"""
app/services/discovery.py
Service für Link-Vorschläge und Knowledge-Discovery (WP-11).
Analysiert Drafts auf Keywords und semantische Ähnlichkeiten.
"""
import logging
from typing import List, Dict, Any, Set
from qdrant_client.http import models as rest

from app.core.qdrant import QdrantConfig, get_client
from app.models.dto import QueryRequest
from app.core.retriever import hybrid_retrieve

logger = logging.getLogger(__name__)

class DiscoveryService:
    def __init__(self, collection_prefix: str = "mindnet"):
        self.prefix = collection_prefix
        self.cfg = QdrantConfig.from_env()
        self.cfg.prefix = collection_prefix
        self.client = get_client(self.cfg)

    async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]:
        """
        Analysiert einen Draft-Text und schlägt Verlinkungen vor.
        Kombiniert Exact Match (Titel) und Semantic Match (Vektor).
        """
        suggestions = []
        
        # 1. Exact Match: Finde Begriffe im Text, die als Notiz-Titel existieren
        # (Bei sehr großen Vaults >10k sollte dies gecached werden)
        known_entities = self._fetch_all_titles_and_aliases()
        found_entities = self._find_entities_in_text(text, known_entities)
        
        existing_target_ids = set()

        for entity in found_entities:
            existing_target_ids.add(entity["id"])
            suggestions.append({
                "type": "exact_match",
                "text_found": entity["match"],
                "target_title": entity["title"],
                "target_id": entity["id"],
                "confidence": 1.0,
                "reason": "Existierender Notiz-Titel"
            })

        # 2. Semantic Match: Finde inhaltlich ähnliche Notizen via Vektor-Suche
        # Wir filtern Ergebnisse heraus, die wir schon per Exact Match gefunden haben.
        semantic_hits = self._get_semantic_suggestions(text)
        
        for hit in semantic_hits:
            if hit.node_id in existing_target_ids:
                continue
            
            # Schwellwert: Nur relevante Vorschläge anzeigen (z.B. > 0.65)
            # Wir nutzen den total_score, der bereits Typ-Gewichte enthält.
            if hit.total_score > 0.65:
                suggestions.append({
                    "type": "semantic_match",
                    "text_found": (hit.source.get("text") or "")[:50] + "...", # Snippet
                    "target_title": hit.payload.get("title", "Unbekannt"),
                    "target_id": hit.node_id,
                    "confidence": round(hit.total_score, 2),
                    "reason": f"Inhaltliche Ähnlichkeit (Score: {round(hit.total_score, 2)})"
                })

        return {
            "draft_length": len(text),
            "suggestions_count": len(suggestions),
            "suggestions": suggestions
        }

    def _fetch_all_titles_and_aliases(self) -> List[Dict]:
        """Lädt alle Titel und Aliases aus der Notes-Collection."""
        notes = []
        next_page = None
        col_name = f"{self.prefix}_notes"
        
        try:
            while True:
                # Scroll API nutzen, um effizient alle Metadaten zu laden
                res, next_page = self.client.scroll(
                    collection_name=col_name,
                    limit=1000,
                    offset=next_page,
                    with_payload=True,
                    with_vectors=False
                )
                for point in res:
                    pl = point.payload or {}
                    notes.append({
                        "id": pl.get("note_id"),
                        "title": pl.get("title"),
                        "aliases": pl.get("aliases", [])
                    })
                
                if next_page is None:
                    break
        except Exception as e:
            logger.error(f"Error fetching titles: {e}")
            return []
            
        return notes

    def _find_entities_in_text(self, text: str, entities: List[Dict]) -> List[Dict]:
        """
        Sucht Vorkommen von Titeln im Text (Case-Insensitive).
        """
        found = []
        text_lower = text.lower()
        
        for entity in entities:
            # 1. Titel prüfen
            title = entity.get("title")
            if title and title.lower() in text_lower:
                found.append({
                    "match": title, 
                    "title": title, 
                    "id": entity["id"]
                })
                continue # Wenn Titel gefunden, Aliases nicht mehr prüfen (Prio)

            # 2. Aliases prüfen
            aliases = entity.get("aliases")
            if aliases and isinstance(aliases, list):
                for alias in aliases:
                    if alias and str(alias).lower() in text_lower:
                        found.append({
                            "match": alias, 
                            "title": title, # Target ist immer der Haupt-Titel
                            "id": entity["id"]
                        })
                        break
        return found

    def _get_semantic_suggestions(self, text: str):
        """Wrapper um den Hybrid Retriever."""
        # Wir nutzen eine vereinfachte Query
        req = QueryRequest(
            query=text,
            top_k=5,
            explain=False
        )
        try:
            # hybrid_retrieve ist sync, wird aber schnell genug sein für diesen Kontext
            res = hybrid_retrieve(req) 
            return res.results
        except Exception as e:
            logger.error(f"Semantic suggestion failed: {e}")
            return []