The scheduler loop enqueues the analysis task when a realtime campaign
completes; accelerated or cancelled campaigns and a missing analysis
model skip silently. The campaign markdown export appends the analysis
appendix (overall, problems, narratives, suggestions) when a completed
analysis exists.
Add the analysis role's execution path: a two-phase orchestration
(per-scenario diagnosis gathered in parallel, then a synthesis pass)
that reads the existing campaign report aggregation plus capped failure
samples, validates the LLM's JSON against the report schema, and strips
fabricated run/scenario references before persisting. Results upsert one
row per campaign (generating/completed/failed) with the model config
snapshot; GET/POST /api/campaigns/{id}/analysis expose the state machine,
guarding non-terminal campaigns and missing analysis models with 400s.
Campaigns can pin an analysis model config instead of following the
global analysis default. Creation validates the referenced config
exists (400 otherwise); the create form offers enabled chat configs
with the global default as the fallback option.
build_campaign_timeline flattens a campaign's child Runs into offset-sorted
per-Run entries (distinct from the report's 12-bucket aggregation), reusing a
shared _run_window_offset口径 so both views place a run identically. Exposes
GET /campaigns/{id}/timeline and the api.ts type/call. (v0.6 ticket 06)
report.py mixed DB-reading generation with string formatting: the four
render_*_report(run_id, session) functions each re-fetched via
generate_report, so the HTML/Markdown/JSON formatting was welded to storage
and could not be unit-tested from a plain dict. Extract the formatting into a
new pure report_render module whose renderers take the already-built report
dict (no session, no storage import). Migrate every caller to generate-then-
render, delete the old coupled renderers with no back-compat shim, and drop
the _aggregate_runs middle-man alias in favour of metrics.aggregate_runs.
Embed compact progress (completed/planned total + overall pass_rate,
reusing the report's aggregation) into GET /campaigns so the list drops
its N+1 report fetch. Poll list and open report drawer every 5s while the
tab is active and a campaign is still running. Show scenario version and
trigger source tags in the child-run drill-down.
Add generate_campaign_report: a pure aggregator over a campaign's child Runs
producing a time-trend axis (Runs bucketed by service-window position) and a
capability-summary axis (grouped by scenario), each carrying pass_rate /
availability / latency. pass_rate keeps the single-Run case-level meaning and
counts execution failures as 0.0 (ADR-0002); time_scale only places Runs into
window-time buckets and never alters any figure. Engine summary now records
avg_latency_ms to feed the latency axis.
Expose GET /api/campaigns/{id}/report (structured) and .../report/markdown
(reusing the existing Markdown export path). Adds "可用性/Availability" to the
domain glossary.
Add a thin async loop (run_campaign_loop) that ticks on real wall-clock time,
maps elapsed×time_scale to a window offset via the pure decide_schedule, spawns
due child Runs, and marks the campaign COMPLETED at window end. All authority
lives in the DB (started_at, spawned_indices, status), so the app lifespan can
resume every RUNNING campaign on startup without double-spawning and stop all
loops gracefully on shutdown. A failing plan entry is skipped and recorded
rather than wedging the campaign.
Creating a campaign now starts its loop; POST /api/campaigns/{id}/cancel stops
further spawning (completed child Runs are kept); GET /api/campaigns/{id}
reports live progress (window offset, spawned/completed Run counts).
