#!/usr/bin/env python3
"""
Chat-Auto-Save: Wertvolle User-Nachrichten → Engramm.
Wird am Ende jeder Main-Session-Antwort aufgerufen.
"""

import sys
import json
import hashlib
from pathlib import Path

BRAIN_DIR = Path("/root/.openclaw/workspace/second-brain")
sys.path.insert(0, str(BRAIN_DIR))

from src.engram import Engram, Grounding
from src.store import EngramStore

DB_PATH = BRAIN_DIR / "data" / "brain.sqlite"


def _hash(text: str) -> str:
    return hashlib.sha256(text.encode("utf-8")).hexdigest()[:12]


def is_fluff(content: str) -> bool:
    """Prüft ob Inhalt nur Floskel ist."""
    lower = content.lower().strip().rstrip(".?!")
    short_fluff = [
        "hallo", "hi", "hey", "guten tag", "guten morgen", "guten abend",
        "danke", "ok", "okay", "ja", "nein", "bitte", "gerne", "tschüss",
        "bis später", "bis morgen", "alles klar", "in ordnung",
    ]
    if lower in short_fluff:
        return True
    if len(content) < 10 and all(c in " ?,!.;:-" for c in content):
        return True
    return False


def save_if_worthy(content: str, source: str = "user", tags: list = None,
                   confidence: float = 0.7, session_id: str = None,
                   reasoning: str = None) -> dict:
    """
    Speichert Nachricht als Engramm wenn sie Wert hat.
    Wird in jeder Antwort aufgerufen.
    """
    
    if is_fluff(content):
        return {"saved": False, "reason": "fluff"}
    
    store = EngramStore(str(DB_PATH))
    content_hash = _hash(content)
    recent = store.get_all(limit=200)
    for eg in recent:
        if _hash(eg.content) == content_hash:
            return {"saved": False, "reason": "duplicate", "id": str(eg.id)}
    
    eg = Engram.create(
        content=content,
        source=source,
        tags=tags or ["auto-save", "chat"],
        session_id=session_id,
        confidence=confidence,
        grounding=Grounding.ASSUMPTION,
    )
    store.save(eg)
    
    return {
        "saved": True,
        "id": str(eg.id),
        "confidence": eg.compute_confidence(),
        "first8": str(eg.id)[:8],
    }


def enrich_prompt(topic: str, limit: int = 3) -> str:
    """
    Holt relevante bestätigte Engramme für Kontext-Anreicherung.
    Wird VOR jeder Antwort aufgerufen.
    """
    store = EngramStore(str(DB_PATH))
    recent = store.get_all(limit=100)
    
    # Einfache Text-Suche (kein FTS wegen Satzzeichen)
    topic_lower = topic.lower()
    matches = []
    for eg in recent:
        if eg.correctness.confirmed and topic_lower in eg.content.lower():
            matches.append(eg)
        elif len(matches) < limit and any(t in topic_lower for t in [t.lower() for t in eg.metadata.get("tags", [])]):
            matches.append(eg)
        if len(matches) >= limit:
            break
    
    if not matches:
        return ""
    
    lines = ["\n📚 Relevantes Wissen:"]
    for eg in matches[:limit]:
        lines.append(f"  • [{eg.compute_confidence():.0%}] {eg.content[:120]}")
    
    return "\n".join(lines)


def check_pending(session_id: str = None) -> list:
    """Gibt unbestätigte Engramme zurück."""
    store = EngramStore(str(DB_PATH))
    egs = store.get_all(limit=50)
    pending = [eg for eg in egs if not eg.correctness.confirmed]
    return pending


if __name__ == "__main__":
    import sys
    if len(sys.argv) > 1:
        result = save_if_worthy(sys.argv[1])
        print(json.dumps(result, indent=2))
    else:
        print("Usage: python3 chat_autosave.py 'Nachricht'")