mindnet/app/services/discovery.py

"""
app/services/discovery.py
Service für Link-Vorschläge und Knowledge-Discovery (WP-11).
Analysiert Drafts auf Keywords und semantische Ähnlichkeiten.
Implementiert 'Late Binding' für Edge-Typen via types.yaml.
"""
import logging
import os
from typing import List, Dict, Any, Optional
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):
        # 1. Config laden
        self.cfg = QdrantConfig.from_env()
        # Prefix Priorität: Argument > Env > Default
        self.prefix = collection_prefix or self.cfg.prefix or "mindnet"
        self.client = get_client(self.cfg)
        
        # 2. Registry für Late Binding laden (Edge Defaults)
        self.registry = self._load_type_registry()

    async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]:
        """
        Analysiert einen Draft-Text und schlägt Verlinkungen vor.
        Nutzt 'types.yaml' um den passenden Edge-Typ vorzuschlagen.
        """
        suggestions = []
        
        # Welcher Edge-Typ ist für diesen Draft-Typ (z.B. 'project') der Standard?
        # Late Binding: Wir schauen in die Config, statt es zu hardcoden.
        default_edge_type = self._get_default_edge_type(current_type)

        # 1. Exact Match: Finde Begriffe im Text, die als Notiz-Titel existieren
        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"])
            
            # Vorschlag generieren
            target_title = entity["title"]
            # Markdown-Vorschlag: [[rel:depends_on Ziel]]
            suggested_md = f"[[rel:{default_edge_type} {target_title}]]"
            
            suggestions.append({
                "type": "exact_match",
                "text_found": entity["match"],
                "target_title": target_title,
                "target_id": entity["id"],
                "suggested_edge_type": default_edge_type,
                "suggested_markdown": suggested_md,
                "confidence": 1.0,
                "reason": f"Existierender Titel (Default für '{current_type}': {default_edge_type})"
            })

        # 2. Semantic Match: Finde inhaltlich ähnliche Notizen
        semantic_hits = self._get_semantic_suggestions(text)
        
        for hit in semantic_hits:
            if hit.node_id in existing_target_ids:
                continue
            
            if hit.total_score > 0.65:
                # Bei semantischen Treffern ist 'related_to' oft sicherer als 'depends_on',
                # es sei denn, die Config erzwingt etwas anderes.
                # Wir bleiben hier beim Config-Default, um konsistent zu sein.
                target_title = hit.payload.get("title", "Unbekannt")
                suggested_md = f"[[rel:{default_edge_type} {target_title}]]"
                
                suggestions.append({
                    "type": "semantic_match",
                    "text_found": (hit.source.get("text") or "")[:50] + "...",
                    "target_title": target_title,
                    "target_id": hit.node_id,
                    "suggested_edge_type": default_edge_type,
                    "suggested_markdown": suggested_md,
                    "confidence": round(hit.total_score, 2),
                    "reason": f"Semantische Ähnlichkeit (Score: {round(hit.total_score, 2)})"
                })

        return {
            "draft_length": len(text),
            "draft_type": current_type,
            "default_strategy": default_edge_type,
            "suggestions_count": len(suggestions),
            "suggestions": suggestions
        }

    # --- Configuration & Late Binding Helpers ---

    def _load_type_registry(self) -> dict:
        """Lädt die types.yaml für Konfigurations-Zugriffe."""
        path = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
        if not os.path.exists(path):
            # Fallback relative Pfade
            if os.path.exists("types.yaml"): path = "types.yaml"
            elif os.path.exists("../config/types.yaml"): path = "../config/types.yaml"
            else: return {}
            
        try:
            with open(path, "r", encoding="utf-8") as f:
                return yaml.safe_load(f) or {}
        except Exception as e:
            logger.warning(f"Failed to load types registry: {e}")
            return {}

    def _get_default_edge_type(self, note_type: str) -> str:
        """
        Ermittelt den bevorzugten Kanten-Typ für einen gegebenen Notiz-Typ.
        Logik: types.yaml -> types -> {note_type} -> edge_defaults[0]
        Fallback: 'related_to'
        """
        # 1. Config für den Typ laden
        types_cfg = self.registry.get("types", {})
        type_def = types_cfg.get(note_type, {})
        
        # 2. Defaults prüfen
        defaults = type_def.get("edge_defaults")
        if defaults and isinstance(defaults, list) and len(defaults) > 0:
            # Wir nehmen den ersten Default als "Haupt-Beziehung"
            return defaults[0]
            
        # 3. Fallback, falls nichts konfiguriert ist
        return "related_to"

    # --- Core Logic (Unverändert) ---

    def _fetch_all_titles_and_aliases(self) -> List[Dict]:
        notes = []
        next_page = None
        col_name = f"{self.prefix}_notes"
        
        try:
            while True:
                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 {}
                    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 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]:
        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 
            aliases = entity.get("aliases", [])
            for alias in aliases:
                if alias and str(alias).lower() in text_lower:
                    found.append({"match": alias, "title": title, "id": entity["id"]})
                    break
        return found

    def _get_semantic_suggestions(self, text: str):
        req = QueryRequest(query=text, top_k=5, explain=False)
        try:
            res = hybrid_retrieve(req) 
            return res.results
        except Exception:
            return []