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Lars 2025-11-11 16:58:22 +01:00
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@ -1,43 +1,35 @@
# chunk_payload.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
app/core/chunk_payload.py Mindnet V2 (compat)
Ziel:
- Bewahrt bestehendes Verhalten (index, chunk_profile, retriever_weight, etc.)
- Ergänzt optionale Denormalisierung: `tags` aus der NoteFM auch auf Chunks
- Fügt Aliase für die ChunkNummer hinzu: `ord` (v2Schema), `chunk_num`, `Chunk_Nummer`
(Letztere ist rein UI/Filter-freundlich für deine bestehenden Indizes mit dt. Keys.)
Hinweis:
- `edge_defaults` gehören konzeptionell zur *Note* (Regelmenge des Quelltyps)
und werden nicht pro Chunk repliziert. Falls gewünscht, kann das optional
ergänzt werden aktuell **nicht** gesetzt, siehe Design-Kommentar im PR.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Tuple
import os, json, pathlib, re, yaml, hashlib
FRONTMATTER_RE = re.compile(r"^---\s*\n.*?\n---\s*\n?", re.DOTALL)
import json
import os
import pathlib
import hashlib
from typing import Any, Dict, List, Optional
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","metadata","body","text","content","raw","markdown","type","chunks"):
if hasattr(note, attr):
d[attr] = getattr(note, attr)
fm = d.get("frontmatter") or d.get("meta") or d.get("metadata") or {}
d["frontmatter"] = fm if isinstance(fm, dict) else {}
return d
from app.core.chunker import assemble_chunks
def _pick_args(*args, **kwargs) -> Tuple[Optional[str], Optional[Dict[str,Any]]]:
path = kwargs.get("path")
types_cfg = kwargs.get("types_config")
for a in args:
if path is None and isinstance(a, (str, pathlib.Path)):
path = str(a)
if types_cfg is None and isinstance(a, dict):
types_cfg = a
return path, types_cfg
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 _as_dict(obj):
if isinstance(obj, dict): return obj
try:
return dict(obj) # type: ignore
except Exception:
return {"value": obj}
def _coalesce(*vals):
for v in vals:
@ -45,94 +37,52 @@ def _coalesce(*vals):
return v
return None
def _text_from_note(n: Dict[str, Any], path_hint: Optional[str]) -> str:
# häufige Felder
cand = [
n.get("body"),
n.get("text"),
n.get("markdown"),
n.get("raw"),
]
content = n.get("content")
if isinstance(content, str):
cand.append(content)
elif isinstance(content, dict):
for k in ("text","body","raw","markdown","content"):
v = content.get(k)
if isinstance(v, str):
cand.append(v)
for t in cand:
if isinstance(t, str) and t.strip():
return t
def _env_float(name: str, default: float) -> float:
try:
return float(os.environ.get(name, default))
except Exception:
return default
# Fallback: Datei lesen und Frontmatter entfernen
p = n.get("path") or path_hint
if p:
try:
pth = pathlib.Path(p)
if pth.exists():
txt = pth.read_text(encoding="utf-8", errors="ignore")
if txt:
return FRONTMATTER_RE.sub("", txt).strip()
except Exception:
pass
return ""
def _ensure_list(x) -> list:
if x is None: return []
if isinstance(x, list): return [str(i) for i in x]
if isinstance(x, (set, tuple)): return [str(i) for i in x]
return [str(x)]
def _iter_chunks(n: Dict[str, Any], profile: str, fulltext: str) -> List[Dict[str, Any]]:
# 1) vorhandene Chunks nehmen, wenn sinnvoll
existing = n.get("chunks")
out: List[Dict[str, Any]] = []
if isinstance(existing, list) and existing:
for i, c in enumerate(existing):
text = ""
if isinstance(c, dict):
text = c.get("text") or c.get("body") or c.get("raw") or ""
elif isinstance(c, str):
text = c
if text and text.strip():
out.append({"index": i, "text": text})
if out:
return out
def _load_types_config(types_cfg_explicit: Optional[dict] = None) -> dict:
"""Types-Registry *optional* einspeisen (bereits geparst), sonst lazy-laden vermeiden."""
return types_cfg_explicit or {}
# 2) Fallback: profilabhängige Bündelung
if not isinstance(profile, str):
profile = "medium"
size = {"short": 600, "medium": 1200, "long": 2400}.get(profile, 1200)
if not fulltext:
return []
paras = re.split(r"\n{2,}", fulltext)
buf = ""
chunks: List[str] = []
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 _text_from_note(note: Dict[str, Any], path: str) -> str:
# Erwartete Inputs (siehe parser.py / import_markdown.py):
# note["body"] oder note["text"]; Fallback leerer String
return note.get("body") or note.get("text") or ""
def make_chunk_payloads(note: Any, *args, **kwargs) -> List[Dict[str, Any]]:
def _iter_chunks(note: Dict[str, Any], chunk_profile: str, fulltext: str) -> List[Dict[str, Any]]:
"""Nutze bestehenden assemble_chunks(note_id, body, type) Pfad, keine Doppel-Logik hier."""
note_id = note.get("id") or (note.get("frontmatter") or {}).get("id")
ntype = (note.get("frontmatter") or {}).get("type") or note.get("type") or "note"
# assemble_chunks liefert Liste von Dicts mit mindestens {"index","text"} (v1)
return assemble_chunks(note_id, fulltext, ntype)
def make_chunk_payloads(note: Any, path_arg: Optional[str], chunks_from_chunker: Optional[List[Dict[str, Any]]] = None, *, note_text: Optional[str] = None, types_cfg: Optional[dict] = None) -> List[Dict[str, Any]]:
"""
Erzeugt Chunk-Payloads. Erwartet:
- `note`: Normalisierte Note-Struktur (inkl. frontmatter)
- `path_arg`: Pfad der Note
- `chunks_from_chunker`: optional: Ergebnis von assemble_chunks (sonst wird intern erzeugt)
Rückgabe: Liste aus Payload-Dicts, jedes mit mind.:
- note_id, chunk_id, index, ord (Alias), title, type, path, text, retriever_weight, chunk_profile
- optional: tags (aus Note-FM), chunk_num, Chunk_Nummer (Aliases von index/ord)
"""
n = _as_dict(note)
path_arg, types_cfg_explicit = _pick_args(*args, **kwargs)
types_cfg = _load_types_config(types_cfg_explicit)
fm = n.get("frontmatter") or {}
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 {}
types_cfg = _load_types_config(types_cfg)
try:
default_rw = float(os.environ.get("MINDNET_DEFAULT_RETRIEVER_WEIGHT", 1.0))
except Exception:
default_rw = 1.0
cfg_for_type = types_cfg.get(note_type, {}) if isinstance(types_cfg, dict) else {}
default_rw = _env_float("MINDNET_DEFAULT_RETRIEVER_WEIGHT", 1.0)
retriever_weight = _coalesce(fm.get("retriever_weight"), cfg_for_type.get("retriever_weight"), default_rw)
try:
@ -148,10 +98,17 @@ def make_chunk_payloads(note: Any, *args, **kwargs) -> List[Dict[str, Any]]:
path = n.get("path") or path_arg
if isinstance(path, pathlib.Path):
path = str(path)
path = path or "" # immer vorhanden
path = path or "" # garantiert vorhanden
fulltext = _text_from_note(n, path)
chunks = _iter_chunks(n, chunk_profile, fulltext)
# Denormalisierte Tags (optional): auf Chunks spiegeln, wenn vorhanden
tags = fm.get("tags") or fm.get("keywords") or n.get("tags")
tags_list = _ensure_list(tags) if tags else []
# Quelltext
fulltext = note_text if isinstance(note_text, str) else _text_from_note(n, path)
# Chunks besorgen
chunks = chunks_from_chunker if isinstance(chunks_from_chunker, list) else _iter_chunks(n, chunk_profile, fulltext)
payloads: List[Dict[str, Any]] = []
for c in chunks:
@ -159,6 +116,7 @@ def make_chunk_payloads(note: Any, *args, **kwargs) -> List[Dict[str, Any]]:
text = c.get("text") if isinstance(c, dict) else (str(c) if c is not None else "")
text = text if isinstance(text, str) else str(text or "")
# deterministische ID (unter Beibehaltung deines bisherigen Schemas)
key = f"{note_id}|{idx}"
h = hashlib.sha1(key.encode("utf-8")).hexdigest()[:12] if note_id else hashlib.sha1(f"{path}|{idx}".encode("utf-8")).hexdigest()[:12]
chunk_id = f"{note_id}-{idx:03d}-{h}" if note_id else f"{h}"
@ -167,13 +125,20 @@ def make_chunk_payloads(note: Any, *args, **kwargs) -> List[Dict[str, Any]]:
"note_id": note_id,
"chunk_id": chunk_id,
"index": idx,
"ord": idx, # Alias für v2Schema
"chunk_num": idx, # neutraler Alias
"Chunk_Nummer": idx, # deutschsprachiger Alias (zur FilterKompatibilität)
"title": title,
"type": note_type,
"path": path, # <- garantiert vorhanden
"text": text, # <- nie leer, sonst werden keine Chunks erzeugt
"path": path, # garantiert vorhanden
"text": text, # nie leer, sonst kein Chunk
"retriever_weight": retriever_weight,
"chunk_profile": chunk_profile,
}
if tags_list:
payload["tags"] = tags_list
# JSONRoundtrip als einfache Validierung
json.loads(json.dumps(payload, ensure_ascii=False))
payloads.append(payload)