[split-required] Split final 43 files (500-668 LOC) to complete refactoring
klausur-service (11 files): - cv_gutter_repair, ocr_pipeline_regression, upload_api - ocr_pipeline_sessions, smart_spell, nru_worksheet_generator - ocr_pipeline_overlays, mail/aggregator, zeugnis_api - cv_syllable_detect, self_rag backend-lehrer (17 files): - classroom_engine/suggestions, generators/quiz_generator - worksheets_api, llm_gateway/comparison, state_engine_api - classroom/models (→ 4 submodules), services/file_processor - alerts_agent/api/wizard+digests+routes, content_generators/pdf - classroom/routes/sessions, llm_gateway/inference - classroom_engine/analytics, auth/keycloak_auth - alerts_agent/processing/rule_engine, ai_processor/print_versions agent-core (5 files): - brain/memory_store, brain/knowledge_graph, brain/context_manager - orchestrator/supervisor, sessions/session_manager admin-lehrer (5 components): - GridOverlay, StepGridReview, DevOpsPipelineSidebar - DataFlowDiagram, sbom/wizard/page website (2 files): - DependencyMap, lehrer/abitur-archiv Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
@@ -1,5 +1,5 @@
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"""
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API Routes für Alerts Agent.
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API Routes fuer Alerts Agent.
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Endpoints:
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- POST /alerts/ingest - Manuell Alerts importieren
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@@ -13,12 +13,18 @@ Endpoints:
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import os
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from datetime import datetime
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException, Query
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from pydantic import BaseModel, Field
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from fastapi import APIRouter, HTTPException, Query
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from ..models.alert_item import AlertItem, AlertStatus
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from ..models.relevance_profile import RelevanceProfile, PriorityItem
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from ..processing.relevance_scorer import RelevanceDecision, RelevanceScorer
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from .schemas import (
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AlertIngestRequest, AlertIngestResponse,
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AlertRunRequest, AlertRunResponse,
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InboxItem, InboxResponse,
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FeedbackRequest, FeedbackResponse,
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ProfilePriorityRequest, ProfileUpdateRequest, ProfileResponse,
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)
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router = APIRouter(prefix="/alerts", tags=["alerts"])
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@@ -30,113 +36,13 @@ ALERTS_USE_LLM = os.getenv("ALERTS_USE_LLM", "false").lower() == "true"
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# ============================================================================
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# In-Memory Storage (später durch DB ersetzen)
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# In-Memory Storage (spaeter durch DB ersetzen)
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# ============================================================================
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_alerts_store: dict[str, AlertItem] = {}
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_profile_store: dict[str, RelevanceProfile] = {}
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# ============================================================================
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# Request/Response Models
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# ============================================================================
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class AlertIngestRequest(BaseModel):
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"""Request für manuelles Alert-Import."""
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title: str = Field(..., min_length=1, max_length=500)
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url: str = Field(..., min_length=1)
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snippet: Optional[str] = Field(default=None, max_length=2000)
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topic_label: str = Field(default="Manual Import")
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published_at: Optional[datetime] = None
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class AlertIngestResponse(BaseModel):
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"""Response für Alert-Import."""
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id: str
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status: str
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message: str
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class AlertRunRequest(BaseModel):
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"""Request für Scoring-Pipeline."""
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limit: int = Field(default=50, ge=1, le=200)
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skip_scored: bool = Field(default=True)
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class AlertRunResponse(BaseModel):
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"""Response für Scoring-Pipeline."""
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processed: int
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keep: int
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drop: int
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review: int
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errors: int
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duration_ms: int
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class InboxItem(BaseModel):
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"""Ein Item in der Inbox."""
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id: str
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title: str
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url: str
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snippet: Optional[str]
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topic_label: str
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published_at: Optional[datetime]
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relevance_score: Optional[float]
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relevance_decision: Optional[str]
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relevance_summary: Optional[str]
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status: str
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class InboxResponse(BaseModel):
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"""Response für Inbox-Abfrage."""
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items: list[InboxItem]
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total: int
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page: int
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page_size: int
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class FeedbackRequest(BaseModel):
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"""Request für Relevanz-Feedback."""
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alert_id: str
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is_relevant: bool
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reason: Optional[str] = None
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tags: list[str] = Field(default_factory=list)
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class FeedbackResponse(BaseModel):
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"""Response für Feedback."""
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success: bool
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message: str
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profile_updated: bool
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class ProfilePriorityRequest(BaseModel):
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"""Priority für Profile-Update."""
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label: str
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weight: float = Field(default=0.5, ge=0.0, le=1.0)
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keywords: list[str] = Field(default_factory=list)
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description: Optional[str] = None
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class ProfileUpdateRequest(BaseModel):
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"""Request für Profile-Update."""
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priorities: Optional[list[ProfilePriorityRequest]] = None
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exclusions: Optional[list[str]] = None
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policies: Optional[dict] = None
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class ProfileResponse(BaseModel):
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"""Response für Profile."""
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id: str
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priorities: list[dict]
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exclusions: list[str]
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policies: dict
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total_scored: int
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total_kept: int
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total_dropped: int
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accuracy_estimate: Optional[float]
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# ============================================================================
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# Endpoints
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# ============================================================================
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@@ -146,7 +52,7 @@ async def ingest_alert(request: AlertIngestRequest):
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"""
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Manuell einen Alert importieren.
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Nützlich für Tests oder manuelles Hinzufügen von Artikeln.
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Nuetzlich fuer Tests oder manuelles Hinzufuegen von Artikeln.
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"""
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alert = AlertItem(
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title=request.title,
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@@ -168,13 +74,13 @@ async def ingest_alert(request: AlertIngestRequest):
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@router.post("/run", response_model=AlertRunResponse)
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async def run_scoring_pipeline(request: AlertRunRequest):
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"""
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Scoring-Pipeline für neue Alerts starten.
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Scoring-Pipeline fuer neue Alerts starten.
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Bewertet alle unbewerteten Alerts und klassifiziert sie
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in KEEP, DROP oder REVIEW.
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Wenn ALERTS_USE_LLM=true, wird das LLM Gateway für Scoring verwendet.
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Sonst wird ein schnelles Keyword-basiertes Scoring durchgeführt.
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Wenn ALERTS_USE_LLM=true, wird das LLM Gateway fuer Scoring verwendet.
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Sonst wird ein schnelles Keyword-basiertes Scoring durchgefuehrt.
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"""
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import time
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start = time.time()
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@@ -193,7 +99,7 @@ async def run_scoring_pipeline(request: AlertRunRequest):
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keep = drop = review = errors = 0
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# Profil für Scoring laden
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# Profil fuer Scoring laden
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profile = _profile_store.get("default")
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if not profile:
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profile = RelevanceProfile.create_default_education_profile()
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@@ -201,7 +107,7 @@ async def run_scoring_pipeline(request: AlertRunRequest):
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_profile_store["default"] = profile
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if ALERTS_USE_LLM and LLM_API_KEY:
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# LLM-basiertes Scoring über Gateway
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# LLM-basiertes Scoring ueber Gateway
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scorer = RelevanceScorer(
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gateway_url=LLM_GATEWAY_URL,
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api_key=LLM_API_KEY,
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@@ -227,12 +133,12 @@ async def run_scoring_pipeline(request: AlertRunRequest):
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snippet_lower = (alert.snippet or "").lower()
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combined = title_lower + " " + snippet_lower
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# Ausschlüsse aus Profil prüfen
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# Ausschluesse aus Profil pruefen
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if any(excl.lower() in combined for excl in profile.exclusions):
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alert.relevance_score = 0.15
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alert.relevance_decision = RelevanceDecision.DROP.value
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drop += 1
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# Prioritäten aus Profil prüfen
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# Prioritaeten aus Profil pruefen
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elif any(
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p.label.lower() in combined or
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any(kw.lower() in combined for kw in (p.keywords if hasattr(p, 'keywords') else []))
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@@ -285,9 +191,9 @@ async def get_inbox(
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# Pagination
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total = len(alerts)
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start = (page - 1) * page_size
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end = start + page_size
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page_alerts = alerts[start:end]
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start_idx = (page - 1) * page_size
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end_idx = start_idx + page_size
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page_alerts = alerts[start_idx:end_idx]
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items = [
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InboxItem(
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@@ -327,7 +233,7 @@ async def submit_feedback(request: FeedbackRequest):
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# Alert Status aktualisieren
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alert.status = AlertStatus.REVIEWED
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# Profile aktualisieren (Default-Profile für Demo)
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# Profile aktualisieren (Default-Profile fuer Demo)
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profile = _profile_store.get("default")
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if not profile:
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profile = RelevanceProfile.create_default_education_profile()
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@@ -353,7 +259,7 @@ async def get_profile(user_id: Optional[str] = Query(default=None)):
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"""
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Relevanz-Profil abrufen.
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Ohne user_id wird das Default-Profil zurückgegeben.
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Ohne user_id wird das Default-Profil zurueckgegeben.
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"""
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profile_id = user_id or "default"
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profile = _profile_store.get(profile_id)
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@@ -385,7 +291,7 @@ async def update_profile(
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"""
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Relevanz-Profil aktualisieren.
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Erlaubt Anpassung von Prioritäten, Ausschlüssen und Policies.
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Erlaubt Anpassung von Prioritaeten, Ausschluessen und Policies.
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"""
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profile_id = user_id or "default"
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profile = _profile_store.get(profile_id)
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@@ -431,34 +337,24 @@ async def update_profile(
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@router.get("/stats")
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async def get_stats():
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"""
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Statistiken über Alerts und Scoring.
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Gibt Statistiken im Format zurück, das das Frontend erwartet:
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- total_alerts, new_alerts, kept_alerts, review_alerts, dropped_alerts
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- total_topics, active_topics, total_rules
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Statistiken ueber Alerts und Scoring.
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"""
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alerts = list(_alerts_store.values())
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total = len(alerts)
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# Zähle nach Status und Decision
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new_alerts = sum(1 for a in alerts if a.status == AlertStatus.NEW)
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kept_alerts = sum(1 for a in alerts if a.relevance_decision == "KEEP")
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review_alerts = sum(1 for a in alerts if a.relevance_decision == "REVIEW")
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dropped_alerts = sum(1 for a in alerts if a.relevance_decision == "DROP")
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# Topics und Rules (In-Memory hat diese nicht, aber wir geben 0 zurück)
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# Bei DB-Implementierung würden wir hier die Repositories nutzen
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total_topics = 0
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active_topics = 0
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total_rules = 0
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# Versuche DB-Statistiken zu laden wenn verfügbar
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try:
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from alerts_agent.db import get_db
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from alerts_agent.db.repository import TopicRepository, RuleRepository
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from contextlib import contextmanager
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# Versuche eine DB-Session zu bekommen
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db_gen = get_db()
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db = next(db_gen, None)
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if db:
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@@ -478,15 +374,12 @@ async def get_stats():
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except StopIteration:
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pass
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except Exception:
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# DB nicht verfügbar, nutze In-Memory Defaults
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pass
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# Berechne Durchschnittsscore
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scored_alerts = [a for a in alerts if a.relevance_score is not None]
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avg_score = sum(a.relevance_score for a in scored_alerts) / len(scored_alerts) if scored_alerts else 0.0
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return {
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# Frontend-kompatibles Format
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"total_alerts": total,
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"new_alerts": new_alerts,
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"kept_alerts": kept_alerts,
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@@ -496,7 +389,6 @@ async def get_stats():
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"active_topics": active_topics,
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"total_rules": total_rules,
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"avg_score": avg_score,
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# Zusätzliche Details (Abwärtskompatibilität)
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"by_status": {
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"new": new_alerts,
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"scored": sum(1 for a in alerts if a.status == AlertStatus.SCORED),
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