The system prompt listed phantom tools (create_task, delete_task, get_note)
that don't exist, causing the model to spiral when users asked to create
tasks under a project. Replaced the stale hardcoded string with a
dynamically-built actions list matching all registered tools, and added
conditional searxng/caldav extensions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Qwen3:14b sometimes burns output tokens on tool-calling attempts whose
emission doesn't parse into any field we read — eval_count > 0 but no
thinking/content/tool_calls ever stream to the caller. Generation
completes "successfully," the user sees an empty assistant bubble, and
no error is logged. Seen in conv 220 today.
Two safety rails:
- stream_chat_with_tools now tracks whether it yielded anything; when
Ollama's done frame reports eval_count > 0 with zero yields, log a
warning including the last ~5 raw frames so the next occurrence leaves
breadcrumbs for diagnosis.
- run_generation checks the same post-condition after the tool loop
exits and, if content is empty with no tool calls but output_tokens
> 0, substitutes a visible fallback message and streams it as a chunk
so the user gets something readable instead of a blank bubble.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Discuss flow was hallucinating unrelated content when article
extraction returned empty or RAG pulled in orphan notes that looked
more relevant than the generic seed prompt.
- seed_article_discussion raises EmptyArticleError on empty body;
briefing and /news routes return 422 instead of staging an empty
synthetic tool result.
- build_context skips RAG auto-injection when user_message matches
ARTICLE_DISCUSS_SEED so the article IS the context on turn one;
follow-up turns keep RAG on.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Non-streaming generate_completion was the only LLM entry point that
didn't default num_ctx — stream_chat and stream_chat_with_tools both
fall back to Config.OLLAMA_NUM_CTX (16384). When a caller omitted the
argument, Ollama silently used the model's default window (~4k on
qwen3) and truncated the prompt.
That footgun was masked by fallback paths in the research pipeline:
_generate_outline's prompt carries ~12 sources × 2000 chars (~6k
tokens) of source material plus a system prompt, so the prompt got
chopped, the model never saw the sources, JSON parsing failed twice,
and run_research_pipeline dropped into the single-note "monolith"
fallback (research.py:251). The user reported chat 215 producing such
a monolith note for a multi-source research topic.
Two-layer fix:
- Default num_ctx to Config.OLLAMA_NUM_CTX inside generate_completion,
matching the streaming entry points. Any current or future caller
that forgets the argument stops silently losing input.
- Pin num_ctx=16384 explicitly in _generate_outline and
_generate_executive_summary with comments pointing at the failure
mode, so a refactor of the generate_completion default can't
silently regress the research pipeline.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two small hardening fixes from the mistral-nemo testing round:
1. stream_chat / stream_chat_with_tools now read the Ollama response
body and log it before raising on non-2xx. Previously all we saw
was 'HTTP 400 Bad Request' — the gemma3-no-tools failure would
have been diagnosed in one step if we'd been logging the body,
which says e.g. 'model does not support tools'.
2. backfill_project_summaries() now also targets summaries stamped
before 2026-04-12 (the gemma3:4b cutover). The remaining projects
still carrying the broken qwen2.5:3b output (token repetition,
hallucinated topics) will regenerate on next startup on the
better model.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ollama's /api/tags returns whatever casing was used at pull time
(e.g. 'gemma3:12B' if the user ran 'ollama pull gemma3:12B'), but
/api/chat rejects mixed-case tags with a 400. The two code paths
are inconsistent, which surfaces the capitalized tag in the model
dropdown and then silently kills every chat request against it.
Lowercase on read (get_installed_models), on settings write
(update_settings_route), and on ensure_model() input so a legacy
mixed-case user setting can't trigger a spurious re-pull at
startup. The dropdown and stored settings are now always in the
form Ollama will actually accept.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three related fixes uncovered while benchmarking qwen3:14b against 8b:
- pick_num_ctx was only counting message content, missing the ~15K
tokens of tool schemas. num_ctx=8192 was being selected while actual
prompt_tokens hit 14K+, causing silent prompt truncation on every
tool-using request. Now includes json.dumps(tools) in the estimate.
KV cache priming in app.py and routes/settings.py also fetches tools
so the primed num_ctx matches what real chat requests will use.
- _should_think's heuristic classifier was overriding explicit
think=true requests from the frontend toggle and MCP, gating on
message length and regex patterns. Now a pass-through — the caller
is the source of truth. quick_capture hardcodes think=False since
it's a fast classification path that was relying on the old gating.
- delete_note description only mentioned "note or task", so the model
refused to call it for entries created by save_person / save_place /
create_list. Description now explicitly lists all five note_types it
handles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Applies the grounding discipline from the agentic briefing work to the
main chat system prompt. The regular chat pipeline was already agentic
(it uses stream_chat_with_tools), but its system prompt never told the
model "only assert facts from tool results" or "if a tool returns
nothing, say so honestly." That left room for the same class of
hallucinations the briefings had — calling list_events, getting an
empty array, and then confidently mentioning a meeting anyway.
Adds two new static rules to the tool guidance block in llm.build_context:
GROUNDING — when the user asks about their own data, call the relevant
tool to see what exists. Never assert from memory or assumption.
HONESTY WHEN EMPTY — if a tool returns empty results, tell the user
plainly. No fabricated example items, no invented meetings, no generic
suggestions dressed up as real data.
Both rules are in the static (KV-cache-stable) portion of the system
prompt so they cost nothing on repeated requests for the same user.
Carries the hallucination fix from the briefing work directly into
every chat turn, not just chat that happens inside a briefing thread.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace the hardcoded "2h" keep_alive everywhere with a helper that
returns OLLAMA_KEEP_ALIVE_MAIN (default 30m) for the interactive model
and OLLAMA_KEEP_ALIVE_BACKGROUND (default 10m) for the background
model. Lets the main model release VRAM during long idle periods
while keeping it warm enough for bursty chat use, and stops the
sporadic background model from camping VRAM it rarely needs.
Seven call sites updated to route through llm.keep_alive_for(model):
the streaming helpers, generate_completion, the two startup warmers,
the settings KV-cache primer, and the chat warmer endpoint.
Override via env vars: OLLAMA_KEEP_ALIVE_MAIN, OLLAMA_KEEP_ALIVE_BACKGROUND.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- build_context: when conversation_type is 'briefing', inject a system
prompt instruction telling the model to answer from conversation history
and article context instead of searching the web
- Consolidate briefing conversation type detection to one DB query (was
being checked twice — once for the system prompt addition, once for
article context injection)
- ChatPanel: render a visual 'New Briefing Update' separator line before
2nd+ briefing slot messages (identified by metadata.rss_item_ids)
- types/chat.ts: add metadata field to Message interface
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds pick_num_ctx() which selects the smallest context window tier
(8192, 16384, 32768) that fits the current messages with 25% headroom,
capped at OLLAMA_NUM_CTX. Threads num_ctx through generation_task.py so
every chat request uses the computed tier rather than a fixed 16384.
Fixes a critical cache miss bug: KV cache priming in app.py and
settings.py was sending requests without num_ctx, so Ollama sized the
cache at its model default (different from the 16384 real requests used),
forcing a full model reload on the first real user message. Both priming
sites now call pick_num_ctx() and pass the matching value.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Removes verbose redundant text from tool descriptions and system prompt
guidance: multi-line recurrence_rule JSON examples, CAPS warnings that
duplicate system prompt instructions, and wordy descriptions that don't
add model understanding.
Saves ~990 tokens per request (~17% reduction, 5,639 → ~4,650 tokens),
reducing prefill time on cache misses and lowering KV memory pressure.
No functional changes — parameter names, types, enums, and required
fields are unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
RAG notes, RSS news, current note, URL content, and briefing articles
are now prepended to the user message rather than appended to the system
message. The system message now contains only stable content (persona,
tool guidance, date, profile, workspace, history summary), making its
token sequence identical across consecutive requests and allowing
Ollama's KV prefix cache to fire reliably every time.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Move static content (persona + tool guidance) to a fixed prefix and
append all dynamic content (date, timezone, profile, entities) as a tail.
Ollama prefix caching requires byte-for-byte token match from the start
of the prompt. Previously, Today's date + user profile were embedded
mid-prompt, invalidating the cache on every request/day and causing
~20s TTFT regardless of model warmth.
With this change the static prefix (~5500 tokens) should be cached
after the first request each session, reducing TTFT to ~2-5s for the
~200-token dynamic tail only.
Also removed inline user_timezone from tool_lines (timezone is now
stated once in the dynamic tail, which the model reads).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ChatView: listen mode toggle (auto-reads new responses via TTS), volume popup
with range slider persisted per-device in localStorage via GainNode
- useVoiceAudio: shared module-level _volume ref with localStorage persistence,
GainNode for volume control, exported setVoiceVolume()
- tts.py: pre-warm all Kokoro voices at pipeline load to eliminate HuggingFace
HEAD requests at synthesis time (reduces TTS latency)
- BriefingView: discuss article button now auto-sends instead of just filling input;
prompt capped to 15 sentences; send() accepts optional overrideText
- llm.py: instruct LLM not to proactively search notes or comment on note absence
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Embed RSS items at fetch time (nomic-embed-text); backfill at startup
- Semantic news search injected into chat system prompt ("Recent News You've Seen")
when items match query above 0.55 cosine threshold (independent of note RAG)
- "Discuss in chat" button on news cards — creates a seeded conversation with
the article title + full content, navigates directly to the new chat
- Briefing compilation now passes 500-char article excerpts (not just headlines)
to the LLM and uses 8192 num_ctx to accommodate the larger prompt
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces the freeform briefing-profile note with a DB-backed user_profiles
table. Users can edit job/industry/expertise/response preferences/interests/
work schedule via a new Settings → Profile tab. The LLM appends nightly
observations; at 14+ entries they are auto-consolidated into a learned_summary.
Profile context is injected into both briefing and chat system prompts.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Prevents models from sitting in VRAM indefinitely. Applies to both
streaming chat calls and the non-streaming generate_completion path,
as well as the startup warm-up request.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add _build_briefing_article_context() helper to llm.py that reads
rss_item_ids from briefing message metadata and injects article content
into the system prompt. Pass conv_id through build_context() and
generation_task.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Frontend sends user_timezone (IANA, from Intl.DateTimeFormat) with
every message POST; threaded through route → generation_task → build_context
- System prompt now tells the LLM the user's timezone so it creates
events with the correct UTC offset (e.g. 15:00+01:00 not 15:00Z)
- Calendar tool guidance updated to require UTC offset in all event
datetimes
- EventSlideOver: dateFromIso/timeFromIso now use JS Date to convert
stored UTC times to local time for display; toIso includes local
timezone offset when saving so the correct UTC time is stored
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- stream_chat: add think=False parameter passed through to Ollama payload.
qwen3 models have thinking enabled by default; without this flag the model
spends minutes generating internal thinking tokens that stream_chat silently
discards, leaving the frontend spinner blank until the SSE connection times
out and the widget disappears.
- create_assist_buffer: orphan (overwrite) a still-running buffer instead of
raising. The old asyncio task holds a direct reference and completes
harmlessly against the stale buffer. New requests always win.
- assist_route: remove the 409 guard that blocked new requests when a previous
generation got stuck. create_assist_buffer now handles this transparently.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- stream_chat and stream_chat_with_tools: remove read=300s per-chunk
timeout, replace with read=None. In httpx streaming mode, the read
timeout applies per-chunk — if Ollama pauses >300s while processing
a large input context before the first token, it raises ReadTimeout,
killing generation and leaving the assistant message as an empty stub.
With read=None the stream is unbounded; connect=30s still guards the
initial connection.
- chat_status_route: increase Ollama status check timeout 5s → 10s.
When Ollama is busy processing a large prompt it can be slow to
respond to /api/tags, causing the status indicator to briefly flip to
"offline" even though generation is running normally.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove all 6 CalDAV todo tools (create/list/update/complete/delete/search_todos)
from tools.py definitions, imports, execute_tool branches, intent routing rules,
generation_task labels/actions, and llm.py system prompt hints. CalDAV event
tools remain. Todo functions still exist in caldav.py but are no longer exposed.
- Quick-capture now uses a dedicated classify_capture_intent() with a focused
_CAPTURE_SYSTEM_PROMPT that always routes to a tool (never null). Tool set
expanded: create_note/task/event + update_note + research_topic.
- research_topic in quick-capture calls run_research_pipeline() directly (no SSE
buffer). run_research_pipeline() now accepts buf=None; all buf.append_event
calls are guarded so status events are skipped when no buffer is provided.
- Fallback note now always sets body=text (was empty for texts ≤80 chars).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- tools.py: search_images result now includes 'embed' (ready-to-use
markdown image syntax) and 'citation' fields instead of raw 'local_url';
adds 'instructions' field so the model knows to render them verbatim
- llm.py: system prompt now explicitly tells the model to embed images
using the 'embed' field rather than describing or listing URLs
- markdown.ts: explicitly allow src/alt in PURIFY_OPTS_FULL so img tags
are never stripped by DOMPurify
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Raise similarity threshold 0.30 → 0.45: only genuinely relevant notes
shown; loosely-related notes no longer pad the sidebar
- Increase max suggested notes 3 → 8 (zero added compute — threshold is
the real gate; the embedding call is fixed regardless of limit)
- semantic_search_notes now returns list[tuple[float, Note]] instead of
list[Note] so scores propagate through context_meta to the frontend
- Keyword fallback notes carry score=null (no cosine similarity available)
- ChatView sidebar shows % badge on each suggested note:
green ≥75%, amber 60–74%, muted <60%
Hovering reveals the raw score in a tooltip
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ollama streams message.thinking tokens alongside message.content when
think=True — previously silently dropped. Now forwarded end-to-end.
Backend:
- llm.py: ChatChunk type gains "thinking" variant; stream_chat_with_tools
yields ChatChunk(type="thinking") for msg.thinking chunks before content
- generation_task.py: thinking chunks emit "thinking_chunk" SSE events
(not added to content_so_far — not persisted to DB)
Frontend:
- types/chat.ts: Message.thinking?: string (session-only, not from DB)
- stores/chat.ts: streamingThinking ref; thinking_chunk handler accumulates
chunks; on done, thinking carried into committed Message object then cleared
- ChatMessage.vue: collapsible <details class="thinking-block"> shown for
messages that have .thinking content (collapsed by default)
- ChatView.vue + ChatPanel.vue: live thinking block in streaming bubble —
open while only thinking is flowing, auto-collapses when content arrives;
typing indicator hidden while thinking is active
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Instead of relying solely on retry-on-500, poll /api/ps before starting
any LLM stream so the main model has time to fully load into VRAM.
- llm.py: add wait_for_model_loaded(model, timeout=90s) — polls /api/ps
every 2s, returns True when model appears in loaded list
- generation_task.py: launch model_load_task in parallel with build_context
and classify_intent (both use fast/small-model ops that don't need the
main model); after context is built, await the load task — shows
"Loading model..." status only if the user actually has to wait;
logs a warning and proceeds if 90s timeout elapses
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Tags are now a first-class field rather than being auto-extracted from
the note body. A new TagInput.vue chip component handles tag entry in
both editor views with autocomplete, Enter/comma/backspace UX, and
space-to-hyphen sanitization.
Backend:
- routes/notes.py: create reads tags from JSON; update accepts explicit
tags (omit = keep existing); append_tag writes to tags array with
dedup; suggest-tags accepts current_tags filter; remove extract_tags
- routes/tasks.py: same — explicit tags on create/update; remove extract_tags
- services/tag_suggestions.py: current_tags param replaces body extraction
- services/tools.py: create_note tool schema adds tags param; executor passes it
- services/llm.py: system prompt tells LLM to use tags param, not embed #tag in body
Frontend:
- components/TagInput.vue: new chip-based tag input (autocomplete, keyboard UX)
- NoteEditorView.vue / TaskEditorView.vue: tags ref loaded from note.tags;
TagInput placed between title and body; save/autosave include tags; suggest
now adds chips; fetchTagSuggestions passes current_tags; dirty tracks tags
- TiptapEditor.vue: remove fetchTags prop and TagSuggestion extension;
keep TagDecoration for legacy inline #tag highlighting
No DB migration needed — tags column already correct.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Tags with spaces (e.g. #science fiction) were breaking extraction because
TAG_RE only matched word characters — it would stop at the space and extract
#science instead of #science-fiction.
- TAG_RE (backend + frontend): add hyphens to character class so #science-fiction
is recognized as a single tag: [\w][\w-]* per segment
- System prompt: instruct LLM to use hyphens in multi-word tags, never spaces
- tag_suggestions.py: update prompt example + sanitize output by replacing
spaces with hyphens as a safety net regardless of LLM output
- append-tag route: sanitize incoming tag (spaces → hyphens) before appending
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces the hardcoded num_ctx=32768 KV cache allocation with a
configurable env var defaulting to 8192. This significantly reduces
VRAM pressure when multiple services share the GPU.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Context sidebar + note title:
- ChatView: replace ephemeral context pills with a persistent right-panel sidebar;
auto-found notes accumulate across turns; attached note shows with pin icon;
× button excludes a note from future auto-search; hidden on mobile
- routes/chat.py: batch-fetch note titles via get_notes_by_ids() and inject
context_note_title into each message dict at conversation load time
- notes.py: add get_notes_by_ids() batch fetch helper
- types/chat.ts: add context_note_title field to Message interface
- stores/chat.ts: sendMessage accepts optional 5th arg contextNoteTitle,
included in optimistic user message
- ChatMessage.vue: context badge shows note title instead of 'Note #N'
Expanded LLM tool suite (all with intent router rules + ToolCallCard display):
- delete_note / delete_task: permanent delete with user confirmation (write tool),
type-safe (refuse to delete wrong type), clears note context cache on success
- get_note: fetch full note body by query (search_notes returns only 200-char preview)
- list_notes: browse notes by recency/keyword/tags with limit; notes only
- update_note: add tags + tag_mode (replace/add/remove) parameters
- search_notes: add optional type filter ("note" | "task")
- search_todos (CalDAV): keyword-filter todos, companion to list_todos
- caldav.py: add search_todos() built on top of list_todos()
- generation_task.py: register new tools in _WRITE_TOOLS, _TOOL_LABELS, _TOOL_ACTIONS
- llm.py: update available actions list and guidance in system prompt
- intent.py: routing rules for all new tools
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- New NoteEmbedding model + migration 0014 stores float embeddings (JSONB)
- services/embeddings.py: get_embedding, upsert_note_embedding,
semantic_search_notes (cosine similarity), backfill_note_embeddings
- build_context() now tries semantic search first, falls back to keyword search;
accepts cached_note_ids to reuse last-turn notes and stabilise the system
prompt prefix for Ollama's KV cache
- generation_buffer.py: per-conversation note ID cache (get/set/clear)
- generation_task.py: passes cached IDs into build_context, updates cache
after each turn, and invalidates it after create_note/update_note/create_task
- app.py: pulls nomic-embed-text at startup and launches a background backfill
to embed all existing notes (30 s delay so Ollama has time to load the model)
- routes/notes.py + services/tools.py: fire-and-forget embedding update on
every note create or update via the API or LLM tool calls
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When a conversation exceeds 20 messages (10 exchanges), the oldest
messages are summarized into a compact 3-5 sentence paragraph using the
intent model, and only the most recent 6 messages are passed verbatim.
The summary is injected into the system prompt so the model retains
context without the full token cost. For short conversations the check
is O(1) and returns immediately. The status indicator shows
"Summarizing conversation history..." when the LLM call is needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
stream_chat_with_tools now accepts a think parameter. run_generation
forwards it to Ollama. The message POST route reads think from the
request body. ChatView passes think=true so qwen3 uses chain-of-thought
reasoning for full conversations; the dashboard widget and ChatPanel
omit it, staying fast. Dashboard button updated to "Think it through
in Chat →" to signal the deeper capability.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pinned note: full body restored (truncation is wrong when the user is
explicitly asking about that note's content).
Auto-notes: restored to 2000 chars (800 was too restrictive for useful context).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Auto-notes (keyword-matched): 2000 → 800 chars each (×3 max = 6000 → 2400 chars).
Pinned note (explicit context): was unbounded → capped at 4000 chars with [truncated] marker.
The main post-GPU bottleneck is TTFT caused by the prefill phase — the model
processing the full input before generating any tokens. Shorter context =
faster prefill. Users can ask follow-up questions for more detail.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Docker Compose:
- Enable Ollama GPU passthrough (nvidia, count: all) in both dev and prod files
- Add OLLAMA_FLASH_ATTENTION=1 (faster attention on GPU in both files)
- Add OLLAMA_MAX_LOADED_MODELS=2 and OLLAMA_KEEP_ALIVE=30m to prod (was already in dev)
- Remove 8G memory limit from prod Ollama service (CPU-bound constraint, no longer valid)
llm.py:
- Increase num_ctx 16384 → 32768 in stream_chat and stream_chat_with_tools (GPU VRAM allows it)
- Increase num_predict cap 4096 → 8192 for tool-augmented responses
generation_task.py:
- Parallelize build_context, get_tools_for_user, and get_setting all from the start
- As soon as tools list is ready (fast DB call), launch classify_intent as an asyncio.Task
- Await build_context and classify_intent together via asyncio.gather
- Intent result is pre-computed before the generation loop; loop just reads pre_intent on round 0
- intent_ms timing now reflects wall-clock time from intent start to completion
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- build_context() moved from route handler into run_generation() background task.
The 202 response now returns immediately; client connects to SSE before
note search / URL fetch begins, so 'Building context...' status is visible.
- _generate_title() runs in a fire-and-forget asyncio.create_task() after the
'done' SSE event fires. Users see their response complete 2–5s sooner on new
conversations; title appears later in the sidebar without blocking the stream.
- generate_completion() now sets think:False and accepts a max_tokens limit.
Intent classifier passes max_tokens=200 (JSON only), title generator passes
max_tokens=30 (short title), eliminating qwen3 thinking-mode overhead on these
auxiliary calls.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
bvandeusen
Bryan Van Deusen