update
This commit is contained in:
team3
@@ -17,47 +17,47 @@ class GuideCreateRequest(BaseModel):
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format: FormatType
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instructions: str = Field(default="", max_length=2000)
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provider: ProviderType = "claude"
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ab_step: int | None = Field(default=None, ge=0, le=4) # Re-Run ab Guide-Schritt (0 Gliederung … 4 Lese-Prüfung); None = voll/Resume
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ab_step: int | None = Field(default=None, ge=0, le=4) # re-run from guide step (0 outline … 4 read-exam); None = full/resume
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class TopicCreateRequest(BaseModel):
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name: str = Field(min_length=1, max_length=100)
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class BausteineCreateRequest(BaseModel):
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class BlocksCreateRequest(BaseModel):
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topic: str = Field(min_length=1, max_length=100)
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instructions: str = Field(default="", max_length=2000)
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provider: ProviderType = "claude"
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source_type: SourceType = "thema"
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source_ort: str = Field(default="", max_length=2000)
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ab_phase: int | None = Field(default=None, ge=1, le=9) # Re-Run ab grober Phase (Position in _phasen(topic), 1-based; bis 9: …Gliederung/Fragen/Artefakte); None = Resume/Fortsetzen ohne Löschen
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ab_step: int | None = Field(default=None, ge=0) # Re-Run ab feinem Teilschritt (0-basierter Index in _bausteine_steps); hat Vorrang vor ab_phase
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source_location: str = Field(default="", max_length=2000)
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ab_phase: int | None = Field(default=None, ge=1, le=9) # re-run from a coarse phase (position in _phasen(topic), 1-based; up to 9: …outline/questions/artifacts); None = resume/continue without deleting
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ab_step: int | None = Field(default=None, ge=0) # re-run from a fine sub-step (0-based index into _blocks_steps); takes precedence over ab_phase
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class BausteineResetStepRequest(BaseModel):
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class BlocksResetStepRequest(BaseModel):
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topic: str = Field(min_length=1, max_length=100)
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ab_step: int = Field(ge=0) # NUR zurücksetzen ab diesem Teilschritt (kein Neu-Generieren)
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ab_step: int = Field(ge=0) # ONLY reset from this sub-step (no regeneration)
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class BausteineStep(BaseModel):
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class BlocksStep(BaseModel):
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label: str
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state: Literal["done", "active", "pending"]
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class BausteineFeinStep(BaseModel):
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class BlocksFineStep(BaseModel):
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label: str
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phase: str = ""
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state: Literal["done", "active", "pending"]
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class BausteineStatusResponse(BaseModel):
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class BlocksStatusResponse(BaseModel):
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ready: bool
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generating: bool
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progress: str | None = None
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error: str | None = None
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partial: bool = False
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steps: list[BausteineStep] = []
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feine_steps: list[BausteineFeinStep] = []
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steps: list[BlocksStep] = []
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feine_steps: list[BlocksFineStep] = []
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class ProjectResponse(BaseModel):
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@@ -66,33 +66,33 @@ class ProjectResponse(BaseModel):
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class FolderResponse(BaseModel):
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name: str
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ort: str # relativer Pfad ab Repo-Root (z.B. "projects/foo")
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location: str # path relative to the repo root (e.g. "projects/foo")
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class BausteineQuelleUpdate(BaseModel):
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class BlocksSourceUpdate(BaseModel):
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topic: str = Field(min_length=1, max_length=100)
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type: SourceType = "thema"
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ort: str = Field(default="", max_length=2000)
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location: str = Field(default="", max_length=2000)
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spec: str = Field(default="", max_length=2000)
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class BausteineQuelleResponse(BaseModel):
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class BlocksSourceResponse(BaseModel):
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type: SourceType
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ort: str
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location: str
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spec: str
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class SubbausteinInfo(BaseModel):
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titel: str
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stufe: Literal["anfaenger", "fortgeschritten", "experte", "einfach", "mittel", "schwer"]
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relevanz: Literal["relevant", "rand"] | None = None
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class SubblockInfo(BaseModel):
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title: str
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level: Literal["beginner", "advanced", "expert", "easy", "medium", "hard"]
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relevance: Literal["relevant", "peripheral"] | None = None
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class BausteinUebersicht(BaseModel):
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class BlockOverview(BaseModel):
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num: int
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titel: str
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beschreibung: str = ""
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subbausteine: list[SubbausteinInfo] = []
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title: str
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description: str = ""
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subblocks: list[SubblockInfo] = []
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class ProviderInfo(BaseModel):
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@@ -168,7 +168,7 @@ class ElementCheckResponse(BaseModel):
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class ElementStyleChange(BaseModel):
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text: str
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action: Literal["entfernen", "anpassen", "hinzufuegen"]
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action: Literal["remove", "adjust", "add"]
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target: Literal["title", "description", "examples", "hints"]
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index: int | None = None
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content: str = ""
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@@ -207,104 +207,104 @@ class ProgressResponse(BaseModel):
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chapters: list[str]
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# --- Baustein-Lernen ---
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# --- Block learning ---
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class BausteinChatRequest(BaseModel):
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class BlockChatRequest(BaseModel):
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topic: str = Field(min_length=1, max_length=100)
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baustein: str = Field(min_length=1, max_length=200)
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section: str = Field(default="", max_length=20000) # ausführliche Fassung
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section_kompakt: str = Field(default="", max_length=20000) # kompakte Fassung (Merksätze)
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block: str = Field(min_length=1, max_length=200)
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section: str = Field(default="", max_length=20000) # detailed version
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section_compact: str = Field(default="", max_length=20000) # compact version (mnemonics)
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messages: list[ChatMessage] = Field(min_length=1)
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provider: ProviderType = "claude"
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class BausteinChatResponse(BaseModel):
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class BlockChatResponse(BaseModel):
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reply: str
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class BausteinPruefungRequest(BaseModel):
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class BlockExamRequest(BaseModel):
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topic: str = Field(min_length=1, max_length=100)
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baustein: str = Field(min_length=1, max_length=200)
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section: str = Field(default="", max_length=20000) # ausführliche Fassung
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section_kompakt: str = Field(default="", max_length=20000) # kompakte Fassung (Merksätze)
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aktion: Literal[
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"frage", "diskussion", "antwort", "antwort_pruefen",
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"quiz_frage", "quiz_antwort", "lueck_frage", "lueck_antwort",
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] = "frage"
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frage: str = Field(default="", max_length=2000) # aktuell geprüfte Frage (für diskussion/antwort); Anker der Basis
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auswahl: list[int] = [] # Quiz/Lückentext-Auswahl: vom Lerner gewählte Options-Indizes
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korrekt: list[int] = [] # Quiz/Lückentext-Auswahl: korrekte Indizes (Client hält sie aus der Generierung)
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loesung: str = Field(default="", max_length=500) # Lückentext frei: erwarteter Begriff
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alternativen: list[str] = [] # Lückentext frei: akzeptierte Synonyme
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eingabe: str = Field(default="", max_length=500) # Lückentext frei: getippter Begriff
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schwer: bool = False # Variante: leicht (+1/−1) vs schwer (+3/−1)
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letzte_bewertung: str = Field(default="", max_length=2000) # Feedback der letzten Bewertung (Kontext für diskussion)
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vermeide: list[str] = [] # schon gestellte + vorgemerkte Fragen — sinngemäß nicht wiederholen
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nachgefragt: bool = False # für diese Frage wurde nachgefragt → Gewinn auf +1 gedeckelt
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begruendung: str = Field(default="", max_length=2000) # „Gründlich prüfen": warum mit der Bewertung unzufrieden
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muster: str = Field(default="", max_length=2000) # gezogenes Frage-Muster (Saat); leer → Live-Generierung (Fallback)
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# Basis + cap werden serverseitig geführt (Anker bzw. Subs×25) — Client-cap nur Hinweis.
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cap: int = Field(default=10, ge=1, le=10000) # Score-Deckel = freigeschaltete Subbausteine × 25
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messages: list[ChatMessage] = [] # Dialog bisher; leer = erste Frage
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block: str = Field(min_length=1, max_length=200)
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section: str = Field(default="", max_length=20000) # detailed version
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section_compact: str = Field(default="", max_length=20000) # compact version (mnemonics)
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action: Literal[
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"question", "discussion", "answer", "answer_check",
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"quiz_question", "quiz_answer", "gap_question", "gap_answer",
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] = "question"
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question: str = Field(default="", max_length=2000) # currently checked question (for discussion/answer); base anchor
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selection: list[int] = [] # quiz/gap-text choice: option indices picked by the learner
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correct: list[int] = [] # quiz/gap-text choice: correct indices (the client keeps them from generation)
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solution: str = Field(default="", max_length=500) # gap text free: expected term
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alternatives: list[str] = [] # gap text free: accepted synonyms
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input: str = Field(default="", max_length=500) # gap text free: typed term
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schwer: bool = False # variant: easy (+1/−1) vs hard (+3/−1)
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last_rating: str = Field(default="", max_length=2000) # feedback of the last rating (context for discussion)
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avoid: list[str] = [] # already-asked + earmarked questions — don't repeat them in substance
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asked_again: bool = False # asked_again was used for this question → gain capped at +1
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reason: str = Field(default="", max_length=2000) # "thorough check": why dissatisfied with the rating
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pattern: str = Field(default="", max_length=2000) # drawn question pattern (seed); empty → live generation (fallback)
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# Base + cap are kept server-side (anchor / subs×25) — the client cap is only a hint.
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cap: int = Field(default=10, ge=1, le=10000) # score cap = unlocked subblocks × 25
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messages: list[ChatMessage] = [] # dialog so far; empty = first question
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provider: ProviderType = "claude"
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gruendlich: bool = False # „Gründlich prüfen": Bewertung mit starkem Modell (role guide)
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thorough: bool = False # "thorough check": rating with a strong model (role guide)
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class QuizOption(BaseModel):
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text: str
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korrekt: bool
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correct: bool
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class BausteinPruefungResponse(BaseModel):
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frage: str | None = None
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class BlockExamResponse(BaseModel):
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question: str | None = None
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reply: str | None = None
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feedback: str | None = None
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punkte: int | None = None # Punkt-Delta dieser Antwort (−2 … +3); schnell = voraussichtlich
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bewertung: Literal["gut", "neutral", "schlecht"] | None = None # aus Vorzeichen, fürs Einfärben
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optionen: list[QuizOption] | None = None # Quiz: 4 Optionen + Korrekt-Flags
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satz: str | None = None # Lückentext: Satz mit Lücke (___)
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loesung: str | None = None # Lückentext: erwarteter Begriff
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alternativen: list[str] | None = None # Lückentext: akzeptierte Synonyme
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gute_antworten: int
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streak: int = 0 # aktuelle Serie korrekter Antworten (je Baustein)
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cap: int = 10 # cap_final = alle Subs × 25 — Frontend leitet die Lernstufe ab
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points: int | None = None # points delta of this answer (−2 … +3); fast = expected
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rating: Literal["gut", "neutral", "schlecht"] | None = None # from the sign, for coloring
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options: list[QuizOption] | None = None # quiz: 4 options + correct flags
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sentence: str | None = None # gap text: sentence with a gap (___)
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solution: str | None = None # gap text: expected term
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alternatives: list[str] | None = None # gap text: accepted synonyms
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good_answers: int
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streak: int = 0 # current run of correct answers (per block)
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cap: int = 10 # cap_final = all subs × 25 — the frontend derives the learning level
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class BausteinLernstand(BaseModel):
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gute_antworten: int
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class BlockLearnState(BaseModel):
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good_answers: int
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streak: int = 0
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cap: int = 0 # cap_final = alle Subbausteine × 25
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cap_aktuell: int = 0 # erreichbarer cap der aktuell freigeschalteten Ebene
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freie_ebene: int = 1 # 1=A · 2=F · 3=E · 4=V
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cap: int = 0 # cap_final = all subblocks × 25
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cap_aktuell: int = 0 # reachable cap of the currently unlocked level
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freie_level: int = 1 # 1=A · 2=F · 3=E · 4=V
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class BausteinLernstandResponse(BaseModel):
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bausteine: dict[str, BausteinLernstand]
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class BlockLearnStateResponse(BaseModel):
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blocks: dict[str, BlockLearnState]
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# --- Block-Inhalt: einen Abschnitt on-demand prüfen + übernehmen (Fokus, Rechtsklick) ---
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# --- Block content: check + apply one section on demand (focus, right-click) ---
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class BlockPruefenRequest(BaseModel):
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baustein: str = Field(min_length=1, max_length=200)
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stelle: str = "ausführlich" # "kompakt" | "ausführlich" (angezeigtes Feld)
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block: str = Field(min_length=1, max_length=20000) # roher Markdown-Block
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hinweis: str = Field(default="", max_length=2000) # optionaler Zusatz (✏️)
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block: str = Field(min_length=1, max_length=200)
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spot: str = "ausführlich" # "compact" | "ausführlich" (displayed field)
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snippet: str = Field(min_length=1, max_length=20000) # raw markdown block
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hint: str = Field(default="", max_length=2000) # optional addition (✏️)
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provider: ProviderType = "claude"
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class BlockPruefenResponse(BaseModel):
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neu: str # korrigierter Block als Markdown
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revised: str # corrected block as markdown
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class BlockUebernehmenRequest(BaseModel):
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baustein: str = Field(min_length=1, max_length=200)
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stelle: str = "ausführlich"
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block: str = Field(min_length=1, max_length=200)
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spot: str = "ausführlich"
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alt: str = Field(min_length=1, max_length=20000)
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neu: str = Field(default="", max_length=20000)
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revised: str = Field(default="", max_length=20000)
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provider: ProviderType = "claude"
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class BlockUebernehmenResponse(BaseModel):
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kompakt: str
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compact: str
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md: str
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gefunden: bool
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found: bool
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