Verbesserung des Chunking-Parsers zur Unterstützung atomarer Blöcke und Gewährleistung der strukturellen Integrität von Callouts. Aktualisierung der Beschreibung und Optimierung der Satz- und Blockverarbeitung, einschließlich präziserer Handhabung von H1-Überschriften und Trennern.
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"""
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FILE: app/core/chunking/chunking_parser.py
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DESCRIPTION: Zerlegt Markdown in logische Blöcke (RawBlocks).
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Gewährleistet, dass H1 und Trenner im Stream verbleiben.
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DESCRIPTION: Zerlegt Markdown in atomare Blöcke. Hält H1-Überschriften im Stream
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und gewährleistet die strukturelle Integrität von Callouts.
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"""
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import re
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from typing import List, Tuple, Set
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from .chunking_models import RawBlock
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from .chunking_utils import extract_frontmatter_from_text
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_WS = re.compile(r'\s+')
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_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])')
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def split_sentences(text: str) -> list[str]:
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"""Teilt Text in Sätze auf unter Berücksichtigung deutscher Interpunktion."""
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text = _WS.sub(' ', text.strip())
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text = re.sub(r'\s+', ' ', text.strip())
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if not text: return []
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return [p.strip() for p in _SENT_SPLIT.split(text) if p.strip()]
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# Splittet bei Satzzeichen, gefolgt von Leerzeichen und Großbuchstaben
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sentences = re.split(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])', text)
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return [s.strip() for s in sentences if s.strip()]
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def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
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"""Zerlegt Text in logische Einheiten (RawBlocks), inklusive H1-H6."""
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blocks = []
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h1_title = "Dokument"; section_path = "/"; current_section_title = None
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h1_title = "Dokument"
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section_path = "/"
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current_section_title = None
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# Frontmatter entfernen
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fm, text_without_fm = extract_frontmatter_from_text(md_text)
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# H1 für Metadaten extrahieren
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# H1 für Note-Titel extrahieren
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h1_match = re.search(r'^#\s+(.*)', text_without_fm, re.MULTILINE)
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if h1_match: h1_title = h1_match.group(1).strip()
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if h1_match:
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h1_title = h1_match.group(1).strip()
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lines = text_without_fm.split('\n')
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buffer = []
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for line in lines:
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stripped = line.strip()
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heading_match = re.match(r'^(#{1,6})\s+(.*)', stripped)
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# Heading-Erkennung (H1 bis H6)
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heading_match = re.match(r'^(#{1,6})\s+(.*)', stripped)
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if heading_match:
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if buffer:
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content = "\n".join(buffer).strip()
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@ -52,14 +57,18 @@ def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
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blocks.append(RawBlock("heading", stripped, level, section_path, current_section_title))
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continue
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if (not stripped or stripped == "---") and not line.startswith('>'):
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# Trenner (---) beenden Blöcke, Leerzeilen nur wenn nicht in Callout
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if stripped == "---" and not line.startswith('>'):
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if buffer:
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content = "\n".join(buffer).strip()
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if content:
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blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
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if content: blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
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buffer = []
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if stripped == "---":
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blocks.append(RawBlock("separator", "---", None, section_path, current_section_title))
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elif not stripped and not line.startswith('>'):
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if buffer:
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content = "\n".join(buffer).strip()
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if content: blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
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buffer = []
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else:
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buffer.append(line)
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@ -70,7 +79,7 @@ def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
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return blocks, h1_title
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def parse_edges_robust(text: str) -> Set[str]:
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"""Extrahiert Kanten aus Wikilinks und Callouts."""
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"""Extrahiert Kanten-Kandidaten aus Wikilinks und Callouts."""
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found_edges = set()
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inlines = re.findall(r'\[\[rel:([^\|\]]+)\|?([^\]]*)\]\]', text)
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for kind, target in inlines:
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"""
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FILE: app/core/chunking/chunking_strategies.py
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DESCRIPTION: Universelle Strategie für atomares Sektions-Chunking v3.6.0.
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Garantiert Sektions-Integrität durch präventives Chunk-Management.
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DESCRIPTION: Strategie für atomares Sektions-Chunking v3.7.0.
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Garantiert Sektions-Integrität durch ein flexibles Toleranz-Limit.
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Kein Splitting von Sektionen, solange sie 'ungefähr' passen.
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"""
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import math
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from typing import List, Dict, Any, Optional
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from .chunking_models import RawBlock, Chunk
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from .chunking_parser import split_sentences
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def _accurate_estimate_tokens(text: str) -> int:
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"""Konservative Schätzung für deutschen Text (len/2.5 statt len/4)."""
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return max(1, math.ceil(len(text.strip()) / 2.5))
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# Toleranz-Faktor: Erlaubt Chunks, bis zu 15% über 'max' zu wachsen,
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# um eine Sektion vollständig zu erhalten.
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FLEX_FACTOR = 1.15
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def _safe_estimate(text: str) -> int:
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"""Sicherere Token-Schätzung für MD/Deutsch (Faktor 3.0 statt 4.0)."""
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return max(1, math.ceil(len(text.strip()) / 3.0))
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def _create_context_win(doc_title: str, sec_title: Optional[str], text: str) -> str:
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parts = []
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if doc_title: parts.append(doc_title)
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if sec_title and sec_title != doc_title: parts.append(sec_title)
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prefix = " > ".join(parts)
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return f"{prefix}\n{text}".strip() if prefix else text
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prefix = " > ".join(parts); return f"{prefix}\n{text}".strip() if prefix else text
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def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
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"""
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Sektions-Chunking: Packt komplette Abschnitte in Chunks.
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Bei Überlauf wird die Sektion ohne Ausnahme in den nächsten Chunk geschoben.
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"""
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target = config.get("target", 400)
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max_tokens = config.get("max", 600)
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split_level = config.get("split_level", 2)
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overlap_cfg = config.get("overlap", (50, 80))
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overlap = sum(overlap_cfg) // 2 if isinstance(overlap_cfg, (list, tuple)) else overlap_cfg
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# Das flexible Maximum, das Sektionen unzertrennt lässt
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soft_max = int(max_tokens * FLEX_FACTOR)
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chunks: List[Chunk] = []
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def _emit_chunk(block_list: List[RawBlock]):
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"""Schreibt eine Liste von Blöcken als einen einzigen, ungeteilten Chunk."""
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"""Schreibt eine Liste von Blöcken als einen einzigen Chunk ohne internes Splitting."""
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if not block_list: return
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txt = "\n\n".join([b.text for b in block_list])
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idx = len(chunks)
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@ -42,40 +45,36 @@ def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id:
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win = _create_context_win(doc_title, title, txt)
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chunks.append(Chunk(
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id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx,
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text=txt, window=win, token_count=_accurate_estimate_tokens(txt),
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text=txt, window=win, token_count=_safe_estimate(txt),
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section_title=title, section_path=path,
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neighbors_prev=None, neighbors_next=None
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))
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def _split_giant_section(sec_blocks: List[RawBlock]):
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"""Notfall-Split: Nur wenn eine EINZELNE Sektion bereits > max ist."""
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"""Notfall-Split: Nur wenn eine EINZELNE Sektion bereits > soft_max ist."""
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full_text = "\n\n".join([b.text for b in sec_blocks])
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main_title = sec_blocks[0].section_title
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main_path = sec_blocks[0].section_path
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main_title = sec_blocks[0].section_title; main_path = sec_blocks[0].section_path
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header_text = sec_blocks[0].text if sec_blocks[0].kind == "heading" else ""
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sents = split_sentences(full_text)
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cur_sents = []; sub_len = 0
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for s in sents:
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slen = _accurate_estimate_tokens(s)
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slen = _safe_estimate(s)
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if sub_len + slen > target and cur_sents:
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_emit_chunk([RawBlock("paragraph", " ".join(cur_sents), None, main_path, main_title)])
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ov_s = [header_text] if header_text else []
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ov_l = _accurate_estimate_tokens(header_text) if header_text else 0
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ov_l = _safe_estimate(header_text) if header_text else 0
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for os in reversed(cur_sents):
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if os == header_text: continue
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t_len = _accurate_estimate_tokens(os)
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t_len = _safe_estimate(os)
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if ov_l + t_len < overlap:
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ov_s.insert(len(ov_s)-1 if header_text else 0, os)
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ov_l += t_len
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ov_s.insert(len(ov_s)-1 if header_text else 0, os); ov_l += t_len
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else: break
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cur_sents = list(ov_s); cur_sents.append(s); sub_len = ov_l + slen
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else: cur_sents.append(s); sub_len += slen
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if cur_sents: _emit_chunk([RawBlock("paragraph", " ".join(cur_sents), None, main_path, main_title)])
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# 1. Gruppierung in atomare Einheiten
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# 1. Gruppierung in atomare Sektions-Einheiten
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sections: List[List[RawBlock]] = []
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curr_sec: List[RawBlock] = []
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for b in blocks:
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else: curr_sec.append(b)
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if curr_sec: sections.append(curr_sec)
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# 2. Das Pack-Verfahren (Kein Zerschneiden beim Flashen!)
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candidate_chunk: List[RawBlock] = []
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candidate_tokens = 0
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# 2. Das flexible Pack-Verfahren
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current_chunk_buf: List[RawBlock] = []
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current_tokens = 0
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for sec in sections:
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sec_text = "\n\n".join([b.text for b in sec])
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sec_tokens = _accurate_estimate_tokens(sec_text)
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sec_tokens = _safe_estimate(sec_text)
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# Prüfung: Passt die Sektion noch dazu?
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if candidate_tokens + sec_tokens <= max_tokens:
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candidate_chunk.extend(sec)
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candidate_tokens = _accurate_estimate_tokens("\n\n".join([b.text for b in candidate_chunk]))
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else:
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# Chunk ist voll -> Abschluss an Sektionsgrenze
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if candidate_chunk:
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_emit_chunk(candidate_chunk)
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candidate_chunk = []
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candidate_tokens = 0
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if current_chunk_buf:
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# PRÜFUNG: Würde die neue Sektion das FLEXIBLE Limit sprengen?
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if (current_tokens + sec_tokens > soft_max):
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_emit_chunk(current_chunk_buf)
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current_chunk_buf = []
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current_tokens = 0
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# Haben wir das Ziel-Maß erreicht und es kommt eine neue Sektion?
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elif (current_tokens >= target):
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_emit_chunk(current_chunk_buf)
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current_chunk_buf = []
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current_tokens = 0
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# Neue Sektion allein prüfen
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if sec_tokens > max_tokens:
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# Wenn eine EINZELNE Sektion alleine schon das weiche Limit sprengt
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if not current_chunk_buf and sec_tokens > soft_max:
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_split_giant_section(sec)
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else:
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candidate_chunk = list(sec)
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candidate_tokens = sec_tokens
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current_chunk_buf.extend(sec)
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current_tokens = _safe_estimate("\n\n".join([b.text for b in current_chunk_buf]))
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if current_chunk_buf:
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_emit_chunk(current_chunk_buf)
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if candidate_chunk: _emit_chunk(candidate_chunk)
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return chunks
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def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, context_prefix: str = "") -> List[Chunk]:
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target = config.get("target", 400); max_tokens = config.get("max", 600)
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chunks: List[Chunk] = []; buf: List[RawBlock] = []
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for b in blocks:
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b_tokens = _accurate_estimate_tokens(b.text)
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current_tokens = sum(_accurate_estimate_tokens(x.text) for x in buf) if buf else 0
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b_tokens = _safe_estimate(b.text)
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current_tokens = sum(_safe_estimate(x.text) for x in buf) if buf else 0
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if current_tokens + b_tokens > max_tokens and buf:
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txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
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win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
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@ -129,5 +131,5 @@ def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note
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if buf:
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txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
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win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
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chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=_accurate_estimate_tokens(txt), section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
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chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=_safe_estimate(txt), section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
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return chunks
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