shinkan-jinkendo/backend/planning_exercise_semantics.py

"""
Planungs-KI Phase E: Semantik-Schicht für Anfrage-Verständnis und Retrieval.

Trennt anfrage-spezifische Semantik (Technik, Phrasen, Entwicklungsbogen) vom
Katalog-Profil-Overlay (Fokus/Skills). Wird in Hybrid-Retrieval und Pfad-QA genutzt.
"""
from __future__ import annotations

import json
import logging
import re
from dataclasses import dataclass, field
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple

from pydantic import BaseModel, Field, field_validator

from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
from exercise_ai import strip_html_to_plain
from openrouter_chat import (
    effective_openrouter_model_for_prompt_row,
    normalize_openrouter_env,
    openrouter_chat_completion,
)

_logger = logging.getLogger("shinkan.planning_exercise_semantics")

_GERI_TECHNIQUES: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
    ("mae geri", ("mawashi geri", "yoko geri", "ushiro geri", "sakuto geri", "mikazuki geri")),
    ("mawashi geri", ("mae geri", "yoko geri", "ushiro geri", "sakuto geri")),
    ("yoko geri", ("mae geri", "mawashi geri", "ushiro geri", "sakuto geri")),
    ("ushiro geri", ("mae geri", "mawashi geri", "yoko geri", "sakuto geri")),
    ("sakuto geri", ("mae geri", "mawashi geri", "yoko geri", "mikazuki geri")),
    ("mikazuki geri", ("mae geri", "mawashi geri", "sakuto geri")),
)

_OTHER_TECHNIQUE_PATTERNS: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
    ("oi zuki", ("gyaku zuki", "age uke", "gedan barai")),
    ("gyaku zuki", ("oi zuki", "mae geri")),
    ("age uke", ("gedan barai", "soto uke")),
    ("gedan barai", ("age uke", "soto uke")),
)

_TECHNIQUE_EXPECTED_SKILLS: Dict[str, Tuple[str, ...]] = {
    "mae geri": ("Geri Waza", "Koordination", "Gleichgewicht", "Kime"),
    "mawashi geri": ("Geri Waza", "Koordination", "Gleichgewicht"),
    "yoko geri": ("Geri Waza", "Koordination", "Gleichgewicht"),
    "ushiro geri": ("Geri Waza", "Koordination", "Gleichgewicht"),
    "sakuto geri": ("Geri Waza", "Koordination", "Gleichgewicht"),
    "mikazuki geri": ("Geri Waza", "Koordination", "Gleichgewicht"),
}

_DEFAULT_TECHNIQUE_SKILLS: Tuple[str, ...] = ("Geri Waza", "Koordination", "Gleichgewicht")

_ARC_PHASES: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
    ("einstieg", ("einstieg", "erlernen", "lernen", "anfänger", "anfaenger", "beginn", "grund")),
    ("grundlage", ("grundlage", "fundament", "basis", "basic")),
    ("vertiefung", ("vertief", "festigung", "übung", "uebung", "wiederhol")),
    ("anwendung", ("anwend", "partner", "kampf", "kumite", "reaktion")),
    ("perfektion", ("perfekt", "meisterschaft", "höchst", "hoechst", "kime", "sauber")),
)

_PHASE_QUERY_HINTS: Dict[str, str] = {
    "einstieg": "einstieg grundübung einfach",
    "grundlage": "grundtechnik festigung",
    "vertiefung": "vertiefung technik übung",
    "anwendung": "anwendung partner variante",
    "perfektion": "perfektion kontrolle kime höchste stufe",
}

_QUERY_STOPWORDS = frozenset(
    {
        "von",
        "bis",
        "zur",
        "zum",
        "der",
        "die",
        "das",
        "des",
        "den",
        "dem",
        "ein",
        "eine",
        "einer",
        "eines",
        "und",
        "oder",
        "mit",
        "für",
        "fuer",
        "im",
        "in",
        "am",
        "an",
        "auf",
        "aus",
        "beim",
        "nach",
        "vor",
        "über",
        "ueber",
        "unter",
        "wie",
        "was",
        "wo",
        "wir",
        "soll",
        "sollen",
        "bitte",
        "schlage",
        "vorschlag",
        "übung",
        "uebung",
        "übungen",
        "uebungen",
    }
)


class PlanningSemanticBrief(BaseModel):
    primary_topic: Optional[str] = Field(default=None, max_length=120)
    topic_type: str = Field(default="general", max_length=40)
    must_phrases: List[str] = Field(default_factory=list)
    exclude_phrases: List[str] = Field(default_factory=list)
    development_arc: List[str] = Field(default_factory=list)
    retrieval_query: str = Field(default="", max_length=500)
    semantic_strength: float = Field(default=0.0, ge=0.0, le=1.0)
    rationale: Optional[str] = Field(default=None, max_length=400)

    @field_validator("topic_type")
    @classmethod
    def _topic_type(cls, v: str) -> str:
        s = (v or "general").strip().lower()
        return s if s in {"general", "technique", "focus", "method", "skill"} else "general"

    @field_validator("must_phrases", "exclude_phrases", "development_arc", mode="before")
    @classmethod
    def _norm_phrase_list(cls, v: Any) -> List[str]:
        if not v:
            return []
        if isinstance(v, str):
            s = _normalize_phrase(v)
            return [s] if s else []
        out: List[str] = []
        for item in v:
            s = _normalize_phrase(str(item or ""))
            if s and s not in out:
                out.append(s[:120])
        return out[:12]


def _normalize_phrase(text: str) -> str:
    return re.sub(r"\s+", " ", (text or "").strip().lower())


_STAGE_TITLE_STOP = frozenset(
    {"für", "fur", "und", "der", "die", "das", "mit", "im", "in", "am", "an", "zur", "zum", "den", "dem", "des"}
)


def _stage_title_tokens(text: str) -> List[str]:
    return [
        tok
        for tok in _normalize_phrase(text).split()
        if tok not in _STAGE_TITLE_STOP and len(tok) > 1
    ]


def exercise_title_equivalent_to_stage_goal(title: str, learning_goal: str) -> bool:
    """
    Titel entspricht dem Stufen-Lernziel (wortgleich oder nahezu identisch).

    Deckt Graph-Slots ab, bei denen die Übung gezielt zum Lernziel angelegt wurde,
    ohne dass die Pfad-Haupttechnik im Übungstext vorkommt.
    """
    t = _normalize_phrase(title)
    lg = _normalize_phrase(learning_goal)
    if len(t) < 3 or len(lg) < 3:
        return False
    if t == lg:
        return True
    shorter, longer = (t, lg) if len(t) <= len(lg) else (lg, t)
    if shorter in longer and len(shorter) >= 8 and len(shorter) / max(len(longer), 1) >= 0.72:
        return True
    t_tok = _stage_title_tokens(title)
    lg_tok = _stage_title_tokens(learning_goal)
    if len(t_tok) >= 2 and t_tok == lg_tok:
        return True
    if len(t_tok) >= 2 and len(lg_tok) >= 2:
        t_set = set(t_tok)
        lg_set = set(lg_tok)
        overlap = len(t_set & lg_set)
        if overlap >= 2 and overlap / max(len(t_set), len(lg_set)) >= 0.85:
            return True
    return False


def _normalize_query(text: str) -> str:
    return re.sub(r"\s+", " ", (text or "").strip())


def _extract_json_object(text: str) -> Dict[str, Any]:
    s = (text or "").strip()
    if s.startswith("```"):
        s = re.sub(r"^```[a-zA-Z0-9]*\s*", "", s)
        if s.endswith("```"):
            s = s[:-3].strip()
    start = s.find("{")
    end = s.rfind("}")
    if start < 0 or end <= start:
        raise ValueError("Kein JSON-Objekt in LLM-Antwort")
    obj = json.loads(s[start : end + 1])
    if not isinstance(obj, dict):
        raise ValueError("LLM-Antwort ist kein JSON-Objekt")
    return obj


def _find_technique_in_text(q_lower: str) -> Optional[Tuple[str, Tuple[str, ...]]]:
    for primary, excludes in _GERI_TECHNIQUES + _OTHER_TECHNIQUE_PATTERNS:
        if primary in q_lower:
            return primary, excludes
    return None


def resolve_path_primary_topic(
    goal_query: str,
    semantic_brief: Optional[PlanningSemanticBrief] = None,
    *,
    stage_learning_goal: Optional[str] = None,
    extra_context: Optional[str] = None,
) -> Optional[str]:
    """
    Haupttechnik aus Anfrage, Kontext oder Stufen-Lernziel — nicht nur aus goal_query.
    """
    if semantic_brief:
        primary = (semantic_brief.primary_topic or "").strip()
        if primary:
            return primary
    parts = [goal_query or "", extra_context or "", stage_learning_goal or ""]
    combined = _normalize_phrase(" ".join(p for p in parts if p))
    if not combined:
        return None
    hit = _find_technique_in_text(combined.lower())
    return hit[0] if hit else None


def technique_sibling_excludes(primary_topic: str) -> List[str]:
    """Andere Techniken derselben Familie (z. B. Mae/Yoko bei Mawashi) — aus Katalog."""
    topic = _normalize_phrase(primary_topic)
    if not topic:
        return []
    hit = _find_technique_in_text(topic)
    if not hit:
        return []
    out: List[str] = []
    for raw in hit[1]:
        for expanded in _expand_stage_exclude_phrase(raw):
            if expanded and expanded not in out:
                out.append(expanded)
    return out[:16]


def exercise_passes_technique_path_scope(
    *,
    primary_topic: str,
    title: str,
    summary: str = "",
    goal: str = "",
    learning_goal: str = "",
    sibling_excludes: Optional[Sequence[str]] = None,
    relaxed: bool = False,
) -> bool:
    """
    Technik-Pfad: keine Geschwister-Technik; Haupttechnik muss im Übungstext vorkommen.

    Das Stufen-Lernziel allein reicht nicht — sonst würden themenfremde Übungen (z. B. Kumite)
    nur wegen „Mawashi Geri“ im Lernziel durch das Gate rutschen.
    """
    primary = _normalize_phrase(primary_topic)
    if not primary:
        return True

    blob = _blob_from_fields(title, summary, goal, [])
    excludes = list(sibling_excludes or technique_sibling_excludes(primary))
    if excludes and _blob_matches_stage_excludes(blob, excludes):
        return False

    if _phrase_in_blob(primary, blob):
        return True

    if relaxed:
        parts = [p for p in primary.split() if len(p) >= 4]
        if parts and any(_phrase_in_blob(part, blob) for part in parts):
            return True
    return False


def _detect_development_arc(q_lower: str) -> List[str]:
    found: List[str] = []
    for phase, markers in _ARC_PHASES:
        if any(m in q_lower for m in markers):
            if phase not in found:
                found.append(phase)
    if not found and ("von" in q_lower and "bis" in q_lower):
        found = ["einstieg", "perfektion"]
    return found


def _keyword_phrases_from_query(query: str) -> List[str]:
    q = _normalize_query(query).lower()
    tokens = re.findall(r"[a-zäöüß]{3,}", q, flags=re.IGNORECASE)
    phrases: List[str] = []
    for i, tok in enumerate(tokens):
        low = tok.lower()
        if low in _QUERY_STOPWORDS:
            continue
        if i + 1 < len(tokens):
            nxt = tokens[i + 1].lower()
            if nxt not in _QUERY_STOPWORDS:
                pair = _normalize_phrase(f"{low} {nxt}")
                if len(pair) >= 5 and pair not in phrases:
                    phrases.append(pair)
        if len(low) >= 4 and low not in phrases:
            phrases.append(low)
    return phrases[:6]


def build_semantic_brief(query: Optional[str]) -> PlanningSemanticBrief:
    """Deterministisches Anfrage-Verständnis — ohne LLM."""
    q = _normalize_query(query)
    if not q:
        return PlanningSemanticBrief(retrieval_query="", semantic_strength=0.0)

    q_lower = q.lower()
    must: List[str] = []
    exclude: List[str] = []
    topic_type = "general"
    primary: Optional[str] = None
    strength = 0.25

    technique = _find_technique_in_text(q_lower)
    if technique:
        primary, ex = technique
        must.append(primary)
        exclude.extend(list(ex))
        topic_type = "technique"
        strength = max(strength, 0.82)

    arc = _detect_development_arc(q_lower)
    if arc:
        strength = max(strength, 0.55 if technique else 0.45)

    # Keine generischen Stichwörter in must_phrases — sonst verwässert das Scoring.
    retrieval_parts = list(must)
    if primary:
        retrieval_parts.append(primary)
    if arc:
        retrieval_parts.extend(arc[:2])
    retrieval = " ".join(dict.fromkeys(retrieval_parts))[:500] if retrieval_parts else q

    if len(q) >= 24 and not technique:
        strength = max(strength, 0.4)

    path_constraints = parse_stage_goal_constraints(q)
    for item in path_constraints.exclude_phrases:
        if item not in exclude:
            exclude.append(item)

    return PlanningSemanticBrief(
        primary_topic=primary,
        topic_type=topic_type,
        must_phrases=must[:8],
        exclude_phrases=exclude[:10],
        development_arc=arc[:5],
        retrieval_query=retrieval[:500],
        semantic_strength=min(1.0, round(strength, 3)),
        rationale=None,
    )


def merge_semantic_brief_llm(
    base: PlanningSemanticBrief,
    llm_obj: Mapping[str, Any],
) -> PlanningSemanticBrief:
    """LLM-Enrichment in deterministisches Brief mergen (LLM ergänzt, ersetzt nicht harte Technik-Regeln)."""
    data = base.model_dump()
    for key in ("primary_topic", "topic_type", "rationale"):
        val = llm_obj.get(key)
        if val:
            data[key] = val

    for key in ("must_phrases", "exclude_phrases", "development_arc"):
        extra = llm_obj.get(key) or []
        merged = list(data.get(key) or [])
        for item in extra:
            s = _normalize_phrase(str(item or ""))
            if s and s not in merged:
                merged.append(s)
        data[key] = merged[:12]

    llm_strength = llm_obj.get("semantic_strength")
    if llm_strength is not None:
        try:
            data["semantic_strength"] = min(
                1.0,
                max(float(data["semantic_strength"]), float(llm_strength)),
            )
        except (TypeError, ValueError):
            pass

    if data.get("must_phrases"):
        core = semantic_core_phrases(PlanningSemanticBrief.model_validate(data))
        data["retrieval_query"] = " ".join(core[:4])[:500] if core else data.get("retrieval_query", "")
    out = PlanningSemanticBrief.model_validate(data)
    if out.primary_topic and out.topic_type == "general":
        out = out.model_copy(update={"topic_type": "technique"})
    return out


def try_enrich_semantic_brief_with_llm(
    cur,
    query: str,
    base: PlanningSemanticBrief,
) -> Tuple[PlanningSemanticBrief, bool]:
    api_key, _ = normalize_openrouter_env()
    if not api_key or base.semantic_strength < 0.35:
        return base, False
    if not (query or "").strip():
        return base, False

    variables = {
        "search_query": (query or "").strip(),
        "semantic_brief_json": json.dumps(brief_to_summary_dict(base), ensure_ascii=False),
    }
    try:
        prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_query_semantics", variables)
        model = effective_openrouter_model_for_prompt_row(prow)
        raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
        obj = _extract_json_object(raw)
        return merge_semantic_brief_llm(base, obj), True
    except AiPromptUnavailableError:
        return base, False
    except Exception as exc:
        _logger.warning("Semantik-LLM fehlgeschlagen: %s", exc)
        return base, False


def brief_to_summary_dict(brief: PlanningSemanticBrief) -> Dict[str, Any]:
    return {
        "primary_topic": brief.primary_topic,
        "topic_type": brief.topic_type,
        "must_phrases": list(brief.must_phrases),
        "exclude_phrases": list(brief.exclude_phrases),
        "development_arc": list(brief.development_arc),
        "retrieval_query": brief.retrieval_query,
        "semantic_strength": brief.semantic_strength,
        "rationale": brief.rationale,
    }


def step_phase_for_index(brief: PlanningSemanticBrief, step_index: int, max_steps: int) -> Optional[str]:
    arc = list(brief.development_arc or [])
    if not arc:
        if max_steps <= 1:
            return None
        default_arc = ["einstieg", "grundlage", "vertiefung", "anwendung", "perfektion"]
        arc = default_arc[:max_steps] if brief.semantic_strength >= 0.5 else []
    if not arc:
        return None
    if len(arc) == 1:
        return arc[0]
    pos = step_index / max(max_steps - 1, 1)
    idx = min(len(arc) - 1, int(round(pos * (len(arc) - 1))))
    return arc[idx]


def step_retrieval_query(
    brief: PlanningSemanticBrief,
    goal_query: str,
    step_index: int,
    max_steps: int,
) -> str:
    phase = step_phase_for_index(brief, step_index, max_steps)
    parts: List[str] = []
    if brief.primary_topic:
        parts.append(brief.primary_topic)
    elif brief.retrieval_query:
        parts.append(brief.retrieval_query.split()[0] if brief.retrieval_query else "")
    if phase:
        parts.append(phase)
    if not parts and brief.retrieval_query:
        parts.append(brief.retrieval_query)
    elif not parts and goal_query:
        parts.append(goal_query)
    return _normalize_query(" ".join(p for p in parts if p)) or _normalize_query(goal_query)


def apply_dynamic_retrieval_weights(
    base_weights: Mapping[str, float],
    brief: PlanningSemanticBrief,
    *,
    scenario: str,
    has_planning_reference: bool,
) -> Dict[str, float]:
    """Semantik-Kanal dynamisch gegen Profil/Plan abwägen."""
    out = dict(base_weights)
    sem = float(brief.semantic_strength or 0.0)
    if sem <= 0.05:
        out.setdefault("semantic", 0.0)
        return out

    query_driven = scenario == "free_search" or not has_planning_reference
    sem_weight = 0.12 + sem * (0.38 if query_driven else 0.22)
    out["semantic"] = round(sem_weight, 4)

    if query_driven:
        scale = 1.0 - sem * 0.35
        out["fulltext"] = round(float(out.get("fulltext", 0.18)) * scale, 4)
        out["profile"] = round(float(out.get("profile", 0.22)) * (1.0 - sem * 0.25), 4)
    else:
        out["fulltext"] = round(float(out.get("fulltext", 0.18)) * (1.0 - sem * 0.15), 4)

    total = sum(v for k, v in out.items() if k not in {"repeat_unit", "repeat_group"} and v > 0)
    if total > 0.92:
        factor = 0.88 / total
        for k in list(out.keys()):
            if k in {"repeat_unit", "repeat_group"}:
                continue
            if out[k] > 0:
                out[k] = round(out[k] * factor, 4)
    return out


def _blob_from_fields(
    title: str,
    summary: str,
    goal: str,
    variant_names: Sequence[str],
) -> str:
    parts = [title or "", strip_html_to_plain(summary, max_len=600), strip_html_to_plain(goal, max_len=800)]
    parts.extend(variant_names or [])
    return " ".join(p for p in parts if p).lower()


def _compact_alpha(text: str) -> str:
    return re.sub(r"[^a-z0-9äöüß]+", "", (text or "").lower())


def _phrase_in_blob(phrase: str, blob: str) -> bool:
    ph = _normalize_phrase(phrase)
    if not ph or not blob:
        return False
    low = blob.lower()
    if ph in low:
        return True
    if _compact_alpha(ph) and _compact_alpha(ph) in _compact_alpha(low):
        return True
    if " " not in ph:
        return bool(re.search(rf"\b{re.escape(ph)}\b", low))
    return ph in low


def score_exercise_semantic_relevance(
    *,
    title: str,
    summary: str,
    goal: str,
    variant_names: Sequence[str],
    brief: PlanningSemanticBrief,
    step_phase: Optional[str] = None,
) -> Tuple[float, List[str]]:
    if brief.semantic_strength <= 0.05:
        return 0.0, []

    blob = _blob_from_fields(title, summary, goal, variant_names)
    if not blob.strip():
        return 0.0, []

    reasons: List[str] = []
    must = list(brief.must_phrases or [])
    exclude = list(brief.exclude_phrases or [])
    core = semantic_core_phrases(brief)

    core_hits = sum(1 for ph in core if _phrase_in_blob(ph, blob))
    must_hits = sum(1 for ph in must if _phrase_in_blob(ph, blob))
    exclude_hits = sum(1 for ph in exclude if _phrase_excluded_in_blob(ph, blob))

    score = 0.0
    if core:
        core_ratio = core_hits / len(core)
        score += 0.62 * core_ratio
        if core_hits == len(core):
            reasons.append("Kern-Thema der Anfrage im Übungstext")
        elif core_hits > 0:
            reasons.append("Teilweise passend zum Kern-Thema")
    elif brief.primary_topic and _phrase_in_blob(brief.primary_topic, blob):
        score += 0.55
        reasons.append(f"Thema „{brief.primary_topic}“ im Übungstext")

    if must and core != must:
        extra_ratio = must_hits / len(must)
        score += 0.12 * extra_ratio

    primary_ok = bool(core_hits) or (
        brief.primary_topic and _phrase_in_blob(brief.primary_topic, blob)
    )
    if exclude_hits > 0 and not primary_ok:
        penalty = min(0.65, 0.22 * exclude_hits)
        score -= penalty
        reasons.append("Enthält ausgeschlossene Nebenthemen")
    elif exclude_hits > 0 and primary_ok:
        score -= min(0.12, 0.06 * exclude_hits)

    if step_phase and step_phase in _PHASE_QUERY_HINTS:
        phase_markers = next((markers for phase, markers in _ARC_PHASES if phase == step_phase), ())
        if any(m in blob for m in phase_markers) or step_phase in blob:
            score += 0.12
            reasons.append(f"Passt zur Pfad-Phase „{step_phase}“")

    if brief.development_arc and not step_phase:
        arc_hits = sum(1 for phase in brief.development_arc if phase in blob)
        if arc_hits:
            score += min(0.15, 0.05 * arc_hits)

    return max(0.0, min(1.0, round(score, 4))), reasons[:4]


def semantic_core_phrases(brief: PlanningSemanticBrief) -> List[str]:
    """Harte Kernphrasen fürs Matching."""
    if brief.primary_topic:
        return [_normalize_phrase(brief.primary_topic)]
    core = [_normalize_phrase(p) for p in (brief.must_phrases or [])[:2] if p]
    return [p for p in core if p]


def resolve_semantic_skill_weights(cur, brief: PlanningSemanticBrief) -> Dict[int, float]:
    """Deterministisches Fähigkeitserwartungsprofil aus Technik-Thema."""
    topic = _normalize_phrase(brief.primary_topic or "")
    if topic in _TECHNIQUE_EXPECTED_SKILLS:
        names = list(_TECHNIQUE_EXPECTED_SKILLS[topic])
    elif brief.topic_type == "technique" or "geri" in topic:
        names = list(_DEFAULT_TECHNIQUE_SKILLS)
    else:
        return {}

    weights: Dict[int, float] = {}
    for name in names[:6]:
        cur.execute(
            """
            SELECT id, name FROM skills
            WHERE (status IS NULL OR status = 'active')
              AND LOWER(name) LIKE %s
            ORDER BY CASE WHEN LOWER(name) = %s THEN 0 WHEN LOWER(name) LIKE %s THEN 1 ELSE 2 END,
                     LENGTH(name) ASC
            LIMIT 1
            """,
            (f"%{name.lower()}%", name.lower(), f"{name.lower()}%"),
        )
        row = cur.fetchone()
        if row:
            sid = int(row["id"])
            weights[sid] = max(weights.get(sid, 0.0), 1.0)
    return weights


def enrich_target_with_semantic_expectations(
    target,
    *,
    skill_weights: Dict[int, float],
):
    from planning_exercise_profiles import PlanningTargetProfile, _merge_weight_maps, _normalize_weight_map

    if not skill_weights:
        return target
    merged = _normalize_weight_map(_merge_weight_maps(dict(target.skill_weights), skill_weights, scale=1.0))
    sources = list(target.sources)
    if "semantic_expectation" not in sources:
        sources.append("semantic_expectation")
    return PlanningTargetProfile(
        focus_area_ids=dict(target.focus_area_ids),
        style_direction_ids=dict(target.style_direction_ids),
        training_type_ids=dict(target.training_type_ids),
        target_group_ids=dict(target.target_group_ids),
        skill_weights=merged,
        skill_gap_weights=dict(target.skill_gap_weights),
        skill_plan_weights=dict(target.skill_plan_weights),
        sources=sources,
    )


def apply_path_retrieval_weights(brief: PlanningSemanticBrief) -> Dict[str, float]:
    """Pfad-Builder: Semantik + Profil dominieren."""
    sem = float(brief.semantic_strength or 0.0)
    if sem >= 0.65:
        return {
            "semantic": 0.50,
            "fulltext": 0.16,
            "profile": 0.26,
            "progression": 0.04,
            "skill": 0.04,
            "plan": 0.0,
            "repeat_unit": -0.40,
            "repeat_group": -0.15,
        }
    if sem >= 0.35:
        return {
            "semantic": 0.38,
            "fulltext": 0.18,
            "profile": 0.28,
            "progression": 0.06,
            "skill": 0.06,
            "plan": 0.04,
            "repeat_unit": -0.35,
            "repeat_group": -0.15,
        }
    return {
        "semantic": 0.22,
        "fulltext": 0.22,
        "profile": 0.28,
        "progression": 0.10,
        "skill": 0.10,
        "plan": 0.08,
        "repeat_unit": -0.30,
        "repeat_group": -0.15,
    }


_STAGE_GOAL_STOPWORDS = _QUERY_STOPWORDS | frozenset(
    {
        "stufe",
        "phase",
        "lernziel",
        "grundlage",
        "vertiefung",
        "anwendung",
        "perfektion",
        "einstieg",
        "sicher",
        "sauber",
        "korrekt",
        "technik",
        "training",
    }
)


_STAGE_NEGATION_PATTERNS = (
    r"\bohne\s+([^,.;]+)",
    r"\bkein(?:e|en|er|em)?\s+([^,.;]+)",
    r"\bnicht\s+([^,.;]+)",
)

# Aus „ohne Tritttechnik“ etc. — erweiterte Treffer im Übungstext
_STAGE_EXCLUDE_ALIASES: Dict[str, Tuple[str, ...]] = {
    "tritttechnik": (
        "tritttechnik",
        "trittpraezision",
        "trittpräzision",
        "tritt praesision",
        "tritt-präzision",
        "kicktechnik",
        "tritt ausführung",
        "tritt ausfuehrung",
    ),
    "kumite": ("kumite", "partnerkampf", "freikampf", "jiyu kumite"),
    "kraftuebung": ("kraftuebung", "kraftübung", "krafttraining", "kraftübungen"),
    "anwendung": ("kumite anwendung", "kampfanwendung"),
}

_STAGE_FOCUS_TOKENS = frozenset(
    {
        "koordination",
        "absprung",
        "beinhebung",
        "landung",
        "sprung",
        "sprungphase",
        "balance",
        "gleichgewicht",
        "timing",
        "vorbereitung",
        "athletik",
        "mobilitaet",
        "mobilität",
        "stabilisation",
        "stabilisierung",
    }
)


@dataclass
class StageGoalConstraints:
    positive_tokens: List[str] = field(default_factory=list)
    exclude_phrases: List[str] = field(default_factory=list)
    has_negation: bool = False
    strict_positive: bool = False


def _expand_stage_exclude_phrase(phrase: str) -> List[str]:
    norm = _normalize_phrase(phrase)
    if not norm:
        return []
    out: List[str] = [norm]
    compact = norm.replace(" ", "")
    if compact and compact not in out:
        out.append(compact)
    for key, aliases in _STAGE_EXCLUDE_ALIASES.items():
        if key in norm or norm in key:
            for alias in aliases:
                a = _normalize_phrase(alias)
                if a and a not in out:
                    out.append(a)
    return out[:12]


def _significant_stage_tokens(learning_goal: str, *, strip_negated: bool = True) -> List[str]:
    """Wörter aus Stufen-Lernziel für Text-Match (ohne Füllwörter, ohne Negationssegmente)."""
    text = _normalize_phrase(learning_goal)
    if strip_negated:
        for pat in _STAGE_NEGATION_PATTERNS:
            text = re.sub(pat, " ", text)
    raw = re.findall(r"[a-zäöüß]{4,}", text, flags=re.IGNORECASE)
    out: List[str] = []
    for w in raw:
        low = w.lower().replace("ä", "ae").replace("ö", "oe").replace("ü", "ue")
        if low in _STAGE_GOAL_STOPWORDS:
            continue
        if low not in out:
            out.append(low)
    return out[:10]


def parse_stage_goal_constraints(
    learning_goal: str,
    anti_patterns: Optional[Sequence[str]] = None,
) -> StageGoalConstraints:
    """Positiv/Negativ aus Stufen-Lernziel + anti_patterns (Roadmap-Stufe)."""
    lg = (learning_goal or "").strip()
    if len(lg) < 3:
        return StageGoalConstraints()

    norm = _normalize_phrase(lg)
    exclude: List[str] = []
    has_negation = False
    for pat in _STAGE_NEGATION_PATTERNS:
        for m in re.finditer(pat, norm):
            has_negation = True
            chunk = (m.group(1) or "").strip()
            if chunk:
                exclude.extend(_expand_stage_exclude_phrase(chunk))

    for raw in anti_patterns or []:
        s = _normalize_phrase(str(raw or ""))
        if s:
            exclude.extend(_expand_stage_exclude_phrase(s))

    positive = _significant_stage_tokens(lg, strip_negated=True)
    focus_hits = [t for t in positive if t in _STAGE_FOCUS_TOKENS]
    strict_positive = bool(focus_hits) or has_negation

    dedup_exclude: List[str] = []
    for item in exclude:
        if item and item not in dedup_exclude:
            dedup_exclude.append(item)

    return StageGoalConstraints(
        positive_tokens=positive,
        exclude_phrases=dedup_exclude[:16],
        has_negation=has_negation,
        strict_positive=strict_positive,
    )


def _phrase_excluded_in_blob(phrase: str, blob: str) -> bool:
    """Treffer nur wenn das Ausschluss-Thema nicht selbst negiert beschrieben ist."""
    if not phrase or not blob:
        return False
    if not _phrase_in_blob(phrase, blob):
        return False
    norm = _normalize_phrase(phrase)
    for pat in _STAGE_NEGATION_PATTERNS:
        for m in re.finditer(pat, blob):
            chunk = _normalize_phrase(m.group(1) or "")
            if not chunk:
                continue
            if norm in chunk or chunk in norm or _phrase_in_blob(norm, chunk):
                return False
    return True


def _blob_matches_stage_excludes(blob: str, exclude_phrases: Sequence[str]) -> bool:
    for phrase in exclude_phrases:
        if _phrase_excluded_in_blob(phrase, blob):
            return True
    return False


def resolve_path_anti_patterns(
    goal_query: str,
    *,
    semantic_brief: Optional[PlanningSemanticBrief] = None,
    extra_context: Optional[str] = None,
) -> List[str]:
    """
    Pfadweite Ausschlüsse — nur aus expliziten Quellen, kein Themen-Raten.

    Quellen (in dieser Reihenfolge):
      1. Negationen in Anfrage/Kontext (ohne/kein/nicht …) via parse_stage_goal_constraints
      2. exclude_phrases im Semantic Brief (inkl. LLM/Technik-Regeln)
      3. stage_specs.anti_patterns (Roadmap-Stufe, vom Trainer oder LLM)

    Keine stillen Ausschlüsse aus dem Hauptthema (z. B. „Mawashi“ → kein Kumite).
    """
    parts = [str(goal_query or "").strip(), str(extra_context or "").strip()]
    combined = " ".join(p for p in parts if p)
    if not combined and not semantic_brief:
        return []

    constraints = parse_stage_goal_constraints(combined) if combined else StageGoalConstraints()
    out: List[str] = []
    for item in constraints.exclude_phrases:
        if item and item not in out:
            out.append(item)

    if semantic_brief:
        for raw in semantic_brief.exclude_phrases or []:
            for expanded in _expand_stage_exclude_phrase(str(raw or "")):
                if expanded and expanded not in out:
                    out.append(expanded)

    return out[:24]


def enrich_brief_with_path_constraints(
    brief: PlanningSemanticBrief,
    goal_query: str,
    *,
    extra_context: Optional[str] = None,
) -> PlanningSemanticBrief:
    """Negationen/Ausschlüsse aus der Gesamtanfrage in den Semantic Brief übernehmen."""
    anti = resolve_path_anti_patterns(
        goal_query,
        semantic_brief=brief,
        extra_context=extra_context,
    )
    if not anti:
        return brief
    exclude = list(brief.exclude_phrases or [])
    for item in anti:
        if item not in exclude:
            exclude.append(item)
    return brief.model_copy(update={"exclude_phrases": exclude[:16]})


_MIN_STAGE_FIT_SEMANTIC = 0.30
_MIN_STAGE_FIT_RELAXED = 0.20
_MIN_TITLE_EQUIV_SEMANTIC = 0.15


def build_stage_match_brief(
    *,
    learning_goal: str,
    anti_patterns: Optional[Sequence[str]] = None,
    success_criteria: Optional[Sequence[str]] = None,
    load_profile: Optional[Sequence[str]] = None,
    phase: Optional[str] = None,
    path_context_note: Optional[str] = None,
    path_anti_patterns: Optional[Sequence[str]] = None,
    path_primary_topic: Optional[str] = None,
    path_technique_excludes: Optional[Sequence[str]] = None,
    stage_start_state: Optional[str] = None,
    stage_target_state: Optional[str] = None,
    path_target_state: Optional[str] = None,
    contextualized_learning_goal: Optional[str] = None,
) -> PlanningSemanticBrief:
    """
    Stufen-zentrierter Semantik-Brief — unabhängig vom Gesamt-Pfad-Thema.

    Primär für Roadmap-Match: Bewertung gegen Titel + Kurzbeschreibung + Übungsziel.
    """
    lg = (contextualized_learning_goal or learning_goal or "").strip()
    if len(lg) < 3:
        return PlanningSemanticBrief(semantic_strength=0.0)

    merged_anti: List[str] = []
    for raw in list(anti_patterns or []) + list(path_anti_patterns or []):
        s = str(raw or "").strip()
        if s and s not in merged_anti:
            merged_anti.append(s)
    primary_path = _normalize_phrase(path_primary_topic or "")
    if primary_path:
        for item in technique_sibling_excludes(primary_path):
            if item not in merged_anti:
                merged_anti.append(item)
    for raw in path_technique_excludes or []:
        for expanded in _expand_stage_exclude_phrase(str(raw or "")):
            if expanded and expanded not in merged_anti:
                merged_anti.append(expanded)
    constraints = parse_stage_goal_constraints(lg, merged_anti)
    must: List[str] = []
    norm_lg = _normalize_phrase(lg)
    if primary_path and primary_path not in must:
        must.insert(0, primary_path[:120])
    for token in constraints.positive_tokens:
        if token not in must:
            must.append(token)
    if norm_lg and norm_lg not in must:
        must.append(norm_lg[:120])
    for raw in success_criteria or []:
        s = _normalize_phrase(str(raw or ""))
        if s and s not in must:
            must.append(s[:100])
    for raw in load_profile or []:
        s = _normalize_phrase(str(raw or ""))
        if s and s not in must:
            must.append(s[:60])

    retrieval_parts = [norm_lg]
    for raw in (stage_start_state, stage_target_state, path_target_state):
        s = _normalize_phrase(str(raw or ""))[:200]
        if s and s not in retrieval_parts:
            retrieval_parts.append(s)
    if path_context_note:
        note = _normalize_phrase(path_context_note)[:200]
        if note:
            retrieval_parts.append(note)

    arc: List[str] = []
    ph = (phase or "").strip().lower()
    if ph:
        arc.append(ph)

    return PlanningSemanticBrief(
        primary_topic="",
        topic_type="focus",
        must_phrases=must[:12],
        exclude_phrases=list(constraints.exclude_phrases)[:12],
        development_arc=arc[:4],
        retrieval_query=" ".join(p for p in retrieval_parts if p)[:500],
        semantic_strength=0.78,
        rationale="stage_match_brief",
    )


def score_exercise_stage_fit(
    *,
    title: str,
    summary: str,
    goal: str,
    stage_brief: PlanningSemanticBrief,
    variant_names: Optional[Sequence[str]] = None,
    step_phase: Optional[str] = None,
) -> Tuple[float, List[str]]:
    """Semantik-Score Übung ↔ Stufen-Lernziel (Titel + Summary + Goal)."""
    score, reasons = score_exercise_semantic_relevance(
        title=title,
        summary=summary,
        goal=goal,
        variant_names=variant_names or [],
        brief=stage_brief,
        step_phase=step_phase,
    )
    blob = _blob_from_fields(title, summary, goal, variant_names or [])
    focus_tokens = [
        t
        for t in (stage_brief.must_phrases or [])
        if t and " " not in t and len(t) >= 4
    ][:6]
    if focus_tokens:
        hits = sum(1 for t in focus_tokens if _phrase_in_blob(t, blob))
        ratio = hits / len(focus_tokens)
        bonus = 0.28 * ratio
        if bonus > 0:
            score = min(1.0, score + bonus)
            if hits >= max(1, len(focus_tokens) // 2):
                reasons = ["Stufen-Schwerpunkte im Übungstext", *reasons]
    return max(0.0, min(1.0, round(score, 4))), reasons[:4]


def exercise_passes_stage_fit(
    *,
    learning_goal: str,
    title: str,
    summary: str = "",
    goal: str = "",
    stage_brief: Optional[PlanningSemanticBrief] = None,
    stage_semantic_score: Optional[float] = None,
    anti_patterns: Optional[Sequence[str]] = None,
    step_phase: Optional[str] = None,
    path_primary_topic: Optional[str] = None,
    path_technique_excludes: Optional[Sequence[str]] = None,
    min_stage_semantic: float = _MIN_STAGE_FIT_SEMANTIC,
    relaxed: bool = False,
) -> bool:
    """Allgemeines Stufen-Fit-Gate: voller Übungstext vs. Stufen-Brief."""
    lg = (learning_goal or "").strip()
    if len(lg) < 3 and not (path_primary_topic or "").strip():
        return True

    blob = _blob_from_fields(title, summary, goal, [])
    constraints = parse_stage_goal_constraints(lg, anti_patterns)
    if constraints.exclude_phrases and _blob_matches_stage_excludes(blob, constraints.exclude_phrases):
        return False

    title_equiv = exercise_title_equivalent_to_stage_goal(title, learning_goal or lg)

    primary_path = (path_primary_topic or "").strip()
    if not primary_path and lg:
        hit = _find_technique_in_text(_normalize_phrase(lg))
        if hit:
            primary_path = hit[0]
    tech_excludes = list(path_technique_excludes or [])
    if primary_path:
        for item in technique_sibling_excludes(primary_path):
            if item not in tech_excludes:
                tech_excludes.append(item)
    if primary_path and not title_equiv and not exercise_passes_technique_path_scope(
        primary_topic=primary_path,
        title=title,
        summary=summary,
        goal=goal,
        learning_goal=lg,
        sibling_excludes=tech_excludes,
        relaxed=relaxed,
    ):
        return False

    brief = stage_brief or build_stage_match_brief(
        learning_goal=lg,
        anti_patterns=anti_patterns,
    )
    stage_sem = stage_semantic_score
    if stage_sem is None:
        stage_sem, _ = score_exercise_stage_fit(
            title=title,
            summary=summary,
            goal=goal,
            stage_brief=brief,
            step_phase=step_phase,
        )

    if relaxed:
        threshold = _MIN_STAGE_FIT_RELAXED
    elif title_equiv:
        threshold = _MIN_TITLE_EQUIV_SEMANTIC
    else:
        threshold = min_stage_semantic
    return float(stage_sem or 0.0) >= threshold


def apply_stage_match_retrieval_weights(brief: PlanningSemanticBrief) -> Dict[str, float]:
    """Roadmap-Stufe: Stufen-Semantik (Ziel/Summary/Goal) dominiert."""
    return {
        "semantic": 0.58,
        "fulltext": 0.14,
        "profile": 0.18,
        "progression": 0.04,
        "skill": 0.04,
        "plan": 0.02,
        "repeat_unit": -0.40,
        "repeat_group": -0.15,
    }


def semantic_brief_for_stage(
    brief: PlanningSemanticBrief,
    *,
    learning_goal: str,
    phase: Optional[str] = None,
    anti_patterns: Optional[Sequence[str]] = None,
) -> PlanningSemanticBrief:
    """Legacy: globalen Brief anreichern — bevorzugt build_stage_match_brief für Roadmap-Match."""
    lg = _normalize_phrase(learning_goal)
    if not lg:
        return brief
    constraints = parse_stage_goal_constraints(learning_goal, anti_patterns)
    must = list(brief.must_phrases or [])
    for token in constraints.positive_tokens[:4]:
        if token not in must:
            must.append(token)
    if lg not in must:
        must.insert(0, lg[:120])
    exclude = list(brief.exclude_phrases or [])
    for item in constraints.exclude_phrases:
        if item not in exclude:
            exclude.append(item)
    arc = list(brief.development_arc or [])
    ph = (phase or "").strip().lower()
    if ph and ph not in arc:
        arc = [ph, *arc]
    strength = max(float(brief.semantic_strength or 0.0), 0.58)
    return brief.model_copy(
        update={
            "must_phrases": must[:12],
            "exclude_phrases": exclude[:12],
            "development_arc": arc[:8],
            "semantic_strength": min(1.0, strength),
        }
    )


def exercise_passes_stage_learning_goal_gate(
    *,
    learning_goal: str,
    title: str,
    summary: str = "",
    goal: str = "",
    semantic_score: float = 0.0,
    min_semantic: float = 0.20,
    relaxed: bool = False,
    anti_patterns: Optional[Sequence[str]] = None,
    stage_brief: Optional[PlanningSemanticBrief] = None,
    stage_semantic_score: Optional[float] = None,
    step_phase: Optional[str] = None,
) -> bool:
    """Roadmap-Stufe: delegiert an exercise_passes_stage_fit (Titel + Summary + Goal)."""
    del semantic_score, min_semantic
    return exercise_passes_stage_fit(
        learning_goal=learning_goal,
        title=title,
        summary=summary,
        goal=goal,
        stage_brief=stage_brief,
        stage_semantic_score=stage_semantic_score,
        anti_patterns=anti_patterns,
        step_phase=step_phase,
        relaxed=relaxed,
    )


def exercise_passes_path_semantic_gate(
    *,
    semantic_score: float,
    title: str,
    brief: PlanningSemanticBrief,
    summary: str = "",
    goal: str = "",
    strict: bool = True,
) -> bool:
    if brief.semantic_strength < 0.55:
        return True

    blob = _blob_from_fields(title, summary, goal, [])
    min_score = 0.18 if strict else 0.06
    if semantic_score >= min_score:
        return True

    topic = brief.primary_topic or ""
    if topic and _phrase_in_blob(topic, blob):
        return True

    if not strict:
        # Mae Geri oft im Fließtext, nicht im Titel
        if semantic_score >= 0.04 and topic and _phrase_in_blob(topic, blob):
            return True
        parts = topic.split()
        if len(parts) >= 2 and all(_phrase_in_blob(p, blob) for p in parts):
            return True

    return False


def pick_best_path_hit(
    hits: List[Dict[str, Any]],
    used_exercise_ids: Set[int],
    *,
    semantic_brief: Optional[PlanningSemanticBrief] = None,
    stage_learning_goal: Optional[str] = None,
    stage_anti_patterns: Optional[Sequence[str]] = None,
    roadmap_stage_match: bool = False,
    stage_match_brief: Optional[PlanningSemanticBrief] = None,
    path_primary_topic: Optional[str] = None,
    path_technique_excludes: Optional[Sequence[str]] = None,
) -> Optional[Dict[str, Any]]:
    """Gestufte Auswahl: strikt → relaxed → optional Notfall-Fallback."""
    if not hits:
        return None

    stage_goal = (stage_learning_goal or "").strip()

    stage_brief: Optional[PlanningSemanticBrief] = stage_match_brief
    if roadmap_stage_match and stage_goal and stage_brief is None:
        stage_brief = build_stage_match_brief(
            learning_goal=stage_goal,
            anti_patterns=stage_anti_patterns,
        )

    def _scan(*, strict: bool) -> Optional[Dict[str, Any]]:
        best: Optional[Dict[str, Any]] = None
        best_key: Tuple[float, float] = (-1.0, -1.0)
        for hit in hits:
            eid = int(hit["id"])
            if eid in used_exercise_ids:
                continue
            title = str(hit.get("title") or "")
            summary = str(hit.get("summary") or "")
            goal_text = str(hit.get("goal") or hit.get("exercise_goal") or "")
            sem = float(hit.get("semantic_score") or 0.0)
            stage_sem = float(
                hit.get("stage_rank_semantic")
                or hit.get("stage_semantic_score")
                or sem
            )

            if roadmap_stage_match and stage_goal:
                if not exercise_passes_stage_fit(
                    learning_goal=stage_goal,
                    title=title,
                    summary=summary,
                    goal=goal_text,
                    stage_brief=stage_brief,
                    stage_semantic_score=stage_sem,
                    anti_patterns=stage_anti_patterns,
                    path_primary_topic=path_primary_topic,
                    path_technique_excludes=path_technique_excludes,
                    relaxed=not strict,
                ):
                    continue
            else:
                if semantic_brief and not exercise_passes_path_semantic_gate(
                    semantic_score=sem,
                    title=title,
                    summary=summary,
                    goal=goal_text,
                    brief=semantic_brief,
                    strict=strict,
                ):
                    continue

            score = float(hit.get("score") or 0.0)
            rank_sem = stage_sem if roadmap_stage_match and stage_goal else sem
            key = (rank_sem, score)
            if key > best_key:
                best_key = key
                best = hit
        return best

    chosen = _scan(strict=True)
    if chosen:
        return chosen

    if roadmap_stage_match:
        chosen = _scan(strict=False)
        return chosen

    chosen = _scan(strict=False)
    if chosen:
        return chosen

    # Notfall (nur retrieval-first / Brücken): bester verbleibender Treffer
    fallback: Optional[Dict[str, Any]] = None
    fallback_key: Tuple[float, float] = (-1.0, -1.0)
    for hit in hits:
        eid = int(hit["id"])
        if eid in used_exercise_ids:
            continue
        sem = float(hit.get("semantic_score") or 0.0)
        score = float(hit.get("score") or 0.0)
        if sem <= 0 and semantic_brief and semantic_brief.primary_topic:
            topic = semantic_brief.primary_topic
            blob = (str(hit.get("title") or "") + " " + str(hit.get("summary") or "")).lower()
            if not _phrase_in_blob(topic, blob):
                continue
        key = (sem, score)
        if key > fallback_key:
            fallback_key = key
            fallback = hit
    return fallback


__all__ = [
    "PlanningSemanticBrief",
    "apply_dynamic_retrieval_weights",
    "apply_path_retrieval_weights",
    "brief_to_summary_dict",
    "build_semantic_brief",
    "enrich_target_with_semantic_expectations",
    "exercise_passes_path_semantic_gate",
    "StageGoalConstraints",
    "apply_stage_match_retrieval_weights",
    "build_stage_match_brief",
    "enrich_brief_with_path_constraints",
    "exercise_passes_stage_fit",
    "exercise_title_equivalent_to_stage_goal",
    "resolve_path_primary_topic",
    "resolve_path_anti_patterns",
    "exercise_passes_stage_learning_goal_gate",
    "merge_semantic_brief_llm",
    "parse_stage_goal_constraints",
    "pick_best_path_hit",
    "exercise_passes_technique_path_scope",
    "score_exercise_stage_fit",
    "semantic_brief_for_stage",
    "technique_sibling_excludes",
    "resolve_semantic_skill_weights",
    "score_exercise_semantic_relevance",
    "semantic_core_phrases",
    "step_phase_for_index",
    "step_retrieval_query",
    "try_enrich_semantic_brief_with_llm",
]