[split-required] Split 700-870 LOC files across all services
backend-lehrer (11 files): - llm_gateway/routes/schools.py (867 → 5), recording_api.py (848 → 6) - messenger_api.py (840 → 5), print_generator.py (824 → 5) - unit_analytics_api.py (751 → 5), classroom/routes/context.py (726 → 4) - llm_gateway/routes/edu_search_seeds.py (710 → 4) klausur-service (12 files): - ocr_labeling_api.py (845 → 4), metrics_db.py (833 → 4) - legal_corpus_api.py (790 → 4), page_crop.py (758 → 3) - mail/ai_service.py (747 → 4), github_crawler.py (767 → 3) - trocr_service.py (730 → 4), full_compliance_pipeline.py (723 → 4) - dsfa_rag_api.py (715 → 4), ocr_pipeline_auto.py (705 → 4) website (6 pages): - audit-checklist (867 → 8), content (806 → 6) - screen-flow (790 → 4), scraper (789 → 5) - zeugnisse (776 → 5), modules (745 → 4) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
269
klausur-service/backend/mail/ai_category.py
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269
klausur-service/backend/mail/ai_category.py
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"""
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AI Email - Category Classification and Response Suggestions
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Rule-based and LLM-based email category classification,
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plus response suggestion generation.
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Extracted from ai_service.py to keep files under 500 LOC.
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"""
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import os
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import logging
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from typing import Optional, List, Tuple
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import httpx
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from .models import (
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EmailCategory,
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SenderType,
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ResponseSuggestion,
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)
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logger = logging.getLogger(__name__)
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# LLM Gateway configuration
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LLM_GATEWAY_URL = os.getenv("LLM_GATEWAY_URL", "http://localhost:8090")
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async def classify_category(
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http_client: httpx.AsyncClient,
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subject: str,
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body_preview: str,
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sender_type: SenderType,
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) -> Tuple[EmailCategory, float]:
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"""
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Classify email into a category.
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Rule-based classification first, falls back to LLM.
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"""
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category, confidence = _classify_category_rules(subject, body_preview, sender_type)
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if confidence > 0.7:
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return category, confidence
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return await _classify_category_llm(http_client, subject, body_preview)
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def _classify_category_rules(
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subject: str,
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body_preview: str,
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sender_type: SenderType,
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) -> Tuple[EmailCategory, float]:
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"""Rule-based category classification."""
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text = f"{subject} {body_preview}".lower()
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category_keywords = {
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EmailCategory.DIENSTLICH: [
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"dienstlich", "dienstanweisung", "erlass", "verordnung",
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"bescheid", "verfuegung", "ministerium", "behoerde"
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],
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EmailCategory.PERSONAL: [
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"personalrat", "stellenausschreibung", "versetzung",
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"beurteilung", "dienstzeugnis", "krankmeldung", "elternzeit"
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],
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EmailCategory.FINANZEN: [
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"budget", "haushalt", "etat", "abrechnung", "rechnung",
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"erstattung", "zuschuss", "foerdermittel"
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],
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EmailCategory.ELTERN: [
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"elternbrief", "elternabend", "schulkonferenz",
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"elternvertreter", "elternbeirat"
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],
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EmailCategory.SCHUELER: [
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"schueler", "schuelerin", "zeugnis", "klasse", "unterricht",
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"pruefung", "klassenfahrt", "schulpflicht"
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],
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EmailCategory.FORTBILDUNG: [
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"fortbildung", "seminar", "workshop", "schulung",
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"weiterbildung", "nlq", "didaktik"
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],
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EmailCategory.VERANSTALTUNG: [
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"einladung", "veranstaltung", "termin", "konferenz",
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"sitzung", "tagung", "feier"
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],
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EmailCategory.SICHERHEIT: [
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"sicherheit", "notfall", "brandschutz", "evakuierung",
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"hygiene", "corona", "infektionsschutz"
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],
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EmailCategory.TECHNIK: [
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"it", "software", "computer", "netzwerk", "login",
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"passwort", "digitalisierung", "iserv"
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],
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EmailCategory.NEWSLETTER: [
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"newsletter", "rundschreiben", "info-mail", "mitteilung"
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],
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EmailCategory.WERBUNG: [
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"angebot", "rabatt", "aktion", "werbung", "abonnement"
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],
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}
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best_category = EmailCategory.SONSTIGES
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best_score = 0.0
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for category, keywords in category_keywords.items():
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score = sum(1 for kw in keywords if kw in text)
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if score > best_score:
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best_score = score
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best_category = category
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if sender_type in [SenderType.KULTUSMINISTERIUM, SenderType.LANDESSCHULBEHOERDE, SenderType.RLSB]:
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if best_category == EmailCategory.SONSTIGES:
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best_category = EmailCategory.DIENSTLICH
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best_score = 2
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confidence = min(0.9, 0.4 + (best_score * 0.15))
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return best_category, confidence
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async def _classify_category_llm(
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client: httpx.AsyncClient,
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subject: str,
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body_preview: str,
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) -> Tuple[EmailCategory, float]:
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"""LLM-based category classification."""
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try:
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categories = ", ".join([c.value for c in EmailCategory])
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prompt = f"""Klassifiziere diese E-Mail in EINE Kategorie:
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Betreff: {subject}
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Inhalt: {body_preview[:500]}
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Kategorien: {categories}
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Antworte NUR mit dem Kategorienamen und einer Konfidenz (0.0-1.0):
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Format: kategorie|konfidenz
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"""
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response = await client.post(
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f"{LLM_GATEWAY_URL}/api/v1/inference",
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json={
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"prompt": prompt,
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"playbook": "mail_analysis",
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"max_tokens": 50,
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},
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)
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if response.status_code == 200:
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data = response.json()
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result = data.get("response", "sonstiges|0.5")
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parts = result.strip().split("|")
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if len(parts) >= 2:
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category_str = parts[0].strip().lower()
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confidence = float(parts[1].strip())
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try:
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category = EmailCategory(category_str)
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return category, min(max(confidence, 0.0), 1.0)
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except ValueError:
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pass
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except Exception as e:
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logger.warning(f"LLM category classification failed: {e}")
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return EmailCategory.SONSTIGES, 0.5
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async def suggest_response(
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http_client: httpx.AsyncClient,
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subject: str,
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body_text: str,
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sender_type: SenderType,
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category: EmailCategory,
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) -> List[ResponseSuggestion]:
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"""Generate response suggestions for an email."""
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suggestions = []
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if sender_type in [SenderType.KULTUSMINISTERIUM, SenderType.LANDESSCHULBEHOERDE, SenderType.RLSB]:
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suggestions.append(ResponseSuggestion(
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template_type="acknowledgment",
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subject=f"Re: {subject}",
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body="""Sehr geehrte Damen und Herren,
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vielen Dank fuer Ihre Nachricht.
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Ich bestaetige den Eingang und werde die Angelegenheit fristgerecht bearbeiten.
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Mit freundlichen Gruessen""",
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confidence=0.8,
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))
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if category == EmailCategory.ELTERN:
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suggestions.append(ResponseSuggestion(
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template_type="parent_response",
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subject=f"Re: {subject}",
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body="""Liebe Eltern,
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vielen Dank fuer Ihre Nachricht.
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[Ihre Antwort hier]
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Mit freundlichen Gruessen""",
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confidence=0.7,
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))
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try:
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llm_suggestion = await _generate_response_llm(http_client, subject, body_text[:500], sender_type)
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if llm_suggestion:
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suggestions.append(llm_suggestion)
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except Exception as e:
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logger.warning(f"LLM response generation failed: {e}")
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return suggestions
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async def _generate_response_llm(
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client: httpx.AsyncClient,
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subject: str,
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body_preview: str,
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sender_type: SenderType,
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) -> Optional[ResponseSuggestion]:
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"""Generate a response suggestion using LLM."""
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try:
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sender_desc = {
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SenderType.KULTUSMINISTERIUM: "dem Kultusministerium",
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SenderType.LANDESSCHULBEHOERDE: "der Landesschulbehoerde",
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SenderType.RLSB: "dem RLSB",
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SenderType.ELTERNVERTRETER: "einem Elternvertreter",
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}.get(sender_type, "einem Absender")
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prompt = f"""Du bist eine Schulleiterin in Niedersachsen. Formuliere eine professionelle, kurze Antwort auf diese E-Mail von {sender_desc}:
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Betreff: {subject}
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Inhalt: {body_preview}
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Die Antwort sollte:
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- Hoeflich und formell sein
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- Den Eingang bestaetigen
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- Eine konkrete naechste Aktion nennen oder um Klaerung bitten
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Antworte NUR mit dem Antworttext (ohne Betreffzeile, ohne "Betreff:").
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"""
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response = await client.post(
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f"{LLM_GATEWAY_URL}/api/v1/inference",
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json={
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"prompt": prompt,
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"playbook": "mail_analysis",
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"max_tokens": 300,
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},
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)
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if response.status_code == 200:
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data = response.json()
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body = data.get("response", "").strip()
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if body:
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return ResponseSuggestion(
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template_type="ai_generated",
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subject=f"Re: {subject}",
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body=body,
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confidence=0.6,
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)
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except Exception as e:
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logger.warning(f"LLM response generation failed: {e}")
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return None
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