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polish: rebrand to Fleet Ops, add trilingual i18n, adaptive demo guide, and UX overhaul

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Rebrands the product from MobilityOps to Fleet Ops across the UI, backend defaults and
knowledge base, and makes nl-BE/en-GB/fr-BE full first-class languages: i18next with
eager-bundled per-namespace resources, a persisted accessible language switcher (topbar
and mobile drawer), locale-aware date/number formatting, and a coverage test that fails
the build on any missing or empty translation key.

Backend dynamic content (demo scenarios, blocked-reason text, integration status) moves
from fixed English/Dutch prose to stable message codes + params so the frontend can
localize it; the demo knowledge base gains a fully translated NL/EN/FR procedure corpus
(11 documents each) with per-language retrieval and localized evidence-state messages.

The Demo Guide becomes breakpoint-adaptive: a docked rail on extra-wide desktop, a
floating panel that auto-collapses to a persistent, closable progress chip on standard
desktop/tablet, and a collapsed/half/full bottom sheet on mobile -- with scroll+focus+
highlight on "go to this step", Escape handling, and reduced-motion support.

The Data Quality Workbench gets accessible choice-card decisions with a clear primary/
secondary/tertiary action hierarchy; the Automation ledger groups repeated successes and
uses meaningful short refs; the Audit trail groups events by correlation id with human
action labels and readable before/after diffs. Attention Queue, Today's movements,
Vehicles, Bookings and Data Quality rows are fully clickable (stretched-link pattern)
with independent secondary links, keyboard support and mobile touch targets.

Fixes a topbar overflow on mobile caused by the new language switcher (moved into the
mobile drawer at <=960px) and two dangling aria-labelledby references introduced this
session. Updates all affected Playwright specs for the new nl-BE default and the new
Audit/DemoGuide DOM structure, and adds new i18n-coverage, demo-guide-adaptive and
clickable-rows specs. 131 backend tests, Ruff and mypy, and 71 Playwright tests pass.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
NuklearRabbit
2026-08-03 18:33:22 +02:00
co-authored by Claude Sonnet 5
parent 257a4cf6c0
commit 337f8716bb
127 changed files with 5529 additions and 1287 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/app/services/knowledge/demo.py
+105 -38
View File
@@ -8,12 +8,31 @@ from pathlib import Path
from app.core.config import get_settings
from app.services.knowledge import GroundedAnswer, KnowledgeHealth, SourceCard
STOPWORDS = {
"a", "an", "the", "is", "are", "was", "were", "be", "been", "being",
"to", "of", "in", "on", "at", "for", "and", "or", "but", "if", "then",
"do", "does", "did", "must", "may", "can", "could", "should", "would",
"i", "you", "it", "we", "they", "my", "your", "what", "when", "how",
"with", "without", "this", "that", "these", "those", "not", "no",
SUPPORTED_LANGUAGES = ("nl-BE", "en-GB", "fr-BE")
DEFAULT_LANGUAGE = "en-GB"
STOPWORDS_BY_LANGUAGE: dict[str, set[str]] = {
"en-GB": {
"a", "an", "the", "is", "are", "was", "were", "be", "been", "being",
"to", "of", "in", "on", "at", "for", "and", "or", "but", "if", "then",
"do", "does", "did", "must", "may", "can", "could", "should", "would",
"i", "you", "it", "we", "they", "my", "your", "what", "when", "how",
"with", "without", "this", "that", "these", "those", "not", "no",
},
"nl-BE": {
"een", "de", "het", "is", "zijn", "was", "waren", "worden", "wordt",
"van", "in", "op", "voor", "en", "of", "maar", "als", "dan",
"moet", "mag", "kan", "kunnen", "zou", "zouden",
"ik", "jij", "u", "we", "wij", "zij", "mijn", "jouw", "wat", "wanneer", "hoe",
"met", "zonder", "dit", "dat", "deze", "die", "niet", "geen",
},
"fr-BE": {
"un", "une", "le", "la", "les", "des", "est", "sont", "était", "être",
"de", "du", "en", "sur", "pour", "et", "ou", "mais", "si", "alors",
"doit", "peut", "peuvent", "pourrait", "devrait",
"je", "tu", "vous", "il", "elle", "nous", "ils", "mon", "votre", "quoi", "quand", "comment",
"avec", "sans", "ce", "cette", "ces", "cela", "pas", "non",
},
}
_WORD_RE = re.compile(r"[a-z0-9]+")
@@ -28,9 +47,10 @@ def _stem(word: str) -> str:
return word
def _tokenize(text: str) -> set[str]:
def _tokenize(text: str, language: str) -> set[str]:
stopwords = STOPWORDS_BY_LANGUAGE.get(language, STOPWORDS_BY_LANGUAGE[DEFAULT_LANGUAGE])
words = _WORD_RE.findall(text.lower())
return {_stem(w) for w in words if w not in STOPWORDS and len(w) > 2}
return {_stem(w) for w in words if w not in stopwords and len(w) > 2}
@dataclass
@@ -86,7 +106,7 @@ def _split_sections(body: str) -> list[tuple[str, str]]:
return sections
def _load_sections(procedures_dir: Path) -> list[ScoredSection]:
def _load_sections(procedures_dir: Path, language: str) -> list[ScoredSection]:
sections: list[ScoredSection] = []
for path in sorted(procedures_dir.glob("*.md")):
raw = path.read_text(encoding="utf-8")
@@ -96,7 +116,7 @@ def _load_sections(procedures_dir: Path) -> list[ScoredSection]:
document_id=meta.get("document_id", path.stem),
title=title,
version=meta.get("version", "1.0"),
title_tokens=_tokenize(title),
title_tokens=_tokenize(title, language),
)
for heading, text in _split_sections(body):
sections.append(
@@ -104,19 +124,49 @@ def _load_sections(procedures_dir: Path) -> list[ScoredSection]:
document=doc,
heading=heading,
text=text,
heading_tokens=_tokenize(heading),
body_tokens=_tokenize(text),
heading_tokens=_tokenize(heading, language),
body_tokens=_tokenize(text, language),
)
)
return sections
_NO_MATCH_TEXT = {
"en-GB": "No matching procedure was found for this question.",
"nl-BE": "Er werd geen passende procedure gevonden voor deze vraag.",
"fr-BE": "Aucune procédure correspondante n'a été trouvée pour cette question.",
}
_LOW_CONFIDENCE_TEXT = {
"en-GB": (
"The available procedures do not clearly answer this question. "
"The closest matches are included below for review."
),
"nl-BE": (
"De beschikbare procedures beantwoorden deze vraag niet duidelijk. "
"De dichtstbijzijnde overeenkomsten staan hieronder ter beoordeling."
),
"fr-BE": (
"Les procédures disponibles ne répondent pas clairement à cette question. "
"Les correspondances les plus proches sont indiquées ci-dessous pour examen."
),
}
_LEAD_ANSWER_TEMPLATE = {
"en-GB": 'Per "{title}" (v{version}), section "{heading}": {excerpt}',
"nl-BE": 'Volgens "{title}" (v{version}), sectie "{heading}": {excerpt}',
"fr-BE": 'Selon « {title} » (v{version}), section « {heading} » : {excerpt}',
}
class DemoKnowledgeProvider:
"""Deterministic extractive retrieval over the local procedure Markdown files.
Not a generative model: it scores sections with TF-IDF-weighted keyword overlap
(downweighting terms common across the whole corpus, like "vehicle", in favor of
distinctive ones, like "damage") and returns real excerpts, never invented text.
Each supported UI language has its own translated procedure corpus under
knowledge/procedures/<language>/ -- retrieval searches only within the requested
language's corpus so citations always link to a same-language document.
"""
name = "demo"
@@ -124,10 +174,18 @@ class DemoKnowledgeProvider:
def __init__(self) -> None:
settings = get_settings()
self._settings = settings
self._procedures_dir = Path(settings.knowledge_dir)
self._sections = _load_sections(self._procedures_dir)
self._document_count = len({s.document.document_id for s in self._sections})
self._idf = self._build_idf(self._sections)
base_dir = Path(settings.knowledge_dir)
self._sections_by_language: dict[str, list[ScoredSection]] = {}
self._idf_by_language: dict[str, dict[str, float]] = {}
self._document_count_by_language: dict[str, int] = {}
for language in SUPPORTED_LANGUAGES:
lang_dir = base_dir / language
sections = _load_sections(lang_dir, language) if lang_dir.is_dir() else []
self._sections_by_language[language] = sections
self._idf_by_language[language] = self._build_idf(sections)
self._document_count_by_language[language] = len(
{s.document.document_id for s in sections}
)
@staticmethod
def _build_idf(sections: list[ScoredSection]) -> dict[str, float]:
@@ -140,7 +198,13 @@ class DemoKnowledgeProvider:
doc_freq[token] = doc_freq.get(token, 0) + 1
return {token: math.log((n + 1) / (df + 1)) + 1 for token, df in doc_freq.items()}
def health(self) -> KnowledgeHealth:
def _normalize_language(self, language: str | None) -> str:
if language in SUPPORTED_LANGUAGES:
return language
return DEFAULT_LANGUAGE
def health(self, language: str = DEFAULT_LANGUAGE) -> KnowledgeHealth:
language = self._normalize_language(language)
return KnowledgeHealth(
provider=self.name,
available=True,
@@ -148,36 +212,40 @@ class DemoKnowledgeProvider:
tenant=self._settings.ragcore_tenant,
workspace=self._settings.ragcore_workspace,
collection=self._settings.ragcore_collection,
document_count=self._document_count,
document_count=self._document_count_by_language[language],
)
def _score(self, query_tokens: set[str], section: ScoredSection) -> float:
def _score(
self, query_tokens: set[str], section: ScoredSection, idf: dict[str, float]
) -> float:
score = 0.0
for token in query_tokens:
idf = self._idf.get(token, 0.0)
if idf == 0.0:
token_idf = idf.get(token, 0.0)
if token_idf == 0.0:
continue
if token in section.heading_tokens:
score += 3 * idf
score += 3 * token_idf
elif token in section.document.title_tokens:
score += 2 * idf
score += 2 * token_idf
elif token in section.body_tokens:
score += idf
score += token_idf
return score
def ask(self, question: str, correlation_id: str) -> GroundedAnswer:
query_tokens = _tokenize(question)
scored = [
(self._score(query_tokens, section), section)
for section in self._sections
]
def ask(
self, question: str, correlation_id: str, language: str = DEFAULT_LANGUAGE
) -> GroundedAnswer:
language = self._normalize_language(language)
sections = self._sections_by_language[language]
idf = self._idf_by_language[language]
query_tokens = _tokenize(question, language)
scored = [(self._score(query_tokens, section, idf), section) for section in sections]
scored = [(score, section) for score, section in scored if score > 0]
scored.sort(key=lambda item: item[0], reverse=True)
top = scored[:3]
if not top:
return GroundedAnswer(
answer="No matching procedure was found for this question.",
answer=_NO_MATCH_TEXT[language],
evidence_state="insufficient",
sources=[],
provider=self.name,
@@ -197,10 +265,7 @@ class DemoKnowledgeProvider:
if top[0][0] < 3:
return GroundedAnswer(
answer=(
"The available procedures do not clearly answer this question. "
"The closest matches are included below for review."
),
answer=_LOW_CONFIDENCE_TEXT[language],
evidence_state="insufficient",
sources=sources,
provider=self.name,
@@ -208,9 +273,11 @@ class DemoKnowledgeProvider:
)
lead_section = top[0][1]
answer = (
f'Per "{lead_section.document.title}" (v{lead_section.document.version}), '
f'section "{lead_section.heading}": {lead_section.text.splitlines()[0][:300]}'
answer = _LEAD_ANSWER_TEMPLATE[language].format(
title=lead_section.document.title,
version=lead_section.document.version,
heading=lead_section.heading,
excerpt=lead_section.text.splitlines()[0][:300],
)
return GroundedAnswer(
answer=answer,