feat(4636): rule pull-through per surface, and per-rule usage with confirmed lessons on list_rules
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retrieval_telemetry gains rule_usage.by_source, the twin of usage.by_source: distinct rules each surface showed, how many an agent opened, and a ratio that is null on a bulk delivery. list_rules(with_usage=True, days=N) adds each rule's usage, windowed, and how many lessons are confirmed instances of it, so linked and unlinked rules can be compared through MCP. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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@@ -10,13 +10,16 @@ preview-style warning.
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta, timezone
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from grimoire.mcp._context import current_user_id
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from grimoire.services import dedup as dedup_svc
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from grimoire.services import moments as moments_svc
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from grimoire.services import rulebooks as rulebooks_svc
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from grimoire.services import trash as trash_svc
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from grimoire.services.lesson_rules import confirmed_lesson_counts
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from grimoire.services.rule_usage import (
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record_rule_outcome, record_rule_pulled,
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empty_rule_usage, record_rule_outcome, record_rule_pulled, usage_for_rules,
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)
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from grimoire.services import moment_delivery
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@@ -204,6 +207,7 @@ def _rule_summary(r, moments: list[str] | None = None) -> dict:
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async def list_rules(
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rulebook_id: int = 0, topic_id: int = 0, project_id: int = 0,
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with_usage: bool = False, days: int = 0,
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) -> dict:
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"""List rules — filter by rulebook, topic, and/or project.
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@@ -213,9 +217,21 @@ async def list_rules(
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project_id: 0 = no filter; positive = that project's OWN rules.
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Global rules apply to every project, so they are listed by
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rulebook or topic (or unfiltered), not under each project.
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with_usage: add each rule's `usage` and `confirmed_lessons`. Off by
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default: a listing is for applying rules, and this is for judging
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them.
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days: with `with_usage`, count only the last N days; 0 = everything
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recorded.
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rulebook_id and topic_id AND-combine; project_id lists a project's rules
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on its own. Ownership-scoped.
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`usage` is the rule's own telemetry: `surfaced_count` (a ranked surface
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chose it), `ambient_count` (a bulk delivery carried it), `pull_count`,
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`applied_count`, `departed_count` and the latest of each.
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`confirmed_lessons` counts the lessons judged instances of the rule. Read
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together they answer whether rules that situations keep proving are the
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ones that surface and get opened.
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"""
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uid = current_user_id()
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rows = await rulebooks_svc.list_rules(
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@@ -224,12 +240,20 @@ async def list_rules(
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topic_id=topic_id or None,
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project_id=project_id or None,
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)
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ids = [r.id for r in rows]
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# One batched read for the page, not one per row.
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mounted = await rulebooks_svc.list_rule_moments([r.id for r in rows])
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return {
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"rules": [_rule_summary(r, mounted.get(r.id)) for r in rows],
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"total": len(rows),
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}
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mounted = await rulebooks_svc.list_rule_moments(ids)
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rules = [_rule_summary(r, mounted.get(r.id)) for r in rows]
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if with_usage:
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since = (
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datetime.now(timezone.utc) - timedelta(days=days) if days > 0 else None
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)
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usage = await usage_for_rules(ids, since=since)
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lessons = await confirmed_lesson_counts(uid, ids)
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for row in rules:
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row["usage"] = usage.get(row["id"], empty_rule_usage())
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row["confirmed_lessons"] = lessons.get(row["id"], 0)
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return {"rules": rules, "total": len(rows)}
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async def get_rule(rule_id: int) -> dict:
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@@ -509,6 +509,15 @@ async def retrieval_telemetry(
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Read it against `sources["write_path_rule"]`, which declines the large
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majority of its calls like any other surface.
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`rule_usage.by_source` is the per-surface split, the twin of
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`usage.by_source`: for each surface that showed rules, `rules_surfaced`
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(distinct rules), `rules_pulled` (how many of those an agent opened in the
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window) and `pull_through`, null on an ambient surface. It is the readout
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that says which arm — the prompt arm, the tool arm, a lesson pointing at
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its rule — earns its lines. The same upper bound as the note split: a rule
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two surfaces showed and an agent opened once counts for both.
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`by_source_failed: true` means that one query failed.
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`system_usage` — the RULINGS arm, from `system_usage_events`: how
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often an area's rulings were shown because a command (`rulings_pre_tool`)
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or an edit (`rulings_write_path`) touched its files, how often a System
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@@ -324,6 +324,34 @@ async def lessons_for_rule(user_id: int, rule_id: int) -> list[dict]:
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]
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async def confirmed_lesson_counts(user_id: int, rule_ids) -> dict[int, int]:
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"""{rule_id: confirmed lessons the caller can read}, for a list of rules.
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`lessons_for_rule` in bulk, counted rather than listed: one GROUP BY, so a
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rule list can say which rules are proven by situations without a query
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per row. Rules with none read 0.
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"""
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from grimoire.services.access import readable_notes_clause
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ids = [int(i) for i in rule_ids or []]
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out = {i: 0 for i in ids}
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if not ids:
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return out
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async with async_session() as session:
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rows = (await session.execute(
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select(LessonRuleLink.rule_id, func.count())
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.join(Note, Note.id == LessonRuleLink.lesson_id)
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.where(LessonRuleLink.rule_id.in_(ids))
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.where(LessonRuleLink.state == CONFIRMED)
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.where(Note.deleted_at.is_(None))
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.where(readable_notes_clause(user_id))
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.group_by(LessonRuleLink.rule_id)
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)).all()
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for rule_id, n in rows:
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out[int(rule_id)] = int(n)
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return out
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async def judgments_for_lessons(lesson_ids) -> dict[int, dict]:
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"""{lesson_id: {"rule_judgment": linked|no_rule|unjudged, "no_rule"?}}.
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@@ -1109,7 +1109,7 @@ async def retrieval_summary(
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# Assigned inside the try below; named here so the readout can tell
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# "this query failed" from "this window has no rows".
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by_source_rows = None
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by_source_rows = rule_by_source_rows = None
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rule_rows = None
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distinct_rules_surfaced = distinct_rules_pulled = 0
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# None means the coverage read did not happen — distinct from a table with
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@@ -1426,6 +1426,53 @@ async def retrieval_summary(
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rule_rows = None
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distinct_rules_surfaced = distinct_rules_pulled = 0
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distinct_rules_acted = 0
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# The note block's per-source join, at the rule grain. The
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# top-level rule ratio blends every arm — the prompt arm, the tool
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# arm, a lesson pointing at its rule — and "which of them earns its
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# lines" is the question each arm's bar is tuned against. Its own
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# guard, for the reason the note join has one.
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try:
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rule_pulled_ids = (
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select(RuleUsageEvent.rule_id)
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.where(
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RuleUsageEvent.created_at >= since,
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RuleUsageEvent.user_id == user_id,
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RuleUsageEvent.event == RULE_PULLED,
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RuleUsageEvent.source.startswith("mcp_", autoescape=True),
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)
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.distinct()
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.subquery()
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)
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rule_surfaced_pairs = (
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select(RuleUsageEvent.source, RuleUsageEvent.rule_id)
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.where(
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RuleUsageEvent.created_at >= since,
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RuleUsageEvent.user_id == user_id,
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RuleUsageEvent.event == RULE_SURFACED,
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)
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.distinct()
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.subquery()
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)
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rule_by_source_rows = (
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await session.execute(
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select(
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rule_surfaced_pairs.c.source,
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func.count().label("rules_surfaced"),
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func.count(rule_pulled_ids.c.rule_id).label("rules_pulled"),
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)
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.select_from(
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rule_surfaced_pairs.outerjoin(
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rule_pulled_ids,
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rule_pulled_ids.c.rule_id == rule_surfaced_pairs.c.rule_id,
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)
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)
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.group_by(rule_surfaced_pairs.c.source)
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)
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).all()
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except Exception:
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logger.warning("per-source rule pull-through read failed", exc_info=True)
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rule_by_source_rows = None
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except Exception:
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logger.warning("retrieval summary read failed", exc_info=True)
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out["read_failed"] = True
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@@ -1596,6 +1643,28 @@ async def retrieval_summary(
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round(rule_usage["pulled_by_agent"] / rule_usage["surfaced"], 4)
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if rule_usage["surfaced"] else None
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)
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# Per surface, the note block's shape and its caveat: a pull records the
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# door, not the surface that led to it, so a rule two arms surfaced and an
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# agent opened once counts as pulled for both. An upper bound — a surface
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# reading near zero here is not being flattered.
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if rule_by_source_rows is None:
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rule_usage["by_source"] = {}
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rule_usage["by_source_failed"] = True
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else:
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rule_usage["by_source"] = {
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source: {
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"rules_surfaced": int(n_surfaced or 0),
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"rules_pulled": int(n_pulled or 0),
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# None on an ambient surface, as for notes: nothing chose
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# those rules, so the ratio would be misread.
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"pull_through": (
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None if is_ambient(source) or not n_surfaced
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else round(int(n_pulled or 0) / int(n_surfaced), 4)
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),
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"ambient": is_ambient(source),
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}
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for source, n_surfaced, n_pulled in rule_by_source_rows
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}
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rule_usage.update(_coverage((rule_complete or {}).get("*"), since))
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out["rule_usage"] = rule_usage
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out["system_usage"] = await _system_usage(user_id, since)
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@@ -59,8 +59,9 @@ as short and as slow-moving as possible.
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from __future__ import annotations
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import logging
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from datetime import datetime
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from sqlalchemy import case, func, select
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from sqlalchemy import case, func, select, true
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from grimoire.models import async_session
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from grimoire.models.base import iso
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@@ -333,13 +334,18 @@ def empty_rule_usage() -> dict:
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}
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async def usage_for_rules(rule_ids: list[int]) -> dict[int, dict]:
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async def usage_for_rules(
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rule_ids: list[int], since: datetime | None = None,
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) -> dict[int, dict]:
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"""Aggregate usage for a set of rules: {rule_id: {counts + timestamps}}.
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One GROUP BY for the whole page rather than a query per row — this feeds a
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list view, so the per-row shape would be N+1 by construction. Rules with no
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events come back with `empty_rule_usage()`, so the caller never has to tell
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"no events" from "not in the result".
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`since` limits the counts to events at or after it, so two windows can be
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compared; without it they cover everything recorded.
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"""
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ids = [int(r) for r in rule_ids]
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out: dict[int, dict] = {rid: empty_rule_usage() for rid in ids}
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@@ -370,6 +376,10 @@ async def usage_for_rules(rule_ids: list[int]) -> dict[int, dict]:
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ambient,
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)
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.where(RuleUsageEvent.rule_id.in_(ids))
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.where(
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RuleUsageEvent.created_at >= since
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if since is not None else true()
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)
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.group_by(
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RuleUsageEvent.rule_id,
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RuleUsageEvent.event,
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