feat: tenant AI chat agent with function calling

Actionable AI assistant that uses per-tenant Azure OpenAI credentials
to execute natural language commands against the render pipeline.

Backend:
- ChatMessage model + migration (session-based conversations)
- Chat service with 10 OpenAI function-calling tools:
  list_orders, search_products, create_order, dispatch_renders,
  get_order_status, set_material_override, set_render_overrides,
  get_render_stats, check_materials, query_database
- All tools tenant-scoped (queries filtered by tenant_id)
- Write operations use httpx to call backend API internally
- Chat API: POST /chat/messages, GET /chat/sessions, DELETE session
- Conversation history preserved in DB (last 50 messages per session)

Frontend:
- Slide-out ChatPanel (right side, w-96, animated)
- User/assistant message styling with avatars and timestamps
- Session management (new chat, session history, delete)
- Typing indicator while waiting for AI response
- Floating chat button in bottom-right corner
- Error state for unconfigured AI tenants

Example: "Render all Kugellager products as WebP at 1024x1024"
→ Agent calls search_products + create_order + dispatch_renders

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

backend/app/models/__init__.py

@@ -18,11 +18,12 @@ from app.domains.admin.models import DashboardConfig
 # Also re-export SystemSetting (no domain assigned — stays as-is)
 from app.models.system_setting import SystemSetting
 from app.models.worker_config import WorkerConfig
+from app.models.chat import ChatMessage
 
 __all__ = [
     "Tenant", "User", "Template", "CadFile", "Product", "Order", "OrderItem", "OrderLine",
     "AuditLog", "PricingTier", "OutputType", "RenderTemplate", "ProductRenderPosition", "GlobalRenderPosition",
     "WorkflowDefinition", "WorkflowRun", "WorkflowNodeResult",
     "Material", "MaterialAlias", "AssetLibrary", "MediaAsset", "MediaAssetType", "SystemSetting",
-    "DashboardConfig", "WorkerConfig",
+    "DashboardConfig", "WorkerConfig", "ChatMessage",
 ]

backend/app/models/chat.py

@@ -0,0 +1,28 @@
+"""Chat message model for tenant AI agent conversations."""
+import uuid
+from datetime import datetime
+
+from sqlalchemy import String, DateTime, Text, ForeignKey, Integer
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.dialects.postgresql import UUID
+
+from app.database import Base
+
+
+class ChatMessage(Base):
+    __tablename__ = "chat_messages"
+
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    tenant_id: Mapped[uuid.UUID | None] = mapped_column(
+        UUID(as_uuid=True), ForeignKey("tenants.id", ondelete="CASCADE"), nullable=True, index=True
+    )
+    user_id: Mapped[uuid.UUID | None] = mapped_column(
+        UUID(as_uuid=True), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
+    )
+    session_id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), nullable=False, index=True)
+    role: Mapped[str] = mapped_column(String(20), nullable=False)  # "user", "assistant", "system"
+    content: Mapped[str] = mapped_column(Text, nullable=False)
+    context_type: Mapped[str | None] = mapped_column(String(50), nullable=True)  # "order", "product", "general"
+    context_id: Mapped[uuid.UUID | None] = mapped_column(UUID(as_uuid=True), nullable=True)
+    token_count: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow, nullable=False)