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8ec0148 1c8f647 8ec0148 1c8f647 8ec0148 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | import re
from dataclasses import dataclass, field
from typing import Callable
@dataclass
class ProgrammeFacts:
programme: str
source_available: bool = False
focus_points: list[str] = field(default_factory=list)
fit_points: list[str] = field(default_factory=list)
timing_points: list[str] = field(default_factory=list)
document_points: list[str] = field(default_factory=list)
raw_context: str = ""
class ProgrammeFactsProvider:
"""Extract lightweight programme facts from retrieved RAG context.
This keeps volatile programme data in the scraped/imported knowledge base
instead of hardcoding it in the conversation-routing layer.
"""
_PROGRAM_FILTERS = {
"emba": "emba",
"iemba": "iemba",
"emba_x": "emba x",
}
_QUERY_BY_LANGUAGE = {
"de": (
"Bewerbung Zulassung Voraussetzungen Studiengebühr Start Datum Dauer "
"Bewerbungsfrist Unterlagen Dokumente CV Zeugnisse Führungserfahrung "
"Berufserfahrung Sprache Module Präsenzwochen Wahlkurse Capstone"
),
"en": (
"application admissions requirements tuition start date duration deadline "
"documents CV certificates transcripts leadership experience professional "
"experience language modules campus weeks electives capstone"
),
}
_FOCUS_TERMS = (
"focus",
"ziel",
"ziele",
"fokus",
"management",
"leadership",
"transformation",
"innovation",
"international",
"dach",
)
_FIT_TERMS = (
"requirement",
"requirements",
"admission",
"admissions",
"zulassung",
"voraussetzung",
"degree",
"abschluss",
"experience",
"erfahrung",
"leadership",
"führung",
"fuehrung",
"english",
"englisch",
"german",
"deutsch",
)
_TIMING_TERMS = (
"tuition",
"fee",
"fees",
"studiengebühr",
"studiengebuehr",
"chf",
"start",
"duration",
"dauer",
"months",
"monate",
"deadline",
"bewerbungsfrist",
"core course",
"kernkurs",
"elective",
"wahlkurs",
"campus week",
"präsenzwoche",
"praesenzwoche",
"abroad",
"auslandsmodul",
"capstone",
)
_DOCUMENT_TERMS = (
"document",
"documents",
"unterlagen",
"dokument",
"dokumente",
"cv",
"resume",
"zeugnis",
"zeugnisse",
"certificate",
"certificates",
"transcript",
"online-bewerbung",
"online application",
"online-assessment",
"online assessment",
"application file",
"bewerbungsakte",
)
_NOISE_TERMS = (
"vielen dank für ihr interesse",
"vielen dank fuer ihr interesse",
"senior recruitment",
"admissions manager",
"bei allgemeinen anfragen",
"allgemeinen anfragen",
"kontakt cyra",
"kontakt kristin",
"kontakt teyuna",
"impact story",
"alumnus",
"alumni",
"wir sprachen mit",
"du warst teilnehmer",
"für mich war die",
"fuer mich war die",
"unterlagen und werkzeuge",
"jeder kurswoche",
"lernerfahrungen",
"diplomarbeit",
"preis ausgezeichnet",
"ich bin mir ganz sicher",
"beruflichen fortschritt",
"tools, dem netzwerk",
"hsg mitnehmen",
)
def __init__(self, retrieve_context: Callable[[str, str, str], str]) -> None:
self._retrieve_context = retrieve_context
self._cache: dict[tuple[str, str], ProgrammeFacts] = {}
def get_facts(self, programme: str, language: str) -> ProgrammeFacts:
normalized_programme = self._normalize_programme(programme)
normalized_language = language if language in {"de", "en"} else "en"
cache_key = (normalized_programme, normalized_language)
if cache_key in self._cache:
return self._cache[cache_key]
query = self._QUERY_BY_LANGUAGE[normalized_language]
program_filter = self._PROGRAM_FILTERS.get(normalized_programme, normalized_programme)
try:
context = self._retrieve_context(query, program_filter, normalized_language) or ""
except Exception:
context = ""
facts = self._extract_facts(normalized_programme, context)
if facts.source_available:
self._cache[cache_key] = facts
return facts
def _extract_facts(self, programme: str, context: str) -> ProgrammeFacts:
sentences = self._split_sentences(context)
return ProgrammeFacts(
programme=programme,
source_available=bool(sentences),
focus_points=self._select_sentences(sentences, self._FOCUS_TERMS, limit=2),
fit_points=self._select_sentences(sentences, self._FIT_TERMS, limit=3),
timing_points=self._select_sentences(sentences, self._TIMING_TERMS, limit=4),
document_points=self._select_sentences(sentences, self._DOCUMENT_TERMS, limit=3),
raw_context=context,
)
@staticmethod
def _normalize_programme(programme: str) -> str:
normalized = (programme or "").lower().replace("-", "_").replace(" ", "_")
if normalized in {"emba_x", "embax"}:
return "emba_x"
if normalized in {"iemba", "iemba_hsg", "international_emba"}:
return "iemba"
return "emba" if normalized in {"emba", "emba_hsg"} else normalized
@staticmethod
def _split_sentences(text: str) -> list[str]:
raw_text = (text or "").strip()
if not raw_text:
return []
raw_text = re.sub(r"#{1,6}\s*", "\n", raw_text)
raw_text = re.sub(r"\|", "\n", raw_text)
chunks = re.split(r"\n+|(?<=[.!?])\s+|(?:\s+•\s+)", raw_text)
sentences = []
for chunk in chunks:
normalized = re.sub(r"\s+", " ", chunk).strip(" -•\t\n")
normalized_lower = normalized.lower()
has_compact_fact = bool(re.search(r"\b\d{1,2}[./]\d{1,2}[./]\d{2,4}\b|chf\s*[\d']", normalized_lower))
if len(normalized) < 20 and not has_compact_fact:
continue
if any(term in normalized_lower for term in ProgrammeFactsProvider._NOISE_TERMS):
continue
if len(normalized) > 320:
normalized = normalized[:317].rstrip() + "..."
sentences.append(normalized)
return sentences
@staticmethod
def _select_sentences(sentences: list[str], terms: tuple[str, ...], limit: int) -> list[str]:
selected = []
seen = set()
for sentence in sentences:
sentence_lower = sentence.lower()
if sentence_lower in seen:
continue
if any(term in sentence_lower for term in terms):
selected.append(sentence)
seen.add(sentence_lower)
if len(selected) >= limit:
break
return selected
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