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team3
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backend/config.py
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150
backend/config.py
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"""Konstanten + .env-Loader. Regel: .env-Datei gewinnt über geerbtes Env
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(creator2-Lektion 12: --reload-Master pinnte sonst stale Werte)."""
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import os
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import re
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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def _load_env() -> None:
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datei = ROOT / ".env"
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if not datei.exists():
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return
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for zeile in datei.read_text().splitlines():
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zeile = zeile.strip()
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if not zeile or zeile.startswith("#") or "=" not in zeile:
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continue
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k, v = zeile.split("=", 1)
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v = v.split("#", 1)[0].strip()
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if v:
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os.environ[k.strip()] = v
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_load_env()
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FAKE = bool(os.getenv("CREATOR_FAKE"))
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LLM_LOG = bool(os.getenv("LLM_LOG")) # 1 = Prompt+Antwort je Call in call_texte
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DB_PFAD = Path(os.getenv("CREATOR_DB") or ROOT / "storage" / "creator3.db")
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KORPUS_DIR = ROOT / "storage" / "korpus"
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SKILLS_DIR = ROOT / "skills"
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PIPELINE_YAML = ROOT / "pipeline.yaml"
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TOPIC_NAME_RE = re.compile(r"^[a-zA-Z0-9äöüÄÖÜß][a-zA-Z0-9äöüÄÖÜß _-]{0,63}$")
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# ── LLM / MiniMax (R3/R9) — alles M3; M2.x kann Thinking nicht abschalten ──
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LLM_ENDPOINT = "https://api.minimax.io/anthropic/v1/messages"
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ROLLEN = {
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# thinking IMMER explizit ("enabled" gibt 400; Default widersprüchlich dokumentiert)
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"extraktion": dict(model="MiniMax-M3", thinking="disabled", temperature=0.2),
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"judge": dict(model="MiniMax-M3", thinking="disabled", temperature=0.1),
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"writer": dict(model="MiniMax-M3", thinking="adaptive", temperature=0.7),
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}
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LLM_MAX_TOKENS = 32_000
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MAX_PARALLEL_LLM = int(os.getenv("MAX_PARALLEL_LLM", "12")) # M3: 200 RPM → 8-16 sicher
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TAKT_SEKUNDEN = float(os.getenv("TAKT_SEKUNDEN", "3")) # 1 Call-Start je Takt; 0 = Breiten-Drossel
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BUENDEL = {"lang": 1, "mittel": 3, "kurz": 10} # Tasks je Call nach Knoten-Klasse
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INFRA_MAX_RETRIES = 3
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INFRA_BACKOFF_BASE = 8.0 # → 8/16/32 s, mit Jitter (Retry-After unzuverlässig)
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INHALT_MAX_RESTARTS = 2
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DROSSEL_MIN_BREITE = 4 # 429/1002/1041/2045: Breite halbieren, 20 Erfolge = +1
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DROSSEL_ERFOLGE_JE_PLUS = 20
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HEDGE_NACH_S = int(os.getenv("HEDGE_NACH_S", "90")) # Zwilling nach max(90, timeout/2); 0 = aus
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RUN_BUDGET_TOKENS = int(os.getenv("RUN_BUDGET_TOKENS", "10000000")) # DEFAULT AN; 0 = aus
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PREIS_IN, PREIS_OUT = 0.60, 2.40 # $/1M konservativ (R9), real messen
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# Timeouts je Knoten: (basis_s, pro_item_s) — creator2-Lektion 14: nie fix;
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# pro_item skaliert mit der Bündel-Größe (n in llm.call)
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TIMEOUTS = {
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"recherche_plan": (60, 20), "soll_extraktion": (180, 60), "soll_konsens": (240, 2),
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"atom_extraktion": (240, 60), "anker_fix": (180, 4), "dedup": (120, 8),
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"soll_zuordnung": (120, 3), "luecke": (180, 45), "lernziele": (120, 3),
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"bausteine": (120, 3), "ordnung": (180, 3), "writer": (420, 0),
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"kapitel_intro": (90, 0), "llm_pruefung": (240, 60), "fix": (300, 0),
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"redundanz": (120, 8), "klaerung": (180, 0),
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}
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TIMEOUT_DEFAULT = (180, 0)
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# ── Suche (R2) ──
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SUCHE_TIMEOUT_S = 10
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SUCHE_RETRIES = 1
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CIRCUIT_BREAKER_N = 3
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WIKI_MAX_PARALLEL = 3
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DDGS_PAUSE_S = 1.5
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SUCHE_MAX_TREFFER = 8
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# ── Laden (R6) ──
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FETCH_CONNECT_S = 10
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FETCH_READ_S = 30
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DOMAIN_RATE_S = 1.0
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SNAPSHOT_MIN_ZEICHEN = 500
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# ── Korpus ──
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RECHERCHE_LENSES = ("ueberblick", "tutorial", "referenz", "praxis") # generisch!
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RECHERCHE_RUNDEN_MAX = int(os.getenv("RECHERCHE_RUNDEN_MAX", "4"))
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QUERIES_JE_LENS = 3
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SOLL_CHUNK_CHARS = 40_000
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SOLL_MIN_BELEGE = 2 # ≥2 unabhängige Quellen je bestätigtem Punkt
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SOLL_PUNKTE_MIN = int(os.getenv("SOLL_PUNKTE_MIN", "5"))
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SOLL_BAND_FAKTOR = 1.0 # Ziel ≈ √Kandidaten (creator2-Band — Lektion 90)
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SOLL_BAND_TOLERANZ = 1.5 # mehr als Ziel×1.5 bestätigt → nochmal falten
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QUELLEN_MAX = int(os.getenv("QUELLEN_MAX", "24")) # harter Korpus-Deckel
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EXTRAKT_FENSTER = 4_000 # Atome nur ±Fenster um Soll-Belege (belegnah)
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# ── Inventar ──
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ABSCHNITT_CHARS = 6_000 # 12k riss das 32k-Output-Cap (creator2 empirisch)
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READER_JE_ABSCHNITT = 2 # Konsens entsteht über Dedup, nicht Union
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CHUNK_HALBIER_TIEFE = 2
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ANKER_OVERLAP_MERGE = 0.5
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FUZZY_MIN_ZEICHEN = 40
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FUZZY_FEHLERQUOTE = 0.08
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WORT_MIN = 8
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WORT_FEHLERQUOTE = 0.25
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ANKER_FIX_BATCH = 20
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MERGE_PANEL = 2 # Merge nur einstimmig (Falsch-Merge >> Dublette)
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DEDUP_JACCARD = 0.3 # gemessener Paraphrasen-Floor
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DEDUP_TITEL_FUZZY = 85 # nur bei ≥2 Inhaltstoken (Subset-100-Falle)
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DEDUP_PAARE_PRO_CALL = 10
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DEDUP_KANDIDATEN_CAP = 400
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VERDICHTUNG_ZIEL = 8 # weiches Ziel Atome je Soll-Punkt (kein Cap!)
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LUECKEN_RUNDEN_MAX = 2
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WEB_NACHRECHERCHE_RUNDEN = 1
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LUECKE_FENSTER = 6_000
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BELEG_FENSTER_VOR, BELEG_FENSTER_NACH = 300, 1200
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# ── Struktur ──
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KAPITEL_SOLL_PUNKTE = 8
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BAUSTEIN_MIN_ATOME, BAUSTEIN_MAX_ATOME = 4, 8
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ZIELE_CHUNK_ATOME = 30
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# ── Guide ──
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SECTION_WOERTER_PRO_ATOM = (40, 220) # QA-Band = ×1.25
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PRUEF_PANEL = 2
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KLAERUNG_PANEL = 3
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FIX_MAX_VERSUCHE = 3
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REDUNDANZ_JACCARD = 0.5 # Satzpaare zwischen Bausteinen (Paraphrasen!)
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REDUNDANZ_SHINGLE = 5 # gemeinsame 5-Wort-Folge = Kandidat
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REDUNDANZ_SATZ_MIN_TOKEN = 6
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ZIEL_DEDUP_JACCARD = 0.5 # synonyme Lernziele deterministisch mergen
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ECHO_JACCARD = 0.7 # 1. Satz nach Marker ≈ Atom-Definition = Echo
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SOLL_MIN_BELEGE_WIKI = 1 # kuratierte Quelle (Wikipedia) = 1 Beleg genügt
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# ── Engine (R8) ──
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POLL_SEKUNDEN = 2.0
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MAX_VERSUCHE = 2 # Inhalts-Versuche je Task; Infra zählt separat
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STILLSTAND_N = 2 # identischer Befund-Fingerprint n× → PAUSE
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GATE_RUNDEN_MAX = {"gate_korpus": 4, "gate_inventar": 8, "gate_struktur": 3, "gate_guide": 12}
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# gate_inventar 6: Anker-Fix, 2× Dedup/Lücke, Web-Nachrecherche, Nachzügler-Dedup
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# gate_guide 8: Echtlauf konvergierte 45→28→10 — der Stillstand-Fingerprint
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# fängt echte Schleifen, das Runden-Limit ist nur die Notbremse dahinter
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MAX_PARALLEL_GLOBAL = 24
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def timeout_fuer(knoten: str, n: int = 0) -> float:
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basis, pro = TIMEOUTS.get(knoten, TIMEOUT_DEFAULT)
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return basis + pro * n
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def user_agent() -> str:
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mail = os.getenv("KONTAKT_MAIL", "")
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return f"creator3/0.1 (+mailto:{mail})" if mail else "creator3/0.1"
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