# chunk_payload.py
"""
Mindnet - Chunk Payload Builder
Version: 1.4.3
Beschreibung:
- Robust gegenüber alten/neuen Aufrufsignaturen (toleriert *args, **kwargs).
- Liest Typ-Defaults aus ./config/config.yaml oder ./config/types.yaml.
- Baut Chunks aus vorhandenen note.chunks (falls vorhanden) oder fällt auf
  eine einfache, profilabhängige Absatzbündelung zurück.
- Setzt in jedem Chunk-Payload:
  - note_id, chunk_id (deterministisch), index, title, type, path
  - text (nie leer), retriever_weight, chunk_profile
- Garantiert JSON-serialisierbare Payloads.
"""

from __future__ import annotations
from typing import Any, Dict, List, Optional
import os
import json
import pathlib
import re
import yaml
import hashlib


def _as_dict(note: Any) -> Dict[str, Any]:
    if isinstance(note, dict):
        return note
    d: Dict[str, Any] = {}
    for attr in (
        "id",
        "note_id",
        "title",
        "path",
        "frontmatter",
        "meta",
        "body",
        "text",
        "type",
        "chunks",
    ):
        if hasattr(note, attr):
            d[attr] = getattr(note, attr)
    if "frontmatter" not in d and hasattr(note, "metadata"):
        d["frontmatter"] = getattr(note, "metadata")
    return d


def _load_types_config(explicit: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
    if isinstance(explicit, dict):
        return explicit
    for rel in ("config/config.yaml", "config/types.yaml"):
        p = pathlib.Path(rel)
        if p.exists():
            with p.open("r", encoding="utf-8") as f:
                data = yaml.safe_load(f) or {}
            if isinstance(data, dict) and "types" in data and isinstance(data["types"], dict):
                return data["types"]
            return data if isinstance(data, dict) else {}
    return {}


def _get_front(n: Dict[str, Any]) -> Dict[str, Any]:
    fm = n.get("frontmatter") or n.get("meta") or {}
    return fm if isinstance(fm, dict) else {}


def _coalesce(*vals):
    for v in vals:
        if v is not None:
            return v
    return None


def _body(n: Dict[str, Any]) -> str:
    b = n.get("body")
    if isinstance(b, str):
        return b
    t = n.get("text")
    return t if isinstance(t, str) else ""


def _iter_chunks(n: Dict[str, Any], profile: str) -> List[Dict[str, Any]]:
    # 1) Bereits vorhandene Chunks bevorzugen
    existing = n.get("chunks")
    if isinstance(existing, list) and existing:
        out: List[Dict[str, Any]] = []
        for i, c in enumerate(existing):
            if isinstance(c, dict):
                text = c.get("text") or ""
            else:
                text = str(c) if c is not None else ""
            if not text:
                continue
            out.append({"index": i, "text": text})
        if out:
            return out

    # 2) Fallback: naive, profilabhängige Absatz-Bündelung
    size = {"short": 600, "medium": 1200, "long": 2400}.get(str(profile), 1200)
    text = _body(n)
    if not text:
        return []
    paras = re.split(r"\n{2,}", text)
    chunks: List[str] = []
    buf = ""
    for p in paras:
        p = (p or "").strip()
        if not p:
            continue
        if len(buf) + (2 if buf else 0) + len(p) <= size:
            buf = (buf + "\n\n" + p).strip() if buf else p
        else:
            if buf:
                chunks.append(buf)
            if len(p) <= size:
                buf = p
            else:
                for i in range(0, len(p), size):
                    chunks.append(p[i : i + size])
                buf = ""
    if buf:
        chunks.append(buf)
    return [{"index": i, "text": c} for i, c in enumerate(chunks)]


def make_chunk_payloads(note: Any, *args, **kwargs) -> List[Dict[str, Any]]:
    """
    Build payloads for chunks. Tolerates legacy positional arguments.
    Returns list[dict] (ein Payload pro Chunk).
    """
    n = _as_dict(note)
    types_cfg = kwargs.get("types_config") or (args[0] if args else None)
    types_cfg = _load_types_config(types_cfg)

    fm = _get_front(n)
    note_type = str(fm.get("type") or n.get("type") or "note")
    cfg_for_type = types_cfg.get(note_type, {}) if isinstance(types_cfg, dict) else {}

    try:
        default_rw = float(os.environ.get("MINDNET_DEFAULT_RETRIEVER_WEIGHT", 1.0))
    except Exception:
        default_rw = 1.0

    retriever_weight = _coalesce(
        fm.get("retriever_weight"),
        cfg_for_type.get("retriever_weight"),
        default_rw,
    )
    try:
        retriever_weight = float(retriever_weight)
    except Exception:
        retriever_weight = default_rw

    chunk_profile = _coalesce(
        fm.get("chunk_profile"),
        cfg_for_type.get("chunk_profile"),
        os.environ.get("MINDNET_DEFAULT_CHUNK_PROFILE", "medium"),
    )
    if not isinstance(chunk_profile, str):
        chunk_profile = "medium"

    note_id = n.get("note_id") or n.get("id") or fm.get("id")
    title = n.get("title") or fm.get("title") or ""
    path = n.get("path")
    if isinstance(path, pathlib.Path):
        path = str(path)

    chunks = _iter_chunks(n, chunk_profile)

    payloads: List[Dict[str, Any]] = []
    for c in chunks:
        idx = c.get("index", len(payloads))
        text = c.get("text") if isinstance(c, dict) else (str(c) if c is not None else "")
        if not isinstance(text, str):
            text = str(text or "")

        # deterministische chunk_id
        key = f"{note_id}|{idx}"
        h = hashlib.sha1(key.encode("utf-8")).hexdigest()[:12]
        chunk_id = f"{note_id}-{idx:03d}-{h}" if note_id else h

        payload = {
            "note_id": note_id,
            "chunk_id": chunk_id,
            "index": idx,
            "title": title,
            "type": note_type,
            "path": path,
            "text": text,
            "retriever_weight": retriever_weight,
            "chunk_profile": chunk_profile,
        }

        # JSON-Serialisierbarkeit sicherstellen
        json.loads(json.dumps(payload, ensure_ascii=False))
        payloads.append(payload)

    return payloads