"""
Planungs-KI Phase E2/E3: KI-Neuanlage für Pfad-Lücken + strukturierte Angebote für die UI.
"""
from __future__ import annotations

import logging
import uuid
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple

from ai_prompt_context import ExerciseFormAiPromptContext
from ai_prompt_job import run_exercise_form_ai_suggestion
from exercise_ai import strip_html_to_plain

from planning_exercise_path_qa import find_step_pair_index
from planning_exercise_form_context import (
    build_progression_entry_state,
    build_progression_gap_snapshot,
    enrich_gap_snapshot_with_entry_state,
    prior_path_steps_before_major,
)
from planning_exercise_semantics import PlanningSemanticBrief, brief_to_summary_dict

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


def _resolve_neighbor_steps_by_major_index(
    steps: Sequence[Mapping[str, Any]],
    major_idx: int,
) -> Tuple[Optional[Mapping[str, Any]], Optional[Mapping[str, Any]]]:
    """Nachbarn im Pfad anhand roadmap_major_step_index (nicht Array-Position)."""
    step_before: Optional[Mapping[str, Any]] = None
    step_after: Optional[Mapping[str, Any]] = None
    for step in steps:
        raw = step.get("roadmap_major_step_index")
        if raw is None:
            continue
        try:
            mi = int(raw)
        except (TypeError, ValueError):
            continue
        if mi < major_idx:
            step_before = step
        elif mi > major_idx and step_after is None:
            step_after = step
    return step_before, step_after


def _build_stage_ai_context(
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    spec: Mapping[str, Any],
    step_before: Optional[Mapping[str, Any]] = None,
    step_after: Optional[Mapping[str, Any]] = None,
    prior_steps: Optional[Sequence[Mapping[str, Any]]] = None,
    start_situation: Optional[str] = None,
) -> ExerciseFormAiPromptContext:
    """KI-Kontext für unbesetzte Roadmap-Stufe (keine Brücke zwischen falschen Array-Indizes)."""
    gap = dict(spec.get("gap") or {})
    phase = spec.get("phase") or gap.get("expected_phase") or "vertiefung"
    topic = (brief.primary_topic or "Technik").strip()
    learning_goal = (
        gap.get("learning_goal")
        or spec.get("title_hint")
        or spec.get("sketch")
        or ""
    ).strip()
    title = (spec.get("title_hint") or f"{topic} — {phase}").strip()[:280]
    major_idx = spec.get("roadmap_major_step_index")
    entry: Dict[str, Any] = {}
    if prior_steps is not None and major_idx is not None:
        entry = build_progression_entry_state(
            major_step_index=major_idx,
            prior_steps=prior_steps,
            start_situation=start_situation,
        )

    goal_parts = [
        f"Planungsziel: {goal_query}",
        f"Roadmap-Stufe ({phase}): {learning_goal}",
        "Erstelle eine Übung, die dieses Stufen-Lernziel erfüllt — keine generische Brücken-Übung.",
    ]
    if entry.get("entry_state"):
        goal_parts.append(
            f"Eingangszustand (erreichte Voraussetzungen): {entry['entry_state']}"
        )
    if entry.get("entry_state_detail") and entry.get("entry_state_detail") != entry.get("entry_state"):
        goal_parts.append(f"Bisheriger Pfad:\n{entry['entry_state_detail']}")
    if step_before:
        goal_parts.append(
            f"Vorherige Stufe im Pfad: „{(step_before.get('title') or '').strip()}“"
        )
    if step_after:
        goal_parts.append(
            f"Nächste Stufe im Pfad: „{(step_after.get('title') or '').strip()}“"
        )
    sketch = (spec.get("sketch") or "").strip()
    if sketch and sketch != learning_goal:
        goal_parts.extend(["", f"Kontext: {sketch}"])
    goal = "\n".join(goal_parts)

    focus_hint = topic if brief.topic_type == "technique" else None
    if brief.must_phrases:
        focus_hint = ", ".join(brief.must_phrases[:2])

    return ExerciseFormAiPromptContext(
        title=title[:280],
        goal=goal[:8000],
        execution=None,
        focus_hint=focus_hint,
    )


def try_suggest_ai_stage_step(
    cur,
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    spec: Mapping[str, Any],
    steps: Sequence[Mapping[str, Any]],
) -> Optional[Dict[str, Any]]:
    """KI-Vorschlag für leere Roadmap-Stufe."""
    major_idx = spec.get("roadmap_major_step_index")
    if major_idx is None:
        return None
    try:
        mi = int(major_idx)
    except (TypeError, ValueError):
        return None
    step_before, step_after = _resolve_neighbor_steps_by_major_index(steps, mi)
    prior_steps = prior_path_steps_before_major(steps, mi)
    gap = dict(spec.get("gap") or {})
    if not gap.get("expected_phase"):
        gap["expected_phase"] = spec.get("phase") or "vertiefung"
    gap["roadmap_major_step_index"] = mi
    if not gap.get("learning_goal"):
        gap["learning_goal"] = spec.get("title_hint") or spec.get("sketch")

    ctx = _build_stage_ai_context(
        goal_query=goal_query,
        brief=brief,
        spec=spec,
        step_before=step_before,
        step_after=step_after,
        prior_steps=prior_steps,
    )
    try:
        ai_payload = run_exercise_form_ai_suggestion(cur, ctx=ctx)
    except Exception:
        _logger.exception("roadmap_unfilled AI suggest failed")
        return None
    if not ai_payload:
        return None

    summary_text = ""
    summary_obj = ai_payload.get("summary")
    if isinstance(summary_obj, dict):
        summary_text = str(summary_obj.get("text") or "").strip()
    elif isinstance(summary_obj, str):
        summary_text = summary_obj.strip()

    proposal_key = f"ai-{uuid.uuid4().hex[:10]}"
    title = (ctx.title or spec.get("title_hint") or "KI-Vorschlag").strip()
    return {
        "exercise_id": None,
        "proposal_key": proposal_key,
        "variant_id": None,
        "title": title,
        "summary": summary_text or None,
        "score": None,
        "semantic_score": None,
        "reasons": ["KI-Neuanlage für Roadmap-Stufe ohne Bibliothekstreffer"],
        "variants": [],
        "is_bridge": False,
        "is_ai_proposal": True,
        "ai_suggestion": dict(ai_payload),
        "roadmap_major_step_index": mi,
        "roadmap_phase": gap.get("expected_phase"),
        "roadmap_learning_goal": gap.get("learning_goal"),
    }


def _build_gap_ai_context(
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    step_a: Mapping[str, Any],
    step_b: Mapping[str, Any],
    gap: Mapping[str, Any],
    title_hint: Optional[str] = None,
    sketch_hint: Optional[str] = None,
) -> ExerciseFormAiPromptContext:
    topic = (brief.primary_topic or "Technik").strip()
    phase = gap.get("expected_phase") or "vertiefung"
    from_title = (step_a.get("title") or f"Übung #{step_a.get('exercise_id')}").strip()
    to_title = (step_b.get("title") or f"Übung #{step_b.get('exercise_id')}").strip()

    title = (title_hint or f"Brücke {topic} ({phase})").strip()[:280]
    sketch = (sketch_hint or "").strip()
    goal_parts = [
        f"Planungsziel: {goal_query}",
        "",
        f"Didaktische Brücken-Übung zwischen „{from_title}“ und „{to_title}“.",
        f"Phase: {phase}. Thema: {topic}.",
        "Die Übung schließt die Lücke im Progressionspfad und bereitet sinnvoll auf den nächsten Schritt vor.",
    ]
    if sketch:
        goal_parts.extend(["", f"Hinweis: {sketch}"])
    goal = "\n".join(goal_parts)

    focus_hint = topic if brief.topic_type == "technique" else None
    if brief.must_phrases:
        focus_hint = ", ".join(brief.must_phrases[:2])

    return ExerciseFormAiPromptContext(
        title=title[:280],
        goal=goal[:8000],
        execution=None,
        focus_hint=focus_hint,
    )


def ai_proposal_to_path_step(
    *,
    ai_payload: Mapping[str, Any],
    ctx_title: str,
    gap: Mapping[str, Any],
    step_a: Mapping[str, Any],
    step_b: Mapping[str, Any],
) -> Dict[str, Any]:
    summary_text = ""
    summary_obj = ai_payload.get("summary")
    if isinstance(summary_obj, dict):
        summary_text = str(summary_obj.get("text") or "").strip()
    elif isinstance(summary_obj, str):
        summary_text = summary_obj.strip()

    proposal_key = f"ai-{uuid.uuid4().hex[:10]}"
    title = (ctx_title or "").strip() or "KI-Vorschlag (Brücke)"
    reasons = ["KI-Neuanlage-Vorschlag — Lücke ohne passende Bibliotheks-Übung"]

    return {
        "exercise_id": None,
        "proposal_key": proposal_key,
        "variant_id": None,
        "title": title,
        "summary": summary_text or None,
        "score": None,
        "semantic_score": None,
        "reasons": reasons,
        "variants": [],
        "is_bridge": True,
        "is_ai_proposal": True,
        "ai_suggestion": dict(ai_payload),
        "bridge_for_gap": {
            "from_exercise_id": step_a.get("exercise_id"),
            "to_exercise_id": step_b.get("exercise_id"),
            "gap_score": gap.get("gap_score"),
            "expected_phase": gap.get("expected_phase"),
        },
    }


def try_suggest_ai_bridge_step(
    cur,
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    step_a: Mapping[str, Any],
    step_b: Mapping[str, Any],
    gap: Mapping[str, Any],
    title_hint: Optional[str] = None,
    sketch_hint: Optional[str] = None,
) -> Optional[Dict[str, Any]]:
    """Ruft exercise AI suggest auf — kein Speichern in DB."""
    ctx = _build_gap_ai_context(
        goal_query=goal_query,
        brief=brief,
        step_a=step_a,
        step_b=step_b,
        gap=gap,
        title_hint=title_hint,
        sketch_hint=sketch_hint,
    )
    g_plain = strip_html_to_plain(ctx.goal)
    if not g_plain.strip() and not (ctx.title or "").strip():
        return None
    try:
        payload = run_exercise_form_ai_suggestion(
            cur,
            ctx,
            want_summary=True,
            want_skills=True,
            want_instructions=False,
        )
    except Exception as exc:
        _logger.warning("KI-Lückenfüller fehlgeschlagen: %s", exc)
        return None

    if not payload:
        return None
    return ai_proposal_to_path_step(
        ai_payload=payload,
        ctx_title=ctx.title or "",
        gap=gap,
        step_a=step_a,
        step_b=step_b,
    )


def _default_sketch(
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    step_a: Optional[Mapping[str, Any]],
    step_b: Optional[Mapping[str, Any]],
    phase: str,
    rationale: str = "",
) -> str:
    topic = (brief.primary_topic or "Technik").strip()
    from_t = (step_a or {}).get("title") or "vorherigem Schritt"
    to_t = (step_b or {}).get("title") or "nächstem Schritt"
    parts = [
        f"Planungsziel: {goal_query}",
        f"Zwischenschritt für {topic} ({phase}) zwischen „{from_t}“ und „{to_t}“.",
    ]
    if rationale:
        parts.append(rationale)
    return " ".join(parts)[:1200]


def _spec_dedupe_key(spec: Mapping[str, Any]) -> Tuple[Any, ...]:
    return (
        spec.get("source"),
        int(spec.get("insert_after_index") or 0),
        str(spec.get("title_hint") or "")[:48],
    )


def _step_neighbors_at_index(
    steps: Sequence[Mapping[str, Any]],
    idx: int,
) -> Tuple[Optional[Mapping[str, Any]], Optional[Mapping[str, Any]]]:
    """Vorheriger/nächster Pfadschritt ohne IndexError (Rand-Slots, leere Stufen)."""
    if idx < 0 or idx >= len(steps):
        return None, None
    step_a = steps[idx - 1] if idx > 0 else None
    step_b = steps[idx + 1] if idx + 1 < len(steps) else None
    return step_a, step_b


def collect_gap_fill_specs(
    *,
    steps: Sequence[Mapping[str, Any]],
    unfilled_gaps: Sequence[Mapping[str, Any]],
    off_topic_steps: Sequence[Mapping[str, Any]],
    llm_specs: Sequence[Mapping[str, Any]],
    brief: PlanningSemanticBrief,
    goal_query: str,
) -> List[Dict[str, Any]]:
    """Sammelt alle Lücken, für die ein KI-Anlege-Angebot sinnvoll ist."""
    topic = (brief.primary_topic or "Technik").strip()
    specs: List[Dict[str, Any]] = []
    seen: set = set()

    def add(spec: Dict[str, Any]) -> None:
        key = _spec_dedupe_key(spec)
        if key in seen:
            return
        seen.add(key)
        specs.append(spec)

    for gap in unfilled_gaps:
        idx = find_step_pair_index(
            steps,
            int(gap["from_exercise_id"]),
            int(gap["to_exercise_id"]),
        )
        if idx is None or idx + 1 >= len(steps):
            continue
        step_a = steps[idx]
        step_b = steps[idx + 1]
        phase = gap.get("expected_phase") or "vertiefung"
        add(
            {
                "source": "unfilled_gap",
                "insert_after_index": idx,
                "gap": dict(gap),
                "phase": phase,
                "title_hint": f"{topic} — {phase}",
                "sketch": _default_sketch(
                    goal_query=goal_query,
                    brief=brief,
                    step_a=step_a,
                    step_b=step_b,
                    phase=str(phase),
                    rationale="Bibliothek enthält keine passende Brücke.",
                ),
                "rationale": "Lücke zwischen benachbaren Schritten — keine passende Bibliotheks-Übung.",
            }
        )

    for ot in off_topic_steps:
        major_idx = ot.get("roadmap_major_step_index")
        idx: Optional[int] = None
        if major_idx is not None:
            try:
                mi = int(major_idx)
            except (TypeError, ValueError):
                mi = None
            if mi is not None:
                idx = next(
                    (
                        i
                        for i, s in enumerate(steps)
                        if s.get("roadmap_major_step_index") is not None
                        and int(s["roadmap_major_step_index"]) == mi
                    ),
                    None,
                )
        if idx is None:
            idx = int(ot.get("step_index") or 0)
        if idx < 0 or idx >= len(steps):
            continue
        step_a, step_b = _step_neighbors_at_index(steps, idx)
        phase = ot.get("expected_phase") or "vertiefung"
        insert_after = max(idx - 1, -1)
        add(
            {
                "source": "off_topic",
                "insert_after_index": insert_after,
                "replace_step_index": idx,
                "roadmap_major_step_index": major_idx,
                "gap": {
                    "expected_phase": phase,
                    "off_topic_title": ot.get("title"),
                    "off_topic_exercise_id": ot.get("exercise_id"),
                },
                "phase": phase,
                "title_hint": f"{topic} — {phase} (Ersatz für themenfremden Schritt)",
                "sketch": _default_sketch(
                    goal_query=goal_query,
                    brief=brief,
                    step_a=step_a,
                    step_b=step_b,
                    phase=str(phase),
                    rationale=f"Ersetzt themenfremden Schritt „{ot.get('title')}“.",
                ),
                "rationale": f"Schritt „{ot.get('title')}“ passt nicht zum Pfad-Thema.",
            }
        )

    for spec in llm_specs:
        add(dict(spec))

    return specs[:5]


def build_gap_fill_goal_text(
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    spec: Mapping[str, Any],
    step_a: Optional[Mapping[str, Any]] = None,
    step_b: Optional[Mapping[str, Any]] = None,
    roadmap_snapshot: Optional[Mapping[str, Any]] = None,
) -> str:
    """Ausführlicher Zieltext für KI-Neuanlage aus Pfad-, Roadmap- und Stufen-Kontext."""
    topic = (brief.primary_topic or "Technik").strip()
    phase = spec.get("phase") or "vertiefung"
    from_title = (step_a or {}).get("title") or spec.get("from_title") or "vorherigem Schritt"
    to_title = (step_b or {}).get("title") or spec.get("to_title") or "nächstem Schritt"
    arc = ", ".join(brief.development_arc or []) or "einstieg → grundlage → vertiefung → anwendung → perfektion"
    snap = dict(roadmap_snapshot or {})
    if not snap:
        snap = build_progression_gap_snapshot(semantic_brief=brief_to_summary_dict(brief))

    parts = [
        f"Planungsziel (gesamter Pfad): {goal_query}",
        f"Hauptthema: {snap.get('primary_topic') or topic}",
    ]
    if snap.get("entry_state"):
        parts.append(
            f"Eingangszustand (erreichte Voraussetzungen aus Vorstufen): {snap['entry_state']}"
        )
    if snap.get("entry_state_detail") and snap.get("entry_state_detail") != snap.get("entry_state"):
        parts.append(f"Bisheriger Pfad:\n{snap['entry_state_detail']}")
    if snap.get("start_situation") and not snap.get("entry_state"):
        parts.append(f"Voraussetzung / Ausgangslage (Progression): {snap['start_situation']}")
    elif snap.get("start_situation") and snap.get("prior_steps"):
        parts.append(f"Ausgangsbasis des gesamten Pfads: {snap['start_situation']}")
    if snap.get("target_state"):
        parts.append(f"Gesamtziel der Progression: {snap['target_state']}")
    if snap.get("roadmap_notes"):
        parts.append(f"Ergänzender Kontext: {snap['roadmap_notes']}")
    stage_goal = snap.get("stage_learning_goal") or spec.get("title_hint")
    if stage_goal:
        parts.append(f"Lernziel dieser Roadmap-Stufe: {stage_goal}")
    parts.append(f"Entwicklungsphase dieser Übung: {snap.get('stage_phase') or phase}")
    parts.append(f"Erwarteter Entwicklungsbogen: {arc}")
    if spec.get("source") == "roadmap_unfilled":
        parts.append(
            "Einordnung: Übung für diese Roadmap-Stufe — das Stufen-Lernziel steht im Vordergrund."
        )
        if step_a:
            parts.append(f"Vorherige Stufe: „{from_title}“")
        if step_b:
            parts.append(f"Nächste Stufe: „{to_title}“")
    else:
        parts.append(
            f"Einordnung: didaktische Zwischenstufe zwischen „{from_title}“ und „{to_title}“."
        )
    if snap.get("stage_load_profile"):
        parts.append(f"Belastungsschwerpunkte: {', '.join(snap['stage_load_profile'])}")
    if snap.get("stage_success_criteria"):
        parts.append(
            "Erfolgskriterien dieser Stufe: "
            + "; ".join(str(x) for x in snap["stage_success_criteria"][:4])
        )
    if snap.get("stage_anti_patterns"):
        parts.append(
            "Vermeiden: " + "; ".join(str(x) for x in snap["stage_anti_patterns"][:3])
        )
    if snap.get("skill_hints"):
        parts.append(
            "Fähigkeiten-/Fokus-Hinweise: "
            + "; ".join(str(x) for x in snap["skill_hints"][:4])
        )
    expected = snap.get("expected_skills") or []
    if expected:
        names = [
            str(s.get("skill_name") or "").strip()
            for s in expected[:5]
            if str(s.get("skill_name") or "").strip()
        ]
        if names:
            parts.append(
                "Erwartete Fähigkeiten (Scoring): " + ", ".join(names)
            )
    if spec.get("rationale"):
        parts.append(f"Qualitätsprüfung: {spec['rationale']}")
    if spec.get("sketch"):
        parts.append(f"Skizze: {spec['sketch']}")
    parts.append(
        "Die Übung muss die Stufe didaktisch erfüllen: klare Voraussetzungen, messbares Stufenziel, "
        "Bezug zum Gesamtpfad — keine generische Kraftübung ohne Technikbezug. "
        "Konkrete Durchführung, Ziel und Trainerhinweise ausformulieren."
    )
    return "\n\n".join(parts)[:8000]


def build_gap_fill_offer(
    *,
    spec: Mapping[str, Any],
    steps: Sequence[Mapping[str, Any]],
    goal_query: str = "",
    brief: Optional[PlanningSemanticBrief] = None,
    proposal: Optional[Mapping[str, Any]] = None,
    roadmap_snapshot: Optional[Mapping[str, Any]] = None,
) -> Dict[str, Any]:
    source = spec.get("source")
    idx = int(spec.get("insert_after_index") or 0)
    major_idx = spec.get("roadmap_major_step_index")
    if source == "roadmap_unfilled" and major_idx is not None:
        try:
            mi = int(major_idx)
        except (TypeError, ValueError):
            mi = idx
        step_a, step_b = _resolve_neighbor_steps_by_major_index(steps, mi)
        idx = mi
    else:
        step_a = steps[idx] if idx < len(steps) else None
        step_b = steps[idx + 1] if idx + 1 < len(steps) else None
    offer_id = f"{spec.get('source')}-{idx}-{uuid.uuid4().hex[:8]}"
    enriched_snapshot = dict(roadmap_snapshot) if roadmap_snapshot else {}
    major_raw = spec.get("roadmap_major_step_index")
    if major_raw is not None:
        enriched_snapshot = enrich_gap_snapshot_with_entry_state(
            enriched_snapshot,
            steps=steps,
            major_step_index=major_raw,
        )
    goal_for_ai = ""
    if brief and goal_query:
        goal_for_ai = build_gap_fill_goal_text(
            goal_query=goal_query,
            brief=brief,
            spec=spec,
            step_a=step_a,
            step_b=step_b,
            roadmap_snapshot=enriched_snapshot or None,
        )
    ctx_preview = enriched_snapshot or None
    offer: Dict[str, Any] = {
        "offer_id": offer_id,
        "source": spec.get("source"),
        "insert_after_index": idx,
        "replace_step_index": spec.get("replace_step_index"),
        "title_hint": spec.get("title_hint"),
        "sketch": spec.get("sketch"),
        "goal_for_ai": goal_for_ai or spec.get("sketch"),
        "context_preview": ctx_preview,
        "phase": spec.get("phase"),
        "rationale": spec.get("rationale"),
        "has_ai_payload": False,
        "from_title": (step_a or {}).get("title"),
        "to_title": (step_b or {}).get("title"),
        "primary_topic": (brief.primary_topic if brief else None),
        "roadmap_major_step_index": spec.get("roadmap_major_step_index"),
    }
    if proposal:
        offer["has_ai_payload"] = True
        offer["proposal_key"] = proposal.get("proposal_key")
        offer["ai_suggestion"] = proposal.get("ai_suggestion")
        offer["proposal_title"] = proposal.get("title")
        offer["proposal_summary"] = proposal.get("summary")
    return offer


def apply_gap_fill_after_qa(
    cur,
    steps: List[Dict[str, Any]],
    specs: Sequence[Mapping[str, Any]],
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    include_ai_calls: bool = True,
    max_ai_proposals: int = 3,
    auto_insert_proposals: bool = False,
    roadmap_snapshot: Optional[Mapping[str, Any]] = None,
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], List[Dict[str, Any]]]:
    """
    Erzeugt gap_fill_offers für die UI; optional KI-Vorschläge einfügen.
    Returns: (steps, ai_proposals, gap_fill_offers)
    """
    if not specs:
        return steps, [], []

    out = list(steps)
    proposals: List[Dict[str, Any]] = []
    offers: List[Dict[str, Any]] = []

    for spec in specs:
        source = spec.get("source")

        if source == "roadmap_unfilled":
            proposal: Optional[Dict[str, Any]] = None
            if include_ai_calls and len(proposals) < max_ai_proposals:
                proposal = try_suggest_ai_stage_step(
                    cur,
                    goal_query=goal_query,
                    brief=brief,
                    spec=spec,
                    steps=out,
                )
            offer = build_gap_fill_offer(
                spec=spec,
                steps=out,
                goal_query=goal_query,
                brief=brief,
                proposal=proposal,
                roadmap_snapshot=roadmap_snapshot,
            )
            offers.append(offer)
            if proposal and auto_insert_proposals:
                proposals.append(
                    {
                        "roadmap_major_step_index": spec.get("roadmap_major_step_index"),
                        "proposal_key": proposal.get("proposal_key"),
                        "proposal_title": proposal.get("title"),
                        "offer_id": offer.get("offer_id"),
                    }
                )
            continue

        idx = int(spec.get("insert_after_index") or 0)
        if idx < 0 or idx >= len(out) - 1:
            continue
        step_a = out[idx]
        step_b = out[idx + 1]
        if step_a.get("is_ai_proposal") or step_b.get("is_ai_proposal"):
            offer = build_gap_fill_offer(
                spec=spec,
                steps=out,
                goal_query=goal_query,
                brief=brief,
                proposal=None,
                roadmap_snapshot=roadmap_snapshot,
            )
            offers.append(offer)
            continue

        gap = dict(spec.get("gap") or {})
        if not gap.get("expected_phase"):
            gap["expected_phase"] = spec.get("phase") or "vertiefung"

        proposal = None
        if include_ai_calls and len(proposals) < max_ai_proposals:
            proposal = try_suggest_ai_bridge_step(
                cur,
                goal_query=goal_query,
                brief=brief,
                step_a=step_a,
                step_b=step_b,
                gap=gap,
                title_hint=str(spec.get("title_hint") or ""),
                sketch_hint=str(spec.get("sketch") or ""),
            )

        offer = build_gap_fill_offer(
            spec=spec,
            steps=out,
            goal_query=goal_query,
            brief=brief,
            proposal=proposal,
            roadmap_snapshot=roadmap_snapshot,
        )
        offers.append(offer)

        if proposal and auto_insert_proposals:
            out.insert(idx + 1, proposal)
            proposals.append(
                {
                    "inserted_after_index": idx,
                    "proposal_key": proposal.get("proposal_key"),
                    "proposal_title": proposal.get("title"),
                    "gap": gap,
                    "offer_id": offer.get("offer_id"),
                }
            )

    return out, proposals, offers


def insert_ai_proposals_for_gaps(
    cur,
    steps: list,
    unfilled_gaps: list,
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    max_proposals: int = 2,
) -> tuple[list, list]:
    """Legacy: Fügt KI-Vorschläge für Lücken ein, wenn Bibliotheks-Brücke fehlte."""
    specs = collect_gap_fill_specs(
        steps=steps,
        unfilled_gaps=unfilled_gaps,
        off_topic_steps=[],
        llm_specs=[],
        brief=brief,
        goal_query=goal_query,
    )
    out, proposals, _offers = apply_gap_fill_after_qa(
        cur,
        steps,
        specs,
        goal_query=goal_query,
        brief=brief,
        include_ai_calls=True,
        max_ai_proposals=max_proposals,
        auto_insert_proposals=True,
    )
    return out, proposals


__all__ = [
    "apply_gap_fill_after_qa",
    "build_gap_fill_goal_text",
    "build_gap_fill_offer",
    "collect_gap_fill_specs",
    "insert_ai_proposals_for_gaps",
    "try_suggest_ai_bridge_step",
    "try_suggest_ai_stage_step",
]