- update_note: extend with status/priority/due_date fields so task attributes
can be changed via chat (mark done, set priority, move due date). body is now
optional — task field updates work without touching content.
- list_tasks: new core tool with status/priority/due_before/due_after/limit
filters backed by list_notes(is_task=True). Enables queries like
"overdue tasks", "high priority tasks", "what's in progress".
- update_todo: new CalDAV tool to modify VTODO summary, due date, description,
and priority — follows update_event pattern (modify component, rebuild ical,
save). Completes the CalDAV todo CRUD suite.
- tools.py: add update_todo import + execute case (type: todo_updated)
- llm.py: add list_tasks and update_todo to available actions + guidance
- intent.py: routing rules for mark-done/priority/due-date → update_note,
overdue/in-progress/high-priority queries → list_tasks, CalDAV todo updates
→ update_todo
- ToolCallCard.vue: tasks list block (linked titles + due + priority badges),
todo_updated label, tool-task-priority CSS classes
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
- CalDAV integration: per-user calendar config, create/list/search events
via caldav library, LLM tools for calendar operations from chat
- LLM-suggested tags: new tag_suggestions service prompts LLM with existing
tags and note content to suggest 3-5 relevant tags; exposed via API
endpoints (suggest-tags, append-tag); integrated into editor views
(suggest button + clickable pills) and chat tool calls (pills in
ToolCallCard with one-click apply)
- Settings/model UI refinements, generation task improvements
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ollama tool/function calling integration allows the LLM to create tasks,
create notes, and search existing notes on behalf of the user during chat.
Multi-round tool loop (max 5 rounds) lets the model execute tools then
produce a natural language response. Tool results are persisted in a new
JSONB column on messages and rendered as compact cards with linked titles.
- Migration 0013: add tool_calls JSONB column to messages
- New services/tools.py: tool definitions + execute_tool dispatcher
- llm.py: ChatChunk dataclass, stream_chat_with_tools(), date in system prompt
- generation_task.py: multi-round tool call loop with SSE tool_call events
- Frontend: ToolCallRecord type, streamingToolCalls in store, ToolCallCard
component, rendering in ChatMessage and ChatView streaming bubble
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
bvandeusen