mindnet/app/services/discovery.py

"""
app/services/discovery.py
Updated for WP-11: Sliding Window Analysis.
"""
import logging
import asyncio
from typing import List, Dict, Any
import yaml

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 = None):
        self.cfg = QdrantConfig.from_env()
        self.prefix = collection_prefix or self.cfg.prefix or "mindnet"
        self.client = get_client(self.cfg)
        self.registry = self._load_type_registry()

    async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]:
        """
        Analysiert den Draft mit Sliding Window Strategie.
        """
        suggestions = []
        default_edge_type = self._get_default_edge_type(current_type)

        # 1. Exact Match (Läuft über gesamten Text, schnell genug)
        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"],
                "suggested_edge_type": default_edge_type,
                "suggested_markdown": f"[[rel:{default_edge_type} {entity['title']}]]",
                "confidence": 1.0,
                "reason": f"Exakter Treffer ({entity['match']})"
            })

        # 2. Semantic Match (Sliding Window)
        # Wir zerlegen den Text in relevante Chunks, um Token-Limits zu umgehen
        # und Fokus zu streuen.
        search_queries = self._generate_search_queries(text)
        
        # Parallel Execution für alle Queries
        tasks = [self._get_semantic_suggestions_async(q) for q in search_queries]
        results_list = await asyncio.gather(*tasks)
        
        # Ergebnisse mergen und deduplizieren
        seen_semantic_ids = set()
        
        for hits in results_list:
            for hit in hits:
                if hit.node_id in existing_target_ids or hit.node_id in seen_semantic_ids:
                    continue
                
                # Threshold Tuning: Bei 'nomic' sind Scores oft niedriger (0.4-0.6 ist schon gut)
                # Wir setzen ihn moderat auf 0.50
                if hit.total_score > 0.50:
                    seen_semantic_ids.add(hit.node_id)
                    target_title = hit.payload.get("title") or hit.node_id
                    
                    suggestions.append({
                        "type": "semantic_match",
                        "text_found": (hit.source.get("text") or "")[:60] + "...",
                        "target_title": target_title,
                        "target_id": hit.node_id,
                        "suggested_edge_type": default_edge_type,
                        "suggested_markdown": f"[[rel:{default_edge_type} {target_title}]]",
                        "confidence": round(hit.total_score, 2),
                        "reason": f"Semantisch ähnlich ({hit.total_score:.2f})"
                    })

        # Sortieren nach Confidence
        suggestions.sort(key=lambda x: x["confidence"], reverse=True)

        return {
            "draft_length": len(text),
            "suggestions_count": len(suggestions),
            "suggestions": suggestions[:10] # Limit auf Top 10
        }

    # --- Helpers ---

    def _generate_search_queries(self, text: str) -> List[str]:
        """
        Zerlegt den Text in bis zu 3 Such-Queries:
        1. Der Anfang (Kontext/Einleitung)
        2. Die Mitte (Details)
        3. Das Ende (Fazit/Zusammenfassung)
        """
        if len(text) < 600:
            return [text]
            
        queries = []
        # Query 1: Die ersten 400 Zeichen
        queries.append(text[:400])
        
        # Query 2: Ein Fenster aus der Mitte
        mid = len(text) // 2
        queries.append(text[mid-200 : mid+200])
        
        # Query 3: Die letzten 400 Zeichen
        if len(text) > 800:
            queries.append(text[-400:])
            
        return queries

    async def _get_semantic_suggestions_async(self, text: str):
        # Nutzt hybrid_retrieve (sync), aber hier in Async Context okay
        req = QueryRequest(query=text, top_k=5, explain=False)
        try:
            res = hybrid_retrieve(req)
            return res.results
        except Exception: return []

    # ... (Restliche Methoden wie _fetch_all_titles_and_aliases bleiben gleich) ...
    # Füge hier die Methoden aus dem vorherigen Artefakt ein (fetch_all..., find_entities..., load_type...)
    # Der Kürze halber lasse ich sie im Snippet weg, da sie unverändert sind.
    
    def _load_type_registry(self) -> dict:
        path = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
        if not os.path.exists(path):
            if os.path.exists("types.yaml"): path = "types.yaml"
            else: return {}
        try:
            with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {}
        except Exception: return {}

    def _get_default_edge_type(self, note_type: str) -> str:
        types_cfg = self.registry.get("types", {})
        type_def = types_cfg.get(note_type, {})
        defaults = type_def.get("edge_defaults")
        return defaults[0] if defaults else "related_to"

    def _fetch_all_titles_and_aliases(self) -> List[Dict]:
        notes = []
        next_page = None
        col = f"{self.prefix}_notes"
        try:
            while True:
                res, next_page = self.client.scroll(collection_name=col, limit=1000, offset=next_page, with_payload=True, with_vectors=False)
                for point in res:
                    pl = point.payload or {}
                    aliases = pl.get("aliases") or []
                    if isinstance(aliases, str): aliases = [aliases]
                    notes.append({"id": pl.get("note_id"), "title": pl.get("title"), "aliases": aliases})
                if next_page is None: break
        except Exception: pass
        return notes

    def _find_entities_in_text(self, text: str, entities: List[Dict]) -> List[Dict]:
        found = []
        text_lower = text.lower()
        for entity in entities:
            title = entity.get("title")
            if title and title.lower() in text_lower:
                found.append({"match": title, "title": title, "id": entity["id"]})
                continue
            for alias in entity.get("aliases", []):
                if str(alias).lower() in text_lower:
                    found.append({"match": alias, "title": title, "id": entity["id"]})
                    break
        return found