"""
Epistemic learning trajectory storage and search.
"""
from __future__ import annotations
import logging
from typing import Dict, List, Optional

from empirica.core.qdrant.connection import (
    _check_qdrant_available, _get_qdrant_imports, _get_qdrant_client,
    _get_embedding_safe, _service_url, _rest_search, logger,
)
from empirica.core.qdrant.collections import _epistemics_collection

def upsert_epistemics(project_id: str, items: List[Dict]) -> int:
    """
    Store epistemic learning trajectories (PREFLIGHT → POSTFLIGHT deltas).
    Returns number of items upserted, or 0 if Qdrant not available.
    """
    if not _check_qdrant_available():
        return 0

    try:
        _, _, _, PointStruct = _get_qdrant_imports()
        client = _get_qdrant_client()
        if client is None:
            return 0
        coll = _epistemics_collection(project_id)
        points = []

        for item in items:
            vector = _get_embedding_safe(item.get("text", ""))
            if vector is None:
                continue
            payload = item.get("metadata", {})
            points.append(PointStruct(id=item["id"], vector=vector, payload=payload))

        if points:
            client.upsert(collection_name=coll, points=points)
        return len(points)
    except Exception as e:
        logger.warning(f"Failed to upsert epistemics: {e}")
        return 0


def search_epistemics(
    project_id: str,
    query_text: str,
    filters: Optional[Dict] = None,
    limit: int = 5
) -> List[Dict]:
    """
    Search epistemic learning trajectories by semantic similarity.
    Returns empty list if Qdrant not available.
    """
    if not _check_qdrant_available():
        return []

    qvec = _get_embedding_safe(query_text)
    if qvec is None:
        return []

    try:
        client = _get_qdrant_client()
        if client is None:
            return []
        coll = _epistemics_collection(project_id)
        results = client.query_points(
            collection_name=coll,
            query=qvec,
            limit=limit,
            with_payload=True
        )
        return [
            {
                "score": getattr(r, 'score', 0.0) or 0.0,
                **(r.payload or {})
            }
            for r in results.points
        ]
    except Exception as e:
        logger.debug(f"search_epistemics failed: {e}")

    # REST fallback
    try:
        coll = _epistemics_collection(project_id)
        rd = _rest_search(coll, qvec, limit)
        return [
            {
                "score": d.get('score', 0.0),
                **(d.get('payload') or {})
            }
            for d in rd
        ]
    except Exception as e:
        logger.debug(f"search_epistemics REST fallback failed: {e}")
        return []

