"""Qdrant — векторный срез (EmbeddingGemma 2, dim=768)."""
from __future__ import annotations
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct, Filter, FieldCondition, MatchValue
from . import config
from .embedding import embed

_client: QdrantClient | None = None

def client() -> QdrantClient:
    global _client
    if _client is None:
        _client = QdrantClient(url=config.QDRANT_URL)
    return _client

def ensure_collection():
    c = client()
    try:
        c.get_collection(config.QDRANT_COLLECTION)
    except Exception:
        c.create_collection(
            collection_name=config.QDRANT_COLLECTION,
            vectors_config=VectorParams(size=config.EMBED_DIM, distance=Distance.COSINE),
        )

def upsert_atom(atom_id: int, text: str, payload: dict) -> None:
    """text — что эмбеддим; payload — type/confidence/status/doc_id/claim."""
    vec = embed(text)
    c = client()
    c.upsert(
        collection_name=config.QDRANT_COLLECTION,
        points=[PointStruct(id=atom_id, vector=vec, payload=payload)],
    )

def delete_atom(atom_id: int) -> None:
    client().delete(
        collection_name=config.QDRANT_COLLECTION,
        points_selector=[atom_id],
    )

def search(query: str, top_k: int = 5, type_filter: str | None = None,
           status_filter: str | None = None) -> list[dict]:
    """Семантический поиск по атомам."""
    qvec = embed(query)
    must = []
    if type_filter:
        must.append(FieldCondition(key="type", match=MatchValue(value=type_filter)))
    if status_filter:
        must.append(FieldCondition(key="status", match=MatchValue(value=status_filter)))
    qfilter = Filter(must=must) if must else None
    hits = client().query_points(
        collection_name=config.QDRANT_COLLECTION,
        query=qvec,
        limit=top_k,
        query_filter=qfilter,
    ).points
    return [{"id": h.id, "score": h.score, **h.payload} for h in hits]