Switch default model to qwen3 and add intent routing for reliable tool calling

Mistral didn't reliably use Ollama's structured tool calling API — it wrote
tool calls as JSON text instead of invoking them. This adds an intent routing
layer that classifies user intent via a fast non-streaming LLM call before
streaming, executing detected tools directly and bypassing native tool calling.

- Change default OLLAMA_MODEL from mistral to qwen3
- Add intent.py: classify_intent() with JSON parsing and fallback regex
- Integrate intent routing into generation_task.py round 0
- Add all-day event support (iCalendar DATE values) to CalDAV service
- Add recurring event support (RRULE) to CalDAV service and tool definition
- Improve create_event tool description for descriptive titles
- Enhance system prompt with structured tool usage guidance

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

summary.md

@@ -12,7 +12,7 @@
 >    Include file-level details in the commit body when the change is non-trivial.
 
 ## Last Updated
-2026-02-15 — Phase 8: CalDAV calendar integration, LLM-suggested tags, settings/model UI refinements
+2026-02-16 — Phase 9: Switch to qwen3, intent routing for reliable tool calling, all-day/recurring events
 
 ## Project Overview
 Fabled Assistant is a self-hosted note-taking and task-tracking application with
@@ -263,10 +263,11 @@ fabledassistant/
 │       │   ├── llm.py           # Ollama interaction: build_context with user_id, streaming (stream_chat + stream_chat_with_tools), ChatChunk dataclass, URL fetching
 │       │   ├── chat.py          # Conversation CRUD with user_id isolation, add_message, save/summarize as note (LLM-titled, chat-tagged)
 │       │   ├── generation_buffer.py # In-memory SSE event buffer with cancel_event, reconnect support, auto-cleanup; supports chat (int keys) and assist (string keys)
-│       │   ├── generation_task.py   # Background asyncio tasks: run_generation (chat, DB flush, titles, tool loop) + run_assist_generation (lightweight, no DB)
-│       │   ├── tools.py            # LLM tool definitions (create_task, create_note, search_notes, CalDAV events) + execute_tool dispatcher
+│       │   ├── generation_task.py   # Background asyncio tasks: run_generation (chat, DB flush, titles, intent routing + tool loop) + run_assist_generation (lightweight, no DB)
+│       │   ├── intent.py          # Intent routing: classify_intent() makes fast non-streaming LLM call to detect tool intent before streaming
+│       │   ├── tools.py            # LLM tool definitions (create_task, create_note, search_notes, CalDAV events with all-day/recurrence) + execute_tool dispatcher
 │       │   ├── tag_suggestions.py # LLM-powered tag suggestions: suggest_tags() builds prompt with existing tags, calls generate_completion, parses JSON response
-│       │   ├── caldav.py          # CalDAV integration: create/list/search calendar events via caldav library (per-user config from settings)
+│       │   ├── caldav.py          # CalDAV integration: create/list/search calendar events via caldav library, all-day + recurring event support (per-user config from settings)
 │       │   ├── settings.py      # Settings CRUD with user_id isolation: get_setting, set_setting, set_settings_batch, get_all_settings
 │       │   ├── logging.py      # App logging: log_audit, log_usage, log_error, get_logs, get_log_stats, delete_old_logs, start_log_retention_loop
 │       │   ├── email.py        # SMTP email service: get_smtp_config, is_smtp_configured, send_email, send_test_email
@@ -523,7 +524,7 @@ When adding a new migration, follow these conventions:
 - All task sections hidden when empty; marking done removes from all lists
 
 ### LLM Chat
-- Ollama integration via async HTTP (httpx), auto-pull default model on startup
+- Ollama integration via async HTTP (httpx), default model qwen3 (better tool support than mistral), auto-pull on startup
 - Background generation with `GenerationBuffer` (in-memory SSE fan-out, `Last-Event-ID` reconnect, 60s cleanup)
 - Stop generation with partial content preservation
 - Note-aware context building: current note + keyword search for related notes + URL fetching
@@ -546,9 +547,14 @@ When adding a new migration, follow these conventions:
   components (linked titles for created items, search result lists). SSE emits `tool_call` events
   for real-time rendering during streaming. System prompt includes today's date for relative date
   resolution. Graceful degradation: models without tool support respond normally.
+- **Intent routing:** Before streaming, a fast non-streaming LLM call classifies user intent and
+  extracts tool parameters (`services/intent.py`). If a tool call is detected, it executes directly
+  — bypassing the model's native (sometimes unreliable) tool calling API. Falls through to normal
+  streaming when no tool is detected or classification fails. Only runs on first round of tool loop.
 - **CalDAV calendar integration:** Per-user CalDAV settings (URL, username, password, calendar name).
-  LLM tools: `create_event`, `list_events`, `search_events`. Runs synchronous caldav library calls
-  in asyncio executor. Settings UI for CalDAV configuration.
+  LLM tools: `create_event` (with `all_day` and `recurrence` support), `list_events`, `search_events`.
+  All-day events use iCalendar DATE values; recurrence uses RRULE (e.g. `FREQ=YEARLY`).
+  Runs synchronous caldav library calls in asyncio executor. Settings UI for CalDAV configuration.
 - **LLM-suggested tags:** Backend service (`tag_suggestions.py`) prompts LLM with existing user tags
   and note content, returns 3-5 relevant tag suggestions. Tags already in body are filtered out.
   Exposed via `POST /api/notes/suggest-tags` and `POST /api/notes/:id/append-tag`. Integrated in: