from __future__ import annotations

import logging
from typing import Any

from qdrant_client import QdrantClient
from qdrant_client.http import models as qm

from app.config import get_settings

logger = logging.getLogger(__name__)

COLLECTION_FACTS = "memory_facts"
COLLECTION_SUMMARIES = "session_summaries"
COLLECTION_DOC_CHUNKS = "document_chunks"
VECTOR_SIZE = 1536


def _client() -> QdrantClient:
    settings = get_settings()
    return QdrantClient(url=settings.qdrant_url)


def ensure_collections() -> None:
    settings = get_settings()
    if not settings.rag_enabled:
        return
    client = _client()
    for name in (COLLECTION_FACTS, COLLECTION_SUMMARIES, COLLECTION_DOC_CHUNKS):
        if client.collection_exists(name):
            continue
        client.create_collection(
            collection_name=name,
            vectors_config=qm.VectorParams(size=VECTOR_SIZE, distance=qm.Distance.COSINE),
        )
        logger.info("Created Qdrant collection %s", name)


def upsert_points(collection: str, points: list[qm.PointStruct]) -> None:
    if not points:
        return
    _client().upsert(collection_name=collection, points=points)


def delete_by_filter(collection: str, must: list[qm.FieldCondition]) -> None:
    _client().delete(
        collection_name=collection,
        points_selector=qm.FilterSelector(filter=qm.Filter(must=must)),
    )


def search(
    collection: str,
    vector: list[float],
    *,
    limit: int,
    query_filter: qm.Filter | None = None,
) -> list[qm.ScoredPoint]:
    return _client().search(
        collection_name=collection,
        query_vector=vector,
        limit=limit,
        query_filter=query_filter,
    )