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Keep view mode across tabs and speed up recommendation pipeline

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b17301
2026-04-27 17:29:24 +09:00
parent 354d149245
commit b2a3802dff
7 changed files with 504 additions and 68 deletions
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templates/annual_summary.html
+42
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@@ -142,6 +142,48 @@
line-height: 1.18;
}
body[data-view-mode="dual"] .summary-panel {
grid-template-columns: minmax(250px, 300px) minmax(0, 1fr);
gap: 12px;
min-height: calc(100vh - 150px);
}
body[data-view-mode="dual"] .summary-overview {
gap: 10px;
}
body[data-view-mode="dual"] .metric-grid {
grid-template-columns: 1fr;
gap: 8px;
}
body[data-view-mode="dual"] .summary-overview .stat-card {
min-height: 64px;
padding: 10px 11px;
}
body[data-view-mode="dual"] .summary-overview .stat-card .value {
font-size: 18px;
}
body[data-view-mode="dual"] .chart-stack {
grid-template-columns: repeat(2, minmax(0, 1fr));
gap: 12px;
align-items: stretch;
min-height: 100%;
}
body[data-view-mode="dual"] .chart-box {
padding: 12px 12px 14px;
min-height: 100%;
}
body[data-view-mode="dual"] .chart-svg,
body[data-view-mode="dual"] .expense-chart-svg {
aspect-ratio: 1560 / 620;
min-height: 360px;
}
@media (max-width: 1000px) {
.summary-layout,
.summary-panel,
templates/base.html
+135 -1
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@@ -20,6 +20,9 @@
--page-gutter: clamp(14px, 1.8vw, 24px);
--panel-pad: clamp(16px, 1.4vw, 20px);
--page-frame-width: min(1520px, calc(100vw - (var(--page-gutter) * 2)));
--page-frame-width-dual: min(2560px, calc(100vw - (var(--page-gutter) * 2)));
--page-frame-width-single: min(1520px, calc(100vw - (var(--page-gutter) * 2)));
--status-widget-height: 34px;
}
* {
@@ -48,11 +51,28 @@
padding: var(--page-gutter);
}
body[data-view-mode="single"] {
--page-frame-width: var(--page-frame-width-single);
}
body[data-view-mode="dual"] {
--page-frame-width: var(--page-frame-width-dual);
--page-gutter: clamp(4px, 0.45vw, 10px);
--panel-pad: clamp(12px, 1vw, 16px);
}
.page {
width: min(100%, var(--page-frame-width));
margin: 0 auto;
display: grid;
gap: var(--page-gutter);
min-height: calc(100vh - (var(--page-gutter) * 2));
align-content: start;
}
body[data-view-mode="dual"] .page {
width: calc(100vw - (var(--page-gutter) * 2));
max-width: none;
}
.page > * {
@@ -436,13 +456,81 @@
border-radius: 999px;
background: rgba(248, 250, 252, 0.92);
box-shadow: none;
padding: 6px 10px;
min-height: var(--status-widget-height);
padding: 0 10px;
display: inline-flex;
align-items: center;
gap: 8px;
backdrop-filter: blur(6px);
}
.view-mode-switch {
position: relative;
display: inline-flex;
align-items: center;
justify-content: center;
width: 80px;
height: var(--status-widget-height);
min-height: var(--status-widget-height);
border-radius: 999px;
border: 1px solid var(--line);
background: #eef2f6;
color: #1f2937;
font-size: 11px;
font-weight: 800;
letter-spacing: -0.01em;
box-shadow: none;
padding: 0;
overflow: hidden;
cursor: pointer;
transition: background-color 0.16s ease, border-color 0.16s ease;
}
.view-mode-switch:hover {
transform: none;
background: #e7ecf2;
}
.view-mode-switch .label {
position: absolute;
top: 50%;
left: 3px;
width: 36px;
transform: translateY(-50%);
text-align: center;
color: #ffffff;
z-index: 2;
pointer-events: none;
user-select: none;
transition: left 0.2s ease;
}
.view-mode-switch::before {
content: "";
position: absolute;
top: 3px;
left: 3px;
width: 36px;
height: calc(var(--status-widget-height) - 8px);
border-radius: 999px;
background: #111827;
box-shadow: 0 6px 12px rgba(15, 23, 42, 0.18);
transition: transform 0.2s ease;
z-index: 1;
}
.view-mode-switch[data-mode="dual"]::before {
transform: translateX(38px);
}
.view-mode-switch[data-mode="dual"] .label {
left: 41px;
}
.view-mode-switch[data-mode="single"]::before {
transform: translateX(0);
}
.sync-status-head {
display: inline-flex;
align-items: center;
@@ -530,6 +618,9 @@
<a href="/annual-summary" class="{% if request.url.path == '/annual-summary' %}active{% endif %}">연도별 수익/비용</a>
<a href="/wehago-compare" class="{% if request.url.path == '/wehago-compare' %}active{% endif %}">전표비교</a>
<div class="nav-spacer"></div>
<button type="button" class="view-mode-switch" id="viewModeSwitch" data-mode="dual" aria-label="화면 구성 전환">
<span class="label" id="viewModeSwitchLabel">듀얼</span>
</button>
<aside
class="sync-status"
id="syncStatusWidget"
@@ -561,6 +652,49 @@
</div>
{% block script %}{% endblock %}
<script>
(() => {
const body = document.body;
const switchButton = document.getElementById("viewModeSwitch");
const switchLabel = document.getElementById("viewModeSwitchLabel");
if (!body || !switchButton || !switchLabel) return;
const MODE_KEY = "intranet-view-mode";
const SOURCE_KEY = "intranet-view-mode-source";
const computeAutoMode = () => {
const width = window.innerWidth || 0;
const height = window.innerHeight || 1;
const ratio = width / Math.max(height, 1);
return (width >= 1450 || ratio >= 1.7) ? "dual" : "single";
};
const applyMode = (mode) => {
const normalized = mode === "single" ? "single" : "dual";
body.dataset.viewMode = normalized;
switchButton.dataset.mode = normalized;
switchButton.setAttribute("aria-pressed", normalized === "single" ? "true" : "false");
switchLabel.textContent = normalized === "single" ? "싱글" : "듀얼";
};
const savedMode = window.localStorage.getItem(MODE_KEY);
const savedSource = window.localStorage.getItem(SOURCE_KEY);
if ((savedMode === "single" || savedMode === "dual") && savedSource === "manual") {
applyMode(savedMode);
} else {
const autoMode = computeAutoMode();
applyMode(autoMode);
window.localStorage.setItem(MODE_KEY, autoMode);
window.localStorage.setItem(SOURCE_KEY, "auto");
}
switchButton.addEventListener("click", () => {
const nextMode = body.dataset.viewMode === "single" ? "dual" : "single";
applyMode(nextMode);
window.localStorage.setItem(MODE_KEY, nextMode);
window.localStorage.setItem(SOURCE_KEY, "manual");
});
})();
(() => {
const widget = document.getElementById("syncStatusWidget");
if (!widget) return;
templates/dashboard.html
+40
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@@ -182,6 +182,46 @@
display: block;
}
body[data-view-mode="dual"] .dashboard-layout {
grid-template-columns: minmax(250px, 300px) minmax(0, 1fr);
gap: 12px;
min-height: calc(100vh - 150px);
}
body[data-view-mode="dual"] .dashboard-status-grid {
grid-template-columns: 1fr;
gap: 8px;
}
body[data-view-mode="dual"] .dashboard-status-grid .stat-card {
min-height: 64px;
padding: 10px 11px;
}
body[data-view-mode="dual"] .dashboard-status-grid .stat-card .value {
font-size: clamp(16px, 1.15vw, 24px);
}
body[data-view-mode="dual"] .dashboard-chart-stack {
grid-template-columns: repeat(2, minmax(0, 1fr));
grid-template-rows: minmax(0, 1fr);
gap: 12px;
min-height: 100%;
}
body[data-view-mode="dual"] .chart-panel {
gap: 8px;
}
body[data-view-mode="dual"] .chart-shell {
padding: 10px 12px 12px;
min-height: 100%;
}
body[data-view-mode="dual"] .chart-svg {
min-height: 320px;
}
@media (max-width: 1200px) {
.dashboard-layout {
grid-template-columns: 1fr;
templates/projects.html
+32 -4
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@@ -123,10 +123,10 @@
color: #363b44;
font-size: 13px;
font-weight: 700;
background: rgba(255, 255, 255, 0.9);
border: 1px solid var(--line);
border-radius: 10px;
padding: 6px 10px;
background: transparent;
border: 0;
border-radius: 0;
padding: 0;
line-height: 1.3;
}
@@ -2298,6 +2298,34 @@
font-size: 12px;
}
body[data-view-mode="dual"] .overview-row {
grid-template-columns: minmax(260px, 300px) minmax(0, 1fr);
gap: 12px;
}
body[data-view-mode="dual"] .metric-grid {
grid-template-columns: 1fr;
gap: 8px;
}
body[data-view-mode="dual"] .metric-note-grid {
gap: 10px;
}
body[data-view-mode="dual"] .chart-stage {
padding: 12px;
}
body[data-view-mode="dual"] .chart-svg {
min-height: 300px;
}
body[data-view-mode="dual"] .analysis-shell,
body[data-view-mode="dual"] .analysis-main,
body[data-view-mode="dual"] .analysis-content {
gap: 12px;
}
@media (max-width: 1180px) {
.overview-row,
.analysis-toolbar,
templates/wehago_compare.html
+36
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@@ -648,6 +648,40 @@
font-size: 13px;
}
body[data-view-mode="dual"] .voucher-page:has(#pairRecommendPanel.active) {
grid-template-columns: minmax(0, 1fr) minmax(0, 1fr);
align-items: start;
column-gap: 12px;
}
body[data-view-mode="dual"] .voucher-page:has(#pairRecommendPanel.active) > .voucher-topbar {
grid-column: 1 / -1;
}
body[data-view-mode="dual"] .voucher-page:has(#pairRecommendPanel.active) > .panel-shell:not(#pairRecommendPanel) {
grid-column: 1;
min-width: 0;
}
body[data-view-mode="dual"] .voucher-page:has(#pairRecommendPanel.active) > #pairRecommendPanel.active {
grid-column: 2;
grid-row: 2;
display: grid;
grid-template-rows: auto auto minmax(0, 1fr);
position: sticky;
top: 10px;
max-height: calc(100vh - 90px);
min-width: 0;
}
body[data-view-mode="dual"] #pairRecommendPanel.active .table-wrap {
max-height: calc(100vh - 230px);
}
body[data-view-mode="dual"] .detail-panel .table-wrap {
max-height: clamp(300px, 44vh, 520px);
}
@media (max-width: 720px) {
.voucher-topbar,
.panel-header,
@@ -1736,6 +1770,8 @@
const panels = Array.from(document.querySelectorAll('.detail-panel'));
cards.forEach((card) => {
card.addEventListener('click', () => {
// 상태 탭 전환 시 추천/개별 추천 패널은 즉시 닫는다.
closeRecommendationPanel();
const targetId = card.dataset.target;
const statusKey = card.dataset.status;
const panel = targetId ? document.getElementById(targetId) : null;
wehago_compare.py
+219 -63
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@@ -145,7 +145,9 @@ _DASHBOARD_CACHE_TTL_SEC = 20
_SUGGEST_CACHE: dict[str, dict[str, Any]] = {}
_SUGGEST_CACHE_TTL_SEC = 20
_STATUS_ROWS_CACHE: dict[str, dict[str, Any]] = {}
_STATUS_ROWS_CACHE_TTL_SEC = 20
_STATUS_ROWS_CACHE_TTL_SEC = 300
_PAIR_RECOMMEND_CACHE: dict[str, dict[str, Any]] = {}
_PAIR_RECOMMEND_CACHE_TTL_SEC = 120
_STATUS_CACHE_WARMING: set[str] = set()
_STATUS_CACHE_WARMING_LOCK = threading.Lock()
@@ -1000,6 +1002,7 @@ def refresh_wehago_compare_data(engine: Any, source_root: Path | None = None) ->
_DASHBOARD_CACHE.clear()
_SUGGEST_CACHE.clear()
_STATUS_ROWS_CACHE.clear()
_PAIR_RECOMMEND_CACHE.clear()
return {
"scanned_files": summary.scanned_files,
"imported_files": summary.imported_files,
@@ -2189,6 +2192,7 @@ def save_recheck_review_rows(engine: Any, rows: list[dict[str, Any]]) -> int:
_DASHBOARD_CACHE.clear()
_SUGGEST_CACHE.clear()
_STATUS_ROWS_CACHE.clear()
_PAIR_RECOMMEND_CACHE.clear()
return len(normalized_rows)
@@ -2308,6 +2312,7 @@ def save_manual_pair_matches(
_DASHBOARD_CACHE.clear()
_SUGGEST_CACHE.clear()
_STATUS_ROWS_CACHE.clear()
_PAIR_RECOMMEND_CACHE.clear()
return len(rows_to_save)
@@ -2384,6 +2389,7 @@ def undo_last_action(engine: Any) -> dict[str, Any]:
_DASHBOARD_CACHE.clear()
_SUGGEST_CACHE.clear()
_STATUS_ROWS_CACHE.clear()
_PAIR_RECOMMEND_CACHE.clear()
return {"undone": True, "action_type": action_type, "affected": affected}
@@ -2416,6 +2422,14 @@ def _jaccard_similarity(left: Any, right: Any) -> float:
return (inter / union) if union else 0.0
def _jaccard_similarity_tokens(left_tokens: set[str], right_tokens: set[str]) -> float:
if not left_tokens or not right_tokens:
return 0.0
inter = len(left_tokens & right_tokens)
union = len(left_tokens | right_tokens)
return (inter / union) if union else 0.0
def _numeric_amount_for_side(row: dict[str, Any], side: str) -> float:
if side == "debit":
return parse_amount(row.get("ledger_debit") if "ledger_debit" in row else row.get("voucher_debit"))
@@ -2561,62 +2575,41 @@ def _collect_status_rows_for_workbench(
) -> dict[str, list[dict[str, Any]]]:
if start_year is None or end_year is None:
return {"ledger_only": [], "voucher_only": []}
if engine is not None:
init_wehago_compare_db(engine)
reviewed_keys: set[str] = set()
manual_pair_matches: list[dict[str, Any]] = []
if engine is not None:
with engine.begin() as conn:
reviewed_keys = get_saved_recheck_review_keys(conn, start_year, end_year)
manual_pair_matches = get_saved_manual_pair_matches(conn, start_year, end_year)
result: dict[str, list[dict[str, Any]]] = {"ledger_only": [], "voucher_only": []}
ledger_voucher_filter = normalize_text(ledger_voucher_no)
ledger_reason_filter = normalize_text(ledger_review_reason)
voucher_voucher_filter = normalize_text(voucher_voucher_no)
voucher_reason_filter = normalize_text(voucher_review_reason)
for year in range(start_year, end_year + 1):
bundle = discover_compare_result_bundle(year)
if not bundle:
continue
parsed = parse_compare_result_bundle(
str(bundle["ledger_result"]),
bundle["ledger_result"].stat().st_mtime,
str(bundle["voucher_result"]),
bundle["voucher_result"].stat().st_mtime,
year,
)
parsed = apply_saved_recheck_reviews(parsed, reviewed_keys)
parsed = apply_saved_manual_pair_matches(parsed, manual_pair_matches)
for row in parsed["ledger_only"]["rows"]:
if _filter_status_row(
row,
ledger_voucher_filter,
"",
"",
"",
"",
"",
"",
"",
ledger_reason_filter,
):
result["ledger_only"].append(row)
for row in parsed["voucher_only"]["rows"]:
if _filter_status_row(
row,
voucher_voucher_filter,
"",
"",
"",
"",
"",
"",
"",
voucher_reason_filter,
):
result["voucher_only"].append(row)
rows_by_status = _get_cached_status_rows_by_range(engine, start_year, end_year)
for row in rows_by_status["ledger_only"]:
if _filter_status_row(
row,
ledger_voucher_filter,
"",
"",
"",
"",
"",
"",
"",
ledger_reason_filter,
):
result["ledger_only"].append(row)
for row in rows_by_status["voucher_only"]:
if _filter_status_row(
row,
voucher_voucher_filter,
"",
"",
"",
"",
"",
"",
"",
voucher_reason_filter,
):
result["voucher_only"].append(row)
return result
@@ -2630,6 +2623,23 @@ def recommend_pair_matches(
voucher_review_reason: str = "",
limit: int = 300,
) -> dict[str, Any]:
safe_limit = max(min(int(limit or 300), 1000), 1)
cache_key = "|".join(
[
str(start_year),
str(end_year),
normalize_text(ledger_voucher_no),
normalize_text(ledger_review_reason),
normalize_text(voucher_voucher_no),
normalize_text(voucher_review_reason),
str(safe_limit),
]
)
now = time.time()
cached = _PAIR_RECOMMEND_CACHE.get(cache_key)
if cached and (now - float(cached.get("ts", 0))) <= _PAIR_RECOMMEND_CACHE_TTL_SEC:
return cached["payload"]
dataset = _collect_status_rows_for_workbench(
engine,
start_year,
@@ -2642,7 +2652,10 @@ def recommend_pair_matches(
ledger_rows = dataset["ledger_only"]
voucher_rows = dataset["voucher_only"]
if not ledger_rows or not voucher_rows:
return {"pairs": [], "stats": {"ledger_rows": len(ledger_rows), "voucher_rows": len(voucher_rows), "recommended": 0, "auto_eligible": 0}}
payload = {"pairs": [], "stats": {"ledger_rows": len(ledger_rows), "voucher_rows": len(voucher_rows), "recommended": 0, "auto_eligible": 0}}
_PAIR_RECOMMEND_CACHE.clear()
_PAIR_RECOMMEND_CACHE[cache_key] = {"ts": now, "payload": payload}
return payload
amount_index: dict[float, list[dict[str, Any]]] = {}
for voucher_row in voucher_rows:
@@ -2655,6 +2668,19 @@ def recommend_pair_matches(
continue
amount_index.setdefault(amount, []).append(voucher_row)
voucher_tokens_by_key: dict[str, tuple[set[str], set[str], set[str], str, str]] = {}
for voucher_row in voucher_rows:
voucher_key = clean(voucher_row.get("voucher_row_key"))
if not voucher_key:
continue
voucher_tokens_by_key[voucher_key] = (
_tokenize_for_similarity(voucher_row.get("voucher_account_name")),
_tokenize_for_similarity(voucher_row.get("voucher_vendor")),
_tokenize_for_similarity(voucher_row.get("voucher_desc")),
clean(voucher_row.get("voucher_account_code")),
clean(voucher_row.get("voucher_account_name")),
)
edge_candidates: list[dict[str, Any]] = []
for ledger_row in ledger_rows:
candidate_amounts = {
@@ -2673,23 +2699,149 @@ def recommend_pair_matches(
voucher_candidates.append(voucher_row)
if not voucher_candidates:
continue
scored_candidates: list[tuple[dict[str, Any], dict[str, Any]]] = []
ledger_key = clean(ledger_row.get("ledger_row_key"))
ledger_account_code = clean(ledger_row.get("ledger_account_code"))
ledger_account_tokens = _tokenize_for_similarity(ledger_row.get("ledger_account_name"))
ledger_vendor_tokens = _tokenize_for_similarity(ledger_row.get("ledger_vendor"))
ledger_desc_tokens = _tokenize_for_similarity(ledger_row.get("ledger_desc"))
side = _determine_primary_side(ledger_row)
if side == "debit":
ledger_amount = parse_amount(ledger_row.get("ledger_debit"))
elif side == "credit":
ledger_amount = parse_amount(ledger_row.get("ledger_credit"))
else:
ledger_amount = max(parse_amount(ledger_row.get("ledger_debit")), parse_amount(ledger_row.get("ledger_credit")))
top_row: dict[str, Any] | None = None
top_score: dict[str, Any] | None = None
second_best_score = -999.0
for voucher_row in voucher_candidates:
score_result = _score_pair_match(ledger_row, voucher_row)
voucher_key = clean(voucher_row.get("voucher_row_key"))
token_payload = voucher_tokens_by_key.get(voucher_key)
if not token_payload:
continue
voucher_account_tokens, voucher_vendor_tokens, voucher_desc_tokens, voucher_code, voucher_account_name = token_payload
score = 0.0
reasons: list[str] = []
hard_pass = True
if side == "debit":
voucher_amount = parse_amount(voucher_row.get("voucher_debit"))
elif side == "credit":
voucher_amount = parse_amount(voucher_row.get("voucher_credit"))
else:
voucher_amount = max(parse_amount(voucher_row.get("voucher_debit")), parse_amount(voucher_row.get("voucher_credit")))
amount_gap = abs(ledger_amount - voucher_amount)
if amount_gap < 0.5 and ledger_amount > 0:
score += 50
reasons.append("금액 일치")
elif amount_gap < 5 and ledger_amount > 0:
score += 35
reasons.append("금액 근접")
elif amount_gap < 100 and ledger_amount > 0:
score += 10
reasons.append("금액 유사")
else:
hard_pass = False
account_sim = _jaccard_similarity_tokens(ledger_account_tokens, voucher_account_tokens)
if ledger_account_code and voucher_code and ledger_account_code == voucher_code:
score += 22
reasons.append("계정코드 일치")
elif ledger_account_code and voucher_code and ledger_account_code[:4] == voucher_code[:4]:
score += 10
reasons.append("계정코드 대분류 일치")
else:
if account_sim >= 0.8:
score += 16
reasons.append("계정명 유사도 높음")
elif account_sim >= 0.55:
score += 8
reasons.append("계정명 유사")
else:
hard_pass = False
vendor_sim = _jaccard_similarity_tokens(ledger_vendor_tokens, voucher_vendor_tokens)
if vendor_sim >= 0.9:
score += 16
reasons.append("거래처 일치")
elif vendor_sim >= 0.65:
score += 10
reasons.append("거래처 유사")
elif vendor_sim >= 0.4:
score += 4
reasons.append("거래처 일부 유사")
else:
score -= 8
desc_sim = _jaccard_similarity_tokens(ledger_desc_tokens, voucher_desc_tokens)
if desc_sim >= 0.85:
score += 10
reasons.append("적요 매우 유사")
elif desc_sim >= 0.6:
score += 6
reasons.append("적요 유사")
elif desc_sim >= 0.35:
score += 2
ledger_date = _parse_iso_date(ledger_row.get("ledger_date"))
proof_date = _parse_iso_date(voucher_row.get("proof_date"))
if ledger_date and proof_date:
day_gap = abs((ledger_date - proof_date).days)
if day_gap <= 3:
score += 8
reasons.append("일자 근접")
elif day_gap <= 10:
score += 4
elif day_gap <= 45:
score += 1
else:
score -= 6
confidence = "low"
if score >= 88:
confidence = "high"
elif score >= 72:
confidence = "medium"
score_result = {
"score": round(score, 2),
"confidence_level": confidence,
"reason": ", ".join(reasons[:4]),
"auto_eligible": bool(
hard_pass
and score >= 88
and amount_gap < 0.5
and (
(ledger_account_code and voucher_code and ledger_account_code == voucher_code)
or account_sim >= 0.8
)
and vendor_sim >= 0.65
),
"hard_pass": hard_pass,
"vendor_similarity": round(vendor_sim, 4),
"account_similarity": round(account_sim, 4),
"amount_gap": round(amount_gap, 2),
}
if not score_result["hard_pass"] or score_result["score"] < 72:
continue
scored_candidates.append((voucher_row, score_result))
if not scored_candidates:
if top_score is None or score_result["score"] > float(top_score["score"]):
second_best_score = float(top_score["score"]) if top_score else second_best_score
top_row = voucher_row
top_score = score_result
elif score_result["score"] > second_best_score:
second_best_score = score_result["score"]
if top_row is None or top_score is None:
continue
scored_candidates.sort(key=lambda item: item[1]["score"], reverse=True)
top_candidate, top_score = scored_candidates[0]
second_score = scored_candidates[1][1]["score"] if len(scored_candidates) > 1 else -999
if top_score["score"] - second_score < 6:
if top_score["score"] - second_best_score < 6:
continue
edge_candidates.append(
{
"ledger_row": ledger_row,
"voucher_row": top_candidate,
"voucher_row": top_row,
"score": top_score["score"],
"confidence_level": top_score["confidence_level"],
"reason": top_score["reason"],
@@ -2730,10 +2882,10 @@ def recommend_pair_matches(
"voucher_row": edge["voucher_row"],
}
)
if len(picked) >= max(min(int(limit or 300), 1000), 1):
if len(picked) >= safe_limit:
break
return {
payload = {
"pairs": picked,
"stats": {
"ledger_rows": len(ledger_rows),
@@ -2743,6 +2895,9 @@ def recommend_pair_matches(
"high_confidence": sum(1 for row in picked if row["confidence_level"] == "high"),
},
}
_PAIR_RECOMMEND_CACHE.clear()
_PAIR_RECOMMEND_CACHE[cache_key] = {"ts": now, "payload": payload}
return payload
def save_recommended_pair_matches(
@@ -3260,6 +3415,7 @@ def get_status_field_suggestions(
}
)
_SUGGEST_CACHE.clear()
_PAIR_RECOMMEND_CACHE.clear()
_SUGGEST_CACHE[cache_key] = {"ts": now, "rows": all_rows}
safe_offset = max(int(offset or 0), 0)