"""
FILE: app/core/qdrant_points.py
DESCRIPTION: Object-Mapper für Qdrant. Konvertiert JSON-Payloads (Notes, Chunks, Edges) in PointStructs und generiert deterministische UUIDs.
VERSION: 1.5.0
STATUS: Active
DEPENDENCIES: qdrant_client, uuid, os
LAST_ANALYSIS: 2025-12-15
"""
from __future__ import annotations
import os
import uuid
from typing import List, Tuple, Iterable, Optional, Dict, Any

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

# --------------------- ID helpers ---------------------

def _to_uuid(stable_key: str) -> str:
    return str(uuid.uuid5(uuid.NAMESPACE_URL, stable_key))

def _names(prefix: str) -> Tuple[str, str, str]:
    return f"{prefix}_notes", f"{prefix}_chunks", f"{prefix}_edges"

# --------------------- Points builders ---------------------

def points_for_note(prefix: str, note_payload: dict, note_vec: List[float] | None, dim: int) -> Tuple[str, List[rest.PointStruct]]:
    notes_col, _, _ = _names(prefix)
    vector = note_vec if note_vec is not None else [0.0] * int(dim)
    raw_note_id = note_payload.get("note_id") or note_payload.get("id") or "missing-note-id"
    point_id = _to_uuid(raw_note_id)
    pt = rest.PointStruct(id=point_id, vector=vector, payload=note_payload)
    return notes_col, [pt]

def points_for_chunks(prefix: str, chunk_payloads: List[dict], vectors: List[List[float]]) -> Tuple[str, List[rest.PointStruct]]:
    _, chunks_col, _ = _names(prefix)
    points: List[rest.PointStruct] = []
    for i, (pl, vec) in enumerate(zip(chunk_payloads, vectors), start=1):
        chunk_id = pl.get("chunk_id") or pl.get("id")
        if not chunk_id:
            note_id = pl.get("note_id") or pl.get("parent_note_id") or "missing-note"
            chunk_id = f"{note_id}#{i}"
            pl["chunk_id"] = chunk_id
        point_id = _to_uuid(chunk_id)
        points.append(rest.PointStruct(id=point_id, vector=vec, payload=pl))
    return chunks_col, points

def _normalize_edge_payload(pl: dict) -> dict:
    kind = pl.get("kind") or pl.get("edge_type") or "edge"
    source_id = pl.get("source_id") or pl.get("src_id") or "unknown-src"
    target_id = pl.get("target_id") or pl.get("dst_id") or "unknown-tgt"
    seq = pl.get("seq") or pl.get("order") or pl.get("index")

    pl.setdefault("kind", kind)
    pl.setdefault("source_id", source_id)
    pl.setdefault("target_id", target_id)
    if seq is not None and "seq" not in pl:
        pl["seq"] = seq
    return pl

def points_for_edges(prefix: str, edge_payloads: List[dict]) -> Tuple[str, List[rest.PointStruct]]:
    _, _, edges_col = _names(prefix)
    points: List[rest.PointStruct] = []
    for raw in edge_payloads:
        pl = _normalize_edge_payload(raw)
        edge_id = pl.get("edge_id")
        if not edge_id:
            kind = pl.get("kind", "edge")
            s = pl.get("source_id", "unknown-src")
            t = pl.get("target_id", "unknown-tgt")
            seq = pl.get("seq") or ""
            edge_id = f"{kind}:{s}->{t}#{seq}"
            pl["edge_id"] = edge_id
        point_id = _to_uuid(edge_id)
        points.append(rest.PointStruct(id=point_id, vector=[0.0], payload=pl))
    return edges_col, points

# --------------------- Vector schema & overrides ---------------------

def _preferred_name(candidates: List[str]) -> str:
    for k in ("text", "default", "embedding", "content"):
        if k in candidates:
            return k
    return sorted(candidates)[0]

def _env_override_for_collection(collection: str) -> Optional[str]:
    """
    Returns:
      - "__single__" to force single-vector
      - concrete name (str) to force named-vector with that name
      - None to auto-detect
    """
    base = os.getenv("MINDNET_VECTOR_NAME")
    if collection.endswith("_notes"):
        base = os.getenv("NOTES_VECTOR_NAME", base)
    elif collection.endswith("_chunks"):
        base = os.getenv("CHUNKS_VECTOR_NAME", base)
    elif collection.endswith("_edges"):
        base = os.getenv("EDGES_VECTOR_NAME", base)

    if not base:
        return None
    val = base.strip()
    if val.lower() in ("__single__", "single"):
        return "__single__"
    return val  # concrete name

def _get_vector_schema(client: QdrantClient, collection_name: str) -> dict:
    """
    Return {"kind": "single", "size": int} or {"kind": "named", "names": [...], "primary": str}.
    """
    try:
        info = client.get_collection(collection_name=collection_name)
        vecs = getattr(info, "vectors", None)
        # Single-vector config
        if hasattr(vecs, "size") and isinstance(vecs.size, int):
            return {"kind": "single", "size": vecs.size}
        # Named-vectors config (dict-like in .config)
        cfg = getattr(vecs, "config", None)
        if isinstance(cfg, dict) and cfg:
            names = list(cfg.keys())
            if names:
                return {"kind": "named", "names": names, "primary": _preferred_name(names)}
    except Exception:
        pass
    return {"kind": "single", "size": None}

def _as_named(points: List[rest.PointStruct], name: str) -> List[rest.PointStruct]:
    out: List[rest.PointStruct] = []
    for pt in points:
        vec = getattr(pt, "vector", None)
        if isinstance(vec, dict):
            if name in vec:
                out.append(pt)
            else:
                # take any existing entry; if empty dict fallback to [0.0]
                fallback_vec = None
                try:
                    fallback_vec = list(next(iter(vec.values())))
                except Exception:
                    fallback_vec = [0.0]
                out.append(rest.PointStruct(id=pt.id, vector={name: fallback_vec}, payload=pt.payload))
        elif vec is not None:
            out.append(rest.PointStruct(id=pt.id, vector={name: vec}, payload=pt.payload))
        else:
            out.append(pt)
    return out

# --------------------- Qdrant ops ---------------------

def upsert_batch(client: QdrantClient, collection: str, points: List[rest.PointStruct]) -> None:
    if not points:
        return

    # 1) ENV overrides come first
    override = _env_override_for_collection(collection)
    if override == "__single__":
        client.upsert(collection_name=collection, points=points, wait=True)
        return
    elif isinstance(override, str):
        client.upsert(collection_name=collection, points=_as_named(points, override), wait=True)
        return

    # 2) Auto-detect schema
    schema = _get_vector_schema(client, collection)
    if schema.get("kind") == "named":
        name = schema.get("primary") or _preferred_name(schema.get("names") or [])
        client.upsert(collection_name=collection, points=_as_named(points, name), wait=True)
        return

    # 3) Fallback single-vector
    client.upsert(collection_name=collection, points=points, wait=True)

# --- Optional search helpers ---

def _filter_any(field: str, values: Iterable[str]) -> rest.Filter:
    return rest.Filter(should=[rest.FieldCondition(key=field, match=rest.MatchValue(value=v)) for v in values])

def _merge_filters(*filters: Optional[rest.Filter]) -> Optional[rest.Filter]:
    fs = [f for f in filters if f is not None]
    if not fs:
        return None
    if len(fs) == 1:
        return fs[0]
    must = []
    for f in fs:
        if getattr(f, "must", None):
            must.extend(f.must)
        if getattr(f, "should", None):
            must.append(rest.Filter(should=f.should))
    return rest.Filter(must=must)

def _filter_from_dict(filters: Optional[Dict[str, Any]]) -> Optional[rest.Filter]:
    if not filters:
        return None
    parts = []
    for k, v in filters.items():
        if isinstance(v, (list, tuple, set)):
            parts.append(_filter_any(k, [str(x) for x in v]))
        else:
            parts.append(rest.Filter(must=[rest.FieldCondition(key=k, match=rest.MatchValue(value=v))]))
    return _merge_filters(*parts)

def search_chunks_by_vector(client: QdrantClient, prefix: str, vector: List[float], top: int = 10, filters: Optional[Dict[str, Any]] = None) -> List[Tuple[str, float, dict]]:
    _, chunks_col, _ = _names(prefix)
    flt = _filter_from_dict(filters)
    res = client.search(collection_name=chunks_col, query_vector=vector, limit=top, with_payload=True, with_vectors=False, query_filter=flt)
    out: List[Tuple[str, float, dict]] = []
    for r in res:
        out.append((str(r.id), float(r.score), dict(r.payload or {})))
    return out


# --- Edge retrieval helper ---

def get_edges_for_sources(
    client: QdrantClient,
    prefix: str,
    source_ids: Iterable[str],
    edge_types: Optional[Iterable[str]] = None,
    limit: int = 2048,
) -> List[Dict[str, Any]]:
    """Retrieve edge payloads from the <prefix>_edges collection.

    Args:
        client: QdrantClient instance.
        prefix: Mindnet collection prefix (e.g. "mindnet").
        source_ids: Iterable of source_id values (typically chunk_ids or note_ids).
        edge_types: Optional iterable of edge kinds (e.g. ["references", "depends_on"]). If None,
            all kinds are returned.
        limit: Maximum number of edge payloads to return.

    Returns:
        A list of edge payload dicts, e.g.:
        {
            "note_id": "...",
            "chunk_id": "...",
            "kind": "references" | "depends_on" | ...,
            "scope": "chunk",
            "source_id": "...",
            "target_id": "...",
            "rule_id": "...",
            "confidence": 0.7,
            ...
        }
    """
    source_ids = list(source_ids)
    if not source_ids or limit <= 0:
        return []

    # Resolve collection name
    _, _, edges_col = _names(prefix)

    # Build filter: source_id IN source_ids
    src_filter = _filter_any("source_id", [str(s) for s in source_ids])

    # Optional: kind IN edge_types
    kind_filter = None
    if edge_types:
        kind_filter = _filter_any("kind", [str(k) for k in edge_types])

    flt = _merge_filters(src_filter, kind_filter)

    out: List[Dict[str, Any]] = []
    next_page = None
    remaining = int(limit)

    # Use paginated scroll API; we don't need vectors, only payloads.
    while remaining > 0:
        batch_limit = min(256, remaining)
        res, next_page = client.scroll(
            collection_name=edges_col,
            scroll_filter=flt,
            limit=batch_limit,
            with_payload=True,
            with_vectors=False,
            offset=next_page,
        )

        if not res:
            break

        for r in res:
            out.append(dict(r.payload or {}))
            remaining -= 1
            if remaining <= 0:
                break

        if next_page is None or remaining <= 0:
            break

    return out