Adds rulebooks.always_on (migration 0058) and a new list_always_on_rules
MCP tool so a session-start eager pull can fetch standing rules without
needing an active-project notion. Updates _INSTRUCTIONS so Claude calls
the new tool at session start and codifies engineering rules in Scribe
rather than CLAUDE.md / auto-memory.
Seeds FabledSword family rulebook to always_on=true on migrate, matching
its design role as the cross-project standards rulebook.
Frontend: badge in RulebookListPane for always-on rulebooks; toggle in
RulebookDetailPane header bound to a new toggleAlwaysOn store action.
This is S1+S2 of the rules-consolidation plan (Scribe task #508). S3
(project-scoped rules) and S4 (enter_project handshake) follow.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three coordinated changes per operator request 2026-05-24:
1. Settings UI rename matching the language we actually use:
- Chat Model -> Chat & Voice Model
- Worker Model -> Curator Model
Setting KEYS (default_model / background_model) unchanged on
purpose; renaming them requires a migration touching 50+ call
sites for purely UX-facing benefit.
2. Settings UI help text rewritten:
- Chat & Voice: documents that it handles chat AND small
conversational automations (titles, tags). Recommends
OLLAMA_NUM_PARALLEL=2+ on the Ollama server so background
automations get their own KV-cache slot and don't evict
the chat model's working state.
- Curator: notes the app enforces SERIAL execution regardless
of NUM_PARALLEL — only one curator pass runs at a time. This
matters most for 70b CPU models where a second instance
would waste system RAM.
3. Enforce serial curator execution globally:
- New module-level _CURATOR_RUN_LOCK in services/curator.py.
- run_curator_for_conversation now wraps its body in 'async
with _CURATOR_RUN_LOCK' — every entry point (scheduler sweep,
manual route trigger, future hooks) is serialized through it.
- is_curator_running() helper exposes the lock state.
- routes/journal.py manual trigger checks is_curator_running()
first and returns 409 {busy: true} immediately rather than
blocking the HTTP request for minutes waiting for a 70b CPU
pass to finish. The user can retry once the curator clears.
Why a 409 instead of queue: a curator pass on a 70b CPU model
can take 5+ minutes. Tying up an HTTP worker that long is bad;
making the user wait without feedback is worse. 409 surfaces
the busy state immediately and the user retries when they want.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The HTTP surface for the review queue. Three endpoints, all under
the existing /api/journal blueprint to keep the journal-related routes
together:
- GET /api/journal/pending — list current user's pending actions.
- POST /api/journal/pending/<id>/approve — replay the proposed tool
call via execute_tool(authority='user'). On success, marks
the row 'approved'; on replay error, leaves it pending so
the user can retry.
- POST /api/journal/pending/<id>/reject — marks 'rejected' with no
execution.
Each route is a thin wrapper around services/pending_actions and
delegates user-scoping to the service (which checks user_id on every
load — actions are private to the proposer).
api/client.ts:
- PendingCuratorAction interface mirroring the backend dict shape:
id, user_id, conv_id, action_type, target_type/id/label, payload,
current_snapshot, status, timestamps.
- listPendingActions / approvePendingAction / rejectPendingAction
helpers for the upcoming Needs Review panel.
C5 next: the panel itself.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two related reliability fixes.
1. routes/chat.py — guard run_generation against uncaught exceptions.
run_generation is launched with asyncio.create_task(); any exception
raised inside the coroutine is silently swallowed by the event loop,
the buffer stays in GenerationState.RUNNING forever, and every
subsequent POST /api/chat/conversations/<id>/messages returns 409
'Generation already in progress' — locking the user out of the chat
with no log trail.
Observed in dev 2026-05-22: assistant message 768 created at 20:36:59
with status=generating, stayed in that state for an hour+, and four
follow-up message attempts returned 409 instantly. The generation
task hung before any internal log line could fire, so the only
diagnostic was the 409 responses themselves.
Wrap run_generation in _run_generation_guarded() that catches
exceptions, logs with full traceback, transitions the buffer to
ERRORED, emits a final 'done' SSE event so any active stream
client closes cleanly, and marks the assistant message status=error
in the DB. After this, a stuck conversation recovers on its own
the next time the user sends a message — no manual DB poke needed.
2. services/curator_scheduler.py — pass last_curator_run_at as 'since'
to the curator so each sweep only sees messages added after the
previous successful pass.
Previously the scheduler called run_curator_for_conversation(conv_id)
with no 'since' argument, so the curator defaulted to its 24h
lookback window. Within an active journal session that meant every
15-min sweep re-extracted beats from messages already captured
on prior sweeps — producing duplicate moments.
_candidate_conversations() now returns (conv_id, last_curator_run_at)
tuples; _sweep() threads the timestamp through. First-run case
(last_curator_run_at IS NULL) falls back to the curator's default
24h window, which is what we want — process recent backlog on
first contact, then only deltas after.
Manual trigger path (POST /api/journal/curator/run/<conv_id>) is
intentionally NOT changed; it still passes since=None so the
24h re-sweep behaviour is preserved for ad-hoc 'reprocess today'
clicks from the UI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The architecture loop closes. Curator extracts beats and writes a
≤240-char summary; the next chat turn loads that summary into the
journal system prompt so the chat model — which has no tools and
cannot retrieve anything itself — gains awareness of recent topics
captured by the curator.
Migration 0049:
- conversations.curator_summary (text, nullable). Last-write-wins; no
history of prior summaries.
models/conversation.py:
- New curator_summary column on Conversation.
services/curator_scheduler.py:
- _stamp_last_run() takes an optional summary kwarg; persists it when
non-empty (clobbering the previous summary). Empty summary keeps
the existing one rather than overwriting useful context with "".
- _sweep() passes result.summary through.
routes/journal.py:
- Manual /api/journal/curator/run/<conv_id> writes curator_summary
alongside last_curator_run_at on success.
services/journal_pipeline.py:
- build_journal_system_prompt() gains an optional `conv_id` param.
When provided, appends a "CURATOR NOTES" block at the end of the
system prompt with the conversation's stored summary. Positioned
after ambient context so the chat model treats it as current
awareness rather than background.
services/llm.py:
- Threads conv_id through to build_journal_system_prompt.
This is the last commit of the conversation+curator architecture
arc (Fable #172):
- Phase 1a (a7002a8): chat=tools[], curator service backend
- Phase 1b (a73dd17): right-rail captures panel + manual trigger
- Phase 2 (83f1676): auto-scheduler every 15 min
- Phase 3 (this): curator summary → chat context feedback loop
Operator can now device-test the architecture end-to-end: have a
journal conversation (model can't lie about tool calls because it
has none), wait for the scheduler or hit "Process captures", see
moments appear in the right rail, then continue the conversation
and notice the chat model staying topic-aware via the summary block.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The curator now runs automatically every 15 minutes against any
journal conversation that has user messages newer than its last
curator run. Manual triggers from Phase 1b still work and now also
stamp the timestamp so the scheduler doesn't double-process.
Migration 0048:
- conversations.last_curator_run_at (timestamptz, nullable).
- Partial index ix_conversations_journal_last_curator on the column
filtered to conversation_type='journal'. The scheduler's candidate
query is "journal AND (NULL OR stale)" so an index narrowed to
journal rows is the right shape — index size stays small even on
instances with many non-journal conversations.
models/conversation.py:
- New `last_curator_run_at` column on Conversation. DateTime imported.
services/curator_scheduler.py (new):
- IntervalTrigger every 15 min via BackgroundScheduler (same pattern
as journal_scheduler.py).
- _candidate_conversations(): SELECT journal conversations where the
newest user message is newer than last_curator_run_at (or NULL).
Capped at 20 per sweep so a backlog after downtime doesn't stall
the scheduler.
- _sweep() processes candidates sequentially under an asyncio.Lock
so overlapping ticks can't double-fire on the same conversation.
Failed runs leave the timestamp alone — natural retry on next sweep.
- start_/stop_curator_scheduler() wired into app.py boot/shutdown.
routes/journal.py:
- Manual /api/journal/curator/run/<conv_id> stamps last_curator_run_at
on success. Errors don't stamp so the scheduler retries.
What's still pending:
- Phase 3: feedback loop (curator summary into chat context). Currently
the curator's summary lives in the run result but doesn't reach the
chat model.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Backend half of the conversation+curator architecture (Fable #172).
Decouples the journal chat surface from tool calling: the chat model
now sees `tools=[]` and just talks, while a separate curator pass
extracts beats and fires the tool calls.
services/generation_task.py:
- When conversation_type == "journal", pass `tools=[]` to Ollama
regardless of what the journal tool set would normally provide.
The chat model literally cannot fire record_moment / create_task /
etc., so it cannot lie about firing them — the primary failure
mode this architecture removes.
services/curator.py (new):
- `run_curator_for_conversation(conv_id, since=None)` loads recent
messages, builds a curator-specific system prompt (extract beats,
emit tool calls, optionally a one-line summary), and iterates the
Ollama tool-call loop using the user's background_model so the
chat model's KV cache survives.
- Same tool registry as a normal journal conversation
(record_moment, search_notes, update_task, create_task,
save_person, save_place, etc.). The curator chooses naturally
among them; no need for a separate curator-specific filter.
- Returns CuratorRunResult with per-call status + a summary line.
- Caps at 4 tool-call rounds — bounded task (extract beats from a
fixed transcript), shouldn't need more.
- Errors land in result.error rather than raising; the manual
trigger surface (and later the scheduler) want a structured
result, not exceptions.
routes/journal.py:
- New POST /api/journal/curator/run/<conv_id> for manual triggers.
Validates conv ownership before running. Returns the
CuratorRunResult dict so the UI can show what was captured.
What's not in this commit (deferred to later phases):
- The scheduler that auto-runs the curator (phase 2 — adds the
`conversations.last_curator_run_at` column + APScheduler job).
- Curator → chat feedback loop (phase 3 — summary gets injected
into subsequent chat system prompts).
- Right-rail captures panel in JournalView (phase 1b — pure frontend
work, separate commit for clean review).
- Research surface separation (phase 4).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Building on the kokoro→piper swap (B1), this adds the admin-side
voice management story so additional voices can be installed without
rebuilding the image. The bundled two voices stay as immediate defaults;
everything else is opt-in via a one-click install from the catalog.
Backend (services/voice_library.py):
- fetch_catalog() pulls voices.json from the piper-voices HF repo with
a 24h in-memory TTL. Manual refresh available via ?refresh=1 on the
library endpoint.
- shape_catalog_for_ui() projects the raw HF dict (~250 voices, lots of
nesting) into UI-friendly cards: id, name, language, country, quality,
size, install state. Sorted by language_code then name for stable
display. Install state distinguishes bundled (read-only) from user
(admin-installed, can be removed).
- install_voice() downloads .onnx + .onnx.json into /data/voices with
atomic .tmp → rename so a failed partial download can't leave a
corrupt model around. Idempotent — re-installing an already-present
voice is a no-op.
- uninstall_voice() removes /data voices; bundled /opt voices raise
PermissionError (403 at the route layer).
- Strict voice-id regex prevents path traversal in install/uninstall.
Routes (admin-only, since these write to shared /data and affect all
users on the instance):
- GET /api/voice/voices/library
- POST /api/voice/voices/install
- DELETE /api/voice/voices/<voice_id>
Frontend:
- New "Voice Library" section in Settings → Voice, visible only to
admin users. Collapsed by default; expand to load the catalog
on-demand (doesn't hammer HF for non-admins).
- Free-text filter across id, language code, language name, country,
and dataset name. Refresh button forces a catalog re-fetch.
- Per-voice row shows id, language/country/quality/speaker count, size,
and either an Install button, a Remove button (user voices), or a
"bundled" badge (read-only voices in /opt/piper-voices).
- Installs and uninstalls refresh both the library list AND the active
voice picker so the new voice is immediately selectable.
- VoiceLibraryEntry exported from api/client.ts; new client helpers
getVoiceLibrary/installVoice/uninstallVoice.
Tests:
- Pure-transformation unit tests for shape_catalog_for_ui,
_resolve_file_urls, and the voice-id regex (path-traversal coverage).
- DB/network paths (fetch_catalog, install_voice) need a real
environment — left to CI integration tests or device verification.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Kokoro has been stale upstream since April 2025 (`requires_python<3.13`),
which broke the Python 3.14 build. Piper is the active replacement:
maintained by OHF/Home Assistant, depends only on onnxruntime +
pathvalidate (no torch, no spacy, no transformers), and has cp314
support today.
Dockerfile:
- Add `pip install piper-tts` after the STT install.
- Bundle two default voices (en_US-amy-medium, en_US-ryan-medium) into
/opt/piper-voices at build. Additional voices can be downloaded into
/data/voices via the admin UI (separate commit).
- Image add over the STT-only baseline: ~150 MB.
services/tts.py — full rewrite:
- New voice-discovery layer scans /opt/piper-voices + /data/voices for
.onnx + .onnx.json pairs. /data wins over /opt for the same id so
admin-downloaded voices can override bundled defaults.
- Single PiperVoice kept warm; switches via _switch_voice() when the
user changes their voice_tts_voice setting.
- list_voices() returns metadata read from .onnx.json sidecars (label
derived from filename, language, quality, sample_rate).
- synthesise() uses piper's SynthesisConfig; converts kokoro-shaped
`speed` multiplier to piper's `length_scale` (1.0 / speed).
- `voice_blend` parameter accepted but ignored — piper has no blend
equivalent; first entry's voice is used if anything is passed.
- Dropped: HuggingFace commit-hash tracking (~80 lines), the daily
check_for_kokoro_updates task, voice-tensor blending math.
routes/voice.py:
- tts_backend reports "piper" in /api/voice/status.
- /api/voice/voices no longer requires tts_available() — even with
the active voice failed to load, the catalog still lets the user
pick a different one.
- Synthesise request body dropped the voice_blend field; speed and
voice still supported.
alembic 0047_reset_voice_tts_settings:
- Deletes any stored voice_tts_voice (kokoro IDs that don't map to
piper) and voice_tts_blend (no piper equivalent) rows. Both
re-default cleanly on next read.
frontend:
- VoiceBlendEntry type removed from api/client.ts.
- synthesiseSpeech() signature dropped the voiceBlend parameter.
- SettingsView.vue Voice Blend section removed entirely (slider,
preview, slot management). voice_tts_blend save path removed.
- Default voice id changed from "af_heart" to "en_US-amy-medium".
- VoiceEntry gains optional language/quality/sample_rate fields
from the richer piper sidecar metadata.
Voice paths remain lazily guarded — `VOICE_ENABLED=false` (default)
starts the app cleanly regardless of which TTS deps are present.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Prep prose (services/journal_prep.py):
- Emit explicit "WEATHER: none available — do NOT mention weather"
absent-marker so a small model can't invent partly-cloudy/temperature
prose when both configured locations have empty addresses.
- Replace negative-only system rule with positive-anchored guidance
forbidding weather/temp/precip mentions unless a numeric WEATHER
section is present; also bans echoing parenthetical labels verbatim.
- Reword overdue header to "(past their due date, still open — backlog,
not today's work)" and render lines as "was due <date>, N day(s)
overdue" with correct singular/plural. Supersedes the wording noted
in Fable task #159.
- Deterministic fabricated-weather reconciler: low-false-positive regex
detects fabricated weather phrasing; on trip with an empty section,
regenerate once with a corrective. Persistent fabrication logs ERROR
rather than mangling prose.
Journal route (routes/journal.py):
- Override message_count with len(messages) in _day_payload. The chat
path already does this; the journal path was hitting the
Conversation.to_dict() fallback to 0 because messages aren't
eager-loaded on that instance.
Tests:
- tests/test_journal_message_count.py — pins the model-level trap and
the override contract (3 cases).
- tests/test_journal_prep_hardening.py — 11 cases covering the
fabricated-weather reconciler and absent-marker rendering.
- tests/test_journal_prep_filtering.py — updated one stale assertion.
Tracks Fable task #171.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New endpoint manually triggers a consolidation pass for a single task.
Bypasses the auto_consolidate_tasks setting since the user is asking
explicitly. Returns the task with the freshly-written body and
consolidated_at timestamp.
Also un-aliases description and body in the create/update task routes
(was: description folded into body as legacy fallback). With separate
fields under the task-as-durable-record design, both flow through as
distinct kwargs to create_note / update_note.
create_note service accepts a new description kwarg and forwards it to the
Note constructor. PUT/PATCH/POST routes include description in the field
whitelist. update_note already passed **fields through setattr, so the new
column is reachable without touching that signature.
Two related gaps in the journal weather panel:
1. Saving locations via PUT /journal/config didn't trigger a weather
fetch, so newly-entered sites had no cache row (or a stale one) until
the user manually clicked the panel's refresh button. The panel
rendered "two sites with empty values" against pre-existing cache
rows that no longer matched what the user had configured.
2. get_cached_weather_rows returned every WeatherCache row for the user
regardless of whether the location was still in journal_config.
Briefing-era rows survived migration 0040 (which only deleted the
briefing_config setting, not the cache table) and showed up as
ghost tabs in the UI.
Changes:
- get_cached_weather_rows accepts an optional valid_keys filter; rows
whose location_key is not in the set are excluded.
- routes/journal.py:
- put_config kicks off a background refresh_location_cache for any
saved location with valid lat/lon.
- GET /weather and POST /weather/refresh both pass valid_keys derived
from the current config so orphaned rows don't surface.
- services/journal_prep.py filters the weather section to currently-
configured locations as well; uses a lazy import of get_journal_config
to avoid a cycle (journal_scheduler imports journal_prep).
153 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Structural fix for the "end before start" bug class observed on prod
2026-04-29. Bad data became inexpressible at the schema level instead
of getting trapped in defensive read-path filters.
The hotfix that landed earlier today (94b169f) is reverted by the
preceding revert commit; this commit supersedes it cleanly with a
proper data-model change.
## Schema (migration 0043)
- Add `duration_minutes INTEGER NULLABLE` column on `events`.
- CHECK constraint: ``duration_minutes IS NULL OR duration_minutes >= 0``.
- Backfill from existing `end_dt`:
- end_dt valid (end > start) → duration_minutes = total minutes
- end_dt == start → duration_minutes = 0 (zero-duration point)
- end_dt NULL or end_dt < start → duration_minutes = NULL
(the corrupt prod row collapses cleanly to a point event)
- Drop the `end_dt` column. The wire format is preserved — `to_dict()`
emits `end_dt` as a derived `start_dt + duration_minutes`. Existing
API consumers (Flutter app, web frontend, CalDAV sync) keep
receiving the same response shape; they just no longer have a way
to PUT a stored `end_dt` that disagrees with `start_dt`.
## Service layer
- `Event.end_dt` becomes a `@property`. Setting it would require a
setter we deliberately don't define — writes always go through
`duration_minutes`.
- `_normalize_duration` is the single source-of-truth for input
reduction. Accepts (start, end_dt, duration_minutes), returns the
canonical `duration_minutes`, raises `ValueError` for negative
durations, end-before-start, or end/duration disagreement.
- `create_event` and `update_event` accept either `end_dt` or
`duration_minutes` for ergonomic compat; both convert via
`_normalize_duration`. Update validates the post-update state when
the patch includes either.
- `list_events` filter is simpler now: a coarse SQL prefilter
(`start_dt <= date_to`) plus Python-side refinement using the
derived `end_dt`. Avoids Postgres-specific interval arithmetic in
the WHERE clause; refinement runs over a per-user result set so
there's no scan-cost concern at personal scale.
- Recurring-event expansion uses `event.duration_minutes` directly
instead of computing `end - start`. No more negative-timedelta
hazard.
## CalDAV sync (incoming + outgoing)
- `caldav_sync.py` (pull) and `calendar_sync.py` (Radicale upsert)
both convert iCal `DTEND` → `duration_minutes` on the way in.
Outbound iCal still emits `DTEND` as `start_dt + duration_minutes`
via the model's derived property. iCal interop is unchanged.
## Behavioral upgrade for `update_event`
Pure end_dt model: moving start past the existing end_dt would either
silently corrupt or hard-reject. Duration model: the duration is
preserved by default, so moving start slides the effective end
forward — which is what users mean when they "move" an event.
Explicit clear is still possible via `end_dt=None`.
## Tests
`tests/test_events_service.py`:
- 6 new `_normalize_duration` unit tests (sugar conversion, zero
duration valid as point event, end-before-start rejected, negative
duration rejected, inconsistent end+duration rejected, none → None)
- New behavioral test: `update_event` preserves duration when only
start_dt changes (sliding semantics)
- New: clearing `end_dt=None` on update collapses to point event
- New: list_events surfaces a point event in the upcoming window
- New: list_events excludes a timed event whose effective end has
already passed
- Existing mock-event helper updated to use `duration_minutes`
instead of stored `end_dt`.
44 event-related tests pass; ruff clean.
## Out of scope (separate task)
Fable #161 — `find_events_by_query` returning multiple matches and
silently picking matches[0]. The exact root cause of how event id=2
got mutated in the first place; orthogonal to the storage model.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
A prod event surfaced today with `start_dt=2026-05-01T12:00Z` and
`end_dt=2026-03-30T12:00Z` — end was 32 days BEFORE start, almost
certainly from an earlier tool-call mishap (Fable #161). The
list_events filter trusted the bogus end_dt and excluded the event
from every read path that hit the upcoming window, even though
start_dt was correctly in range. The event stayed visible in the
calendar grid (different range) but vanished from "Upcoming",
search, briefings, and journal prep events list.
This is the hotfix half of the response. The structural follow-up is
Fable #160 — replace end_dt with a duration column so invalid state
becomes inexpressible.
## A. Filter robustness in list_events
Treat `end_dt <= start_dt` as if no end_dt exists. The filter now
splits into two branches:
- valid duration: end_dt IS NOT NULL AND end_dt > start_dt AND
end_dt >= date_from
- no/invalid duration: (end_dt IS NULL OR end_dt <= start_dt) AND
start_dt >= date_from
Same change applied to the recurring-event expansion's `duration`
calculation, which was producing negative timedeltas for corrupted
rows and computing nonsensical occurrence end times.
## B. Write-side validation in create/update
`create_event` and `update_event` now raise ValueError when the
resulting state would have end_dt <= start_dt. Update validates
against the *post-update* state, not just the field being changed —
so pushing start_dt past an existing end_dt also fails loudly. Bad
data shouldn't be persistable from any write path.
Surfaced cleanly:
- Calendar tool wrappers (create_event_tool / update_event_tool)
catch ValueError and return `{success: false, error: ...}`, which
the model can read and self-correct.
- Route handlers (POST /api/events, PATCH /api/events/<id>) catch
and return HTTP 400 with the validator's message instead of
letting it bubble to a 500.
4 new tests in test_events_service.py:
- create rejects end before start
- create rejects equal start/end (zero duration)
- update validates the post-update state (start pushed past existing end)
- list_events surfaces an event whose end_dt is before its start_dt
34 event-related tests pass; ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The /api/journal/weather route was filtering out cache rows older than 24
hours via parse_weather_card_data, while journal_prep.py read the same
rows raw without freshness checking. Result: the daily prep referenced
"home" and "work" temperatures while the right-rail UI showed nothing —
two surfaces, same backing data, inconsistent visibility.
Two changes:
1. parse_weather_card_data no longer returns None for stale data.
WeatherCard already exposes fetched_at and gracefully hides
today_high / forecast fields when they're absent, so old data renders
with whatever fields the cached forecast still covers.
2. The /weather route opportunistically schedules a background refresh
for any cache row older than 4 hours. If the user's journal_config
has lat/lon for that location_key, the refresh runs and the next
page load gets fresh data; if no usable config, the refresh is a
silent no-op and the stale cache is still served.
This makes prep and UI consistent. It also self-heals over time — once
locations are configured, stale caches get refreshed on the next page
load instead of waiting indefinitely.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Removes the entire RSS feature surface — feeds, items, embeddings, reactions,
discussion-note flow, briefing news context, settings, env-vars, and DB
tables. Keeps the URL-generic article-reader (the read_article LLM tool)
under a clean module so the LLM can still fetch arbitrary article content
from URLs the user provides.
Backend:
- New services/article_fetcher.py — single source of trafilatura URL→text
- New services/tools/article.py — read_article tool (was nested under tools/rss)
- Delete services/rss.py, rss_classifier.py, rss_filtering.py, article_context.py
- Delete services/tools/rss.py
- Delete models/rss_feed.py (RssFeed, RssItem), models/rss_item_embedding.py
- services/embeddings.py: drop upsert/semantic_search/backfill RSS helpers
- services/llm.py: remove _build_briefing_article_context, briefing-conv branch,
ARTICLE_DISCUSS_SEED skip-RAG branch; drop get_rss_items / add_rss_feed from
the actions list
- services/generation_task.py: drop _maybe_save_article_discussion_note + caller
- routes/chat.py: drop /api/chat/from-article/<id> endpoint
- routes/journal.py: re-import via web.py refactor (article_fetcher path)
- services/tools/__init__.py: register `article`, drop `rss`
- services/tools/_registry.py: drop the requires=='rss' check
- app.py: drop backfill_rss_item_embeddings + backfill_rss_article_content tasks
- config.py: prose-only edit (no env var change — RSS env vars were never first-class)
Frontend:
- stores/settings.ts: drop rssEnabled
- SettingsView.vue: drop the RSS-classification mention
- api/client.ts: drop openArticleInChat (the from-article endpoint is gone)
Tests:
- Delete tests/test_rss_service.py, test_news_api.py, test_article_reading.py
Migration:
- 0042_drop_rss: DROP TABLE rss_item_embeddings, rss_item_reactions, rss_items,
rss_feeds; DELETE settings rows for rss_enabled / briefing_*_topics
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The journal UI was over-stripped earlier — weather panel, current-conditions
poll, and the upcoming-events sidebar were all dropped. Restored those (calls
the new /api/journal/weather, /api/journal/weather/current,
/api/journal/weather/refresh, /api/journal/weather/geocode endpoints).
Also: the daily-prep system message was rendering as flat text inside
ChatPanel because there's no .role-system bubble styling. Added a hideMessage
guard in ChatMessage so daily-prep system messages don't render in the chat
stream — they're already shown above as a structured prep card.
News / RSS reactions / article-discuss are intentionally NOT in the journal
(scoped out per user direction). The broader RSS infrastructure cleanup is
a separate, larger task.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add a refresh button (↻) to the weather section header in BriefingView so
users can manually re-fetch weather data (needed after deploying the hourly
precip changes to populate the cache). Update the existing POST
/api/briefing/weather/refresh endpoint to return full card data instead of
just location keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RSS is now off by default. When disabled:
- Scheduler skips RSS feed sync during compilation slot
- Briefing pipeline skips RSS item gathering
- RSS LLM tools (get_rss_items, add_rss_feed) are hidden
- API routes return empty results for feeds/news
- Frontend hides News nav link, RSS Feeds and News Preferences in settings
- Briefing view hides news sidebar section
Toggle in Settings > Briefing > RSS / News.
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>
Both the /news discuss button and the briefing discuss button now call a
shared seed_article_discussion() helper that stages the synthetic
read_article tool exchange and the conversational seed prompt — behavior
stays byte-identical across entry points. /news also auto-starts
generation so the chat screen lands on an in-flight stream.
First assistant reply in a seeded article conversation is persisted as a
Note (tags: article-summary + article topics) and backlinked via
rss_items.discussion_note_id, so the knowledge base stops being amnesiac
about articles the user has engaged with.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Discuss button on news cards was producing one-shot replies because
the model got the whole trafilatura blob dropped into history with a
canned "summarize and discuss this article" prompt — no length guard, no
prep, no invitation to converse. Large articles got silently truncated by
Ollama; small articles got a tepid reply.
This reworks discuss_article around a three-layer cache:
context_prepared → content_full → fresh trafilatura fetch
First click on a small article fetches once, writes through to both
caches, and passes the body straight into the synthetic read_article
tool-result. First click on a large article additionally runs a parallel
map step (services/article_context.py) that chunks the body on paragraph
boundaries, summarizes each ~8k chunk to ~300 words of dense factual
prose via the background model, and concatenates the summaries under
section headers — all pinned to num_ctx=16384 so the map step doesn't
itself fall victim to silent truncation. Repeat clicks on either path
skip straight to the chat turn.
The canned summary prompt is replaced with a conversational seed that
invites the user into an actual discussion rather than a one-shot
synopsis, matching the goal of "have a conversation about an article,
not just read it."
discuss_topic is intentionally left untouched — it's the multi-article
aggregation path and needs a separate rework. Follow-up task will decide
whether to retire it or rework it on the cached-context approach.
Closes task #106.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two related bugs where the server defaulted naive datetimes to UTC instead
of the configured user timezone, causing all-day events to land on the
previous day and briefings to "disappear" at UTC midnight.
- New services/tz.py helpers: get_user_tz, user_today, user_briefing_date
(the briefing day flips at 4am local to align with the compilation slot,
so the 00:00-04:00 local window still shows yesterday's briefing until
the new one is generated).
- calendar create/list/update tools now parse naive datetimes in the
user's TZ before converting to UTC for storage, and tool descriptions
tell the model to pass plain local dates.
- briefing_conversations.get_or_create_today_conversation and the
reset-today route use user_briefing_date so the in-progress briefing
doesn't get replaced at 19:00 NY / UTC midnight.
- _run_profile_closeout targets user-local "yesterday" for consistency.
Regression tests added for the TZ helpers and the calendar tool.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds 38 parametrized tests for the _should_think classifier covering the
explicit-override path, empty/whitespace content, short/medium/long length
boundaries, case-insensitive keyword matching, and a chatty-message negative
set. These pin the content-based semantics so future tweaks to the keyword
list or length thresholds surface regressions immediately instead of going
unnoticed behind subtle latency changes.
Also drops the `think=True` overrides from the briefing /discuss-article
and /discuss-topic entry points. With `"discuss"` added to _THINK_KEYWORDS,
those canned prompts trip the classifier naturally, so the overrides were
redundant — keeping a uniform "classifier is authoritative" rule makes the
code easier to reason about.
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>
Merge create_task into create_note (set status='todo' for tasks, omit
for notes), merge delete_task into delete_note, consolidate entity
tools (create/update_person → save_person, create/update_place →
save_place), rename get_note → read_note with clearer descriptions,
move calculate out of rag.py into utility.py, and extract shared
duplicate detection into check_duplicate() helper.
Updates all downstream references in generation_task.py, quick_capture.py,
ToolCallCard.vue, and WorkspaceView.vue.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Deletes ~760 lines of legacy briefing code: format_task, compute_task_hash,
upsert_task_snapshots, _gather_internal, _gather_weekly_review,
_llm_synthesise, and the unified prompt helpers. run_compilation and
run_slot_injection are now agentic-tool-use-loop only.
briefing_scheduler and user_profile migrated from the deleted helper to
services.llm.generate_completion (retry + keep_alive baked in).
routes/briefing.manual_trigger now persists agentic tool-call receipts
via _persist_agentic_messages (previously silently dropped them) and
adds POST /api/briefing/reset-today to wipe today's briefing messages.
BREAKING: briefing_mode setting no longer honored; no legacy fallback.
Co-Authored-By: Claude Opus 4.6 <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>
bvandeusen
Bryan Van Deusen