PROMPT_TEMPLATE = """Analyze the following document text and return a JSON object with exactly these keys:
+document_type (one of: invoice, bill, receipt, order, expense, revenue, unknown),
+total_amount (string or null),
+currency (string or null),
+vendor_name (string or null),
+customer_name (string or null),
+billing_address (string or null),
+customer_address (string or null),
+invoice_number (string or null),
+invoice_date (string or null),
+due_date (string or null),
+tags (array of strings),
+line_items (array of objects, each with keys: description, amount).
+
+Document text:
+{text}"""
+
+
+class AIProvider(ABC):
+    @abstractmethod
+    async def classify_document(self, text: str) -> dict:
+        """Return structured extraction dict from document text."""
+        ...