Two-in-one cleanup motivated by the chat hang in dev 2026-05-22.
The crash root cause from the guarded-task traceback:
UnboundLocalError: cannot access local variable 'get_setting'
where it is not associated with a value
File generation_task.py:257, in run_generation
think = (await get_setting(user_id, 'think_enabled', 'false'))...
generation_task.py imports get_setting at module top, but a later
'if voice_mode: from ... import get_setting' block scopes it as a
function-local. When voice_mode=False the local import never runs,
but Python had already flagged get_setting as local for the entire
body — the think_enabled read at line 257 hit UnboundLocalError.
The line itself was dead-weight anyway. With the conversation+curator
architecture: chat ships tools=[] (think on a no-tools pass is pure
latency cost; nothing for the model to reason ABOUT in tool-call
terms), and the curator hardcodes think=False already. The user
setting was a holdover from before the architecture pivot. Removing
it entirely is cleaner than fixing the scoping bug to preserve a
toggle nobody should be using:
- generation_task.py: think hardcoded False. Removed the get_setting
call (which fixes the UnboundLocalError as a side effect).
- SettingsView.vue: dropped the Enable model thinking checkbox, the
thinkEnabled / savingThinkEnabled refs, the saveThinkEnabled
function, and the think_enabled load step.
- Migration 0050: DELETE FROM settings WHERE key='think_enabled'
to clean up any stored rows.
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>
Two architectural bugs in the conversation+curator rollout that
explain the no-response chat in dev:
1. Journal system prompt still instructed tool calls.
JOURNAL_CALIBRATION instructed the model to CALL record_moment,
search_notes, save_person, etc. — but the chat surface ships tools=[]
per the new architecture. The model received contradictory orders
('use these tools' + 'you have no tools') and produced either empty
output or tool-call-shaped text that gets stripped to empty content,
surfacing as status=error or stuck status=generating messages.
Replaced with a chat-only calibration: ~25 lines focused on tone,
length, anti-coaching, and the load-bearing rule 'never claim to
have done anything for the user' (the curator handles capture
silently and separately). JOURNAL_PERSONA also rewritten to drop
the 'use tools to act on their behalf' line.
2. Pre-warm warmed Config.OLLAMA_MODEL ahead of user's real choice.
_pull_model(Config.OLLAMA_MODEL, warm=True) at boot pushed the
system default (qwen3:latest) into VRAM before _warm_user_models()
ran for each user's actual default_model setting. On a single-GPU
setup the second warm could swap the first out — so the user's
chat model wasn't necessarily resident when their first message
landed. Now we just pull the supporting models without warming
them; only user-configured chat models get warm.
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>
Adds an empirical surface for evaluating model swaps. One row per
assistant turn captures: model, think_enabled, tools_available,
tools_attempted, tools_succeeded, tools_failed (with error details
as JSONB). Without this, judging whether a new model "actually fires
record_moment when it should" relies on anecdote across user-reported
sessions. With it, the data is queryable directly.
Pieces:
- Migration 0046: generation_tool_log table with user_created and
per-conversation indexes.
- Model: SQLAlchemy GenerationToolLog with to_dict() for plain-dict
consumption outside session scope.
- Service: log_tool_outcomes() normalizes the in-app tool-call shape
(function/result/status) into the split buckets and persists. It
catches its own exceptions — telemetry failure must NEVER affect
the user-facing generation flow. recent_logs() helper for read.
- Integration in run_generation: called once per turn right after
log_generation, fire-and-forget.
- Tests: pure-normalization unit tests using a stub session — no DB
needed in CI. Cover the success/error split, the empty-tool-calls
case, the exception-swallowing contract, and the success=False
edge case where status incorrectly says "success".
No UI for the telemetry yet — internal infrastructure (the operator
is the consumer, not the journal user), which the FabledRulebook
"no UI no ship" explicitly excepts. Query via psql or extend the
Fable MCP later if direct shell access gets tiresome.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The chat generation pipeline previously forced think=True unconditionally
to match qwen3's combined think+tools template, locking the system into
that model family. Bench data (2026-05-21, qwen3:30b-a3b/qwen3:32b on
CPU) showed thinking adds 1-2 minutes per turn for unclear quality
benefit — qwen3:30b-a3b even produced more rambling with think on.
This decouples think from the model family by reading a per-user
`think_enabled` setting (default `false`). Non-qwen3 models can now run
through the same pipeline without the silent-generation failure mode
that content-gated thinking would have caused — they just don't think.
qwen3 users who still want thinking can opt in via the Settings UI.
Settings UI:
- New "Enable model thinking" checkbox in General → Assistant section.
- Help text explains the default-off rationale and when to opt in.
- Persists via the existing settings API; no schema migration needed
(Setting is key/value text).
Telemetry to confirm whether this regresses tool-call reliability on
qwen3 (the current model) is in a follow-up commit (generation_tool_log).
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>
Both export paths emit pin_kind and pin_label per note_version row.
Restore reads them via .get() so backups predating the schema still
import cleanly (defaults to None → rolling).
BackgroundScheduler with a single CronTrigger fires scan_all_users_for
_auto_pins via asyncio.run_coroutine_threadsafe (mirrors the journal-
scheduler pattern). Wired into app startup/shutdown alongside the other
schedulers.
_promote_stable_versions_for_note is the pure-function core: walks
versions chronologically and pins any with a >= AUTO_PIN_STABILITY_DAYS
(2-day) gap to the next version (or to now, for the latest). Auto-
generated label describes the stability window.
_scan_one_note loads versions for one note, runs the promotion, commits
mutations to the attached rows, then calls prune_auto_pins to cap the
auto bucket. scan_user_for_auto_pins fans out across the user's notes;
scan_all_users_for_auto_pins is the top-level entrypoint for the cron.
Per-note and per-user errors are caught and logged.
Auto-pinned versions live in their own bucket with MAX_AUTO_PINS=25 cap.
The scan job calls this after each note's promotions complete; the
oldest auto-pinned rows are dropped past the cap. Manual pins and
rolling rows are untouched.
pin_version sets pin_kind='manual' and pin_label on the target row.
Accepts already-pinned rows (promotes auto→manual, updates label).
Labels are capped at PIN_LABEL_MAX_LEN=500 chars; longer values raise
ValueError before any DB access.
unpin_version clears both fields, downgrading the row to rolling. Does
NOT delete — if the row is past the rolling FIFO depth, the next
autosave's prune will drop it.
The DELETE inside create_version now filters pin_kind IS NULL so pinned
rows (auto or manual) aren't counted toward MAX_VERSIONS=50 and aren't
candidates for deletion. Pinned versions live indefinitely regardless
of how heavy rolling autosave traffic gets on the same note.
Spec: docs/superpowers/specs/2026-05-13-note-version-pinning-design.md
- pin_kind: NULL=rolling, 'auto'=stability-scan, 'manual'=user-declared.
- pin_label: NULL for rolling; auto-generated for 'auto'; user-supplied
string for 'manual' (may be NULL).
No backfill — every existing row stays rolling. The daily auto-pin scan
will catch up on the first run after deploy.
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.
log_work description now mentions that logs feed the task's auto-summary,
nudging the LLM toward specific log content (commands, decisions, failures)
rather than vague entries.
create_note description gains a runbook-shape clause: code blocks, numbered
procedures, and explicit 'save this as a note/runbook' signals should
spawn standalone notes. Task-specific work-in-progress routes to log_work
instead.
create_note tool:
- New 'description' parameter accepted and forwarded to the service.
- When status is set (creating a task), 'body' is dropped before the
service call. Task bodies are owned by the consolidation pipeline.
update_note tool:
- New 'description' parameter; routed through update_fields.
- When the resolved target has is_task=True and 'body' is in the
arguments, the call errors with a message nudging toward log_work or
description. Knowledge notes are unaffected.
HTTP routes (POST/PATCH/PUT /api/notes) accept body freely — the
restriction is only at the LLM tool layer.
log_work tool now invokes maybe_consolidate(reason='log_added') after a
successful create_log. The gate inside the consolidation service handles
threshold + setting checks.
update_note service snapshots old_status before mutation and fires
maybe_consolidate(reason='task_closed') when the status transitions into
'done' or 'cancelled'. Re-saving an already-terminal status doesn't
retrigger — only transitions count.
consolidate_task reads the task title, description (read-only context),
and chronological work logs; builds a prompt via _build_consolidation_prompt;
calls generate_completion with the user's background_model setting; on a
non-empty result, writes back to Note.body, stamps consolidated_at, and
re-runs the embedding pipeline.
Errors are caught and logged. LLM failures leave body untouched so the
next trigger retries cleanly. Per-task asyncio lock prevents simultaneous
passes for the same task.
New services/consolidation.py module with maybe_consolidate() — the
debounced trigger gate. Two reasons:
- log_added: gated by DEFAULT_LOG_THRESHOLD (3) counted since the task's
consolidated_at timestamp.
- task_closed: bypasses the count gate; fires whenever status flips to
done/cancelled.
Both reasons gated by the auto_consolidate_tasks user setting (default
on). Per-task asyncio.Lock prevents two simultaneous passes for the same
task. consolidate_task is a stub here — full implementation in the next
commit.
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.
Spec: docs/superpowers/specs/2026-05-13-task-as-durable-record-design.md
- description: user-stated goal / initial context for tasks (NULL for
knowledge notes).
- consolidated_at: timestamp of the most recent auto-summary pass (NULL
until first consolidation).
- Migration 0044 backfills description from body for existing rows where
status IS NOT NULL (i.e. tasks). Body left in place; first consolidation
pass will overwrite it.
CI surfaced three issues:
- 'famous supply project' didn't substring-match 'Famous-Supply Work topics'
because the trailing filler word 'project' blocked the substring tier.
Strip {project, projects} from the query before the substring check.
- SequenceMatcher fallback against `combined` (title + description +
summary) diluted ratios to ~0.5 for plausible matches. Use title
directly; the 0.70 tier already handles description/summary mentions.
- Test patches used patch.object on a consumer module where
list_projects is imported locally — patch the source module instead.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Root cause of the 2026-04-29 dentist-appointment incident: the model
called update_event(query="Appointment") when two events had
"Appointment" in their titles. find_events_by_query returned both,
upcoming-first ordered by start_dt — matches[0] was id=2 (a stale
pre-existing event with garbage end_dt), not id=15 (the one the user
just created via the journal flow). update_event_tool silently took
matches[0] and mutated the wrong event.
Fix: a new resolver helper `_resolve_event_for_action` funnels both
update_event_tool and delete_event_tool through one disambiguation
path. Lookup precedence:
- `event_id` → exact get_event lookup, no query at all
- `query` matching exactly one event → proceed
- `query` matching zero → return success=False, "no event found"
- `query` matching 2+ events → return success=False with a
`candidates` array of {id, title, start_dt, location} so the
model can pick one and call again with `event_id`
The candidates list is capped at 8 to keep the model's context tight.
The error message names the count and the next-step ("pass event_id
or refine the query") so the model can self-correct in one turn.
For delete_event, the disambiguation is even more important — the
silent-matches[0] path would have deleted the wrong event outright
rather than just mutating it. The tool description leans into that:
"Deleting the wrong event is a costly user error; never guess."
Tool surface change: `query` and `event_id` are now both optional;
the tool errors clearly when neither is supplied. The model already
knows id values from prior tool results (returned in `data.id`),
which is the natural feeder for the disambiguation flow.
5 new tests in test_calendar_tool_tz.py cover:
- ambiguous query → success=False with candidate list, no mutation
- event_id supplied → bypasses query lookup entirely
- non-existent event_id → clear "no event found" error
- neither identifier → "query or event_id required" error
- same disambiguation enforced for delete_event_tool
46 calendar/events tests pass; ruff clean.
Closes Fable #161.
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>
bvandeusen