"""
FILE: app/core/graph/graph_db_adapter.py
DESCRIPTION: Datenbeschaffung aus Qdrant für den Graphen.
             AUDIT v1.1.0: Nutzt nun die zentrale database-Infrastruktur für Namen.
"""
from typing import List, Dict, Optional
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest

# ENTSCHEIDENDER FIX: Nutzt die neue Infrastruktur für konsistente Collection-Namen
from app.core.database import collection_names

def fetch_edges_from_qdrant(
    client: QdrantClient,
    prefix: str,
    seeds: List[str],
    edge_types: Optional[List[str]] = None,
    limit: int = 2048,
) -> List[Dict]:
    """
    Holt Edges aus der Datenbank basierend auf Seed-IDs.
    Filtert auf source_id, target_id oder note_id.
    """
    if not seeds or limit <= 0:
        return []

    # Konsistente Namensauflösung via database-Paket
    _, _, edges_col = collection_names(prefix)

    seed_conditions = []
    for field in ("source_id", "target_id", "note_id"):
        for s in seeds:
            seed_conditions.append(
                rest.FieldCondition(key=field, match=rest.MatchValue(value=str(s)))
            )
    seeds_filter = rest.Filter(should=seed_conditions) if seed_conditions else None

    type_filter = None
    if edge_types:
        type_conds = [
            rest.FieldCondition(key="kind", match=rest.MatchValue(value=str(k)))
            for k in edge_types
        ]
        type_filter = rest.Filter(should=type_conds)

    must = []
    if seeds_filter: 
        must.append(seeds_filter)
    if type_filter: 
        must.append(type_filter)
    
    flt = rest.Filter(must=must) if must else None

    # Abfrage via Qdrant Scroll API
    pts, _ = client.scroll(
        collection_name=edges_col,
        scroll_filter=flt,
        limit=limit,
        with_payload=True,
        with_vectors=False,
    )

    return [dict(p.payload) for p in pts if p.payload]