WP4d #16

Merged
Lars merged 33 commits from WP4d into main 2025-12-30 12:25:33 +01:00
2 changed files with 108 additions and 106 deletions
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@ -1,6 +1,6 @@
"""
FILE: app/core/chunking/chunking_parser.py
DESCRIPTION: Zerlegt Markdown in atomare Blöcke. Hält H1-Überschriften im Stream
DESCRIPTION: Zerlegt Markdown in logische Blöcke. Hält H1-Überschriften im Stream
und gewährleistet die strukturelle Integrität von Callouts.
"""
import re
@ -8,28 +8,25 @@ from typing import List, Tuple, Set
from .chunking_models import RawBlock
from .chunking_utils import extract_frontmatter_from_text
_WS = re.compile(r'\s+')
_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])')
def split_sentences(text: str) -> list[str]:
"""Teilt Text in Sätze auf unter Berücksichtigung deutscher Interpunktion."""
text = re.sub(r'\s+', ' ', text.strip())
text = _WS.sub(' ', text.strip())
if not text: return []
# Splittet bei Satzzeichen, gefolgt von Leerzeichen und Großbuchstaben
sentences = re.split(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])', text)
return [s.strip() for s in sentences if s.strip()]
# Splittet bei Punkt, Ausrufezeichen oder Fragezeichen, gefolgt von Großbuchstabe
return [p.strip() for p in _SENT_SPLIT.split(text) if p.strip()]
def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
"""Zerlegt Text in logische Einheiten (RawBlocks), inklusive H1-H6."""
blocks = []
h1_title = "Dokument"
section_path = "/"
current_section_title = None
# Frontmatter entfernen
h1_title = "Dokument"; section_path = "/"; current_section_title = None
fm, text_without_fm = extract_frontmatter_from_text(md_text)
# H1 für Note-Titel extrahieren
h1_match = re.search(r'^#\s+(.*)', text_without_fm, re.MULTILINE)
if h1_match:
h1_title = h1_match.group(1).strip()
if h1_match: h1_title = h1_match.group(1).strip()
lines = text_without_fm.split('\n')
buffer = []
@ -57,18 +54,15 @@ def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
blocks.append(RawBlock("heading", stripped, level, section_path, current_section_title))
continue
# Trenner (---) beenden Blöcke, Leerzeilen nur wenn nicht in Callout
if stripped == "---" and not line.startswith('>'):
# Trenner oder Leerzeilen beenden Blöcke, außer innerhalb von Callouts
if (not stripped or stripped == "---") and not line.startswith('>'):
if buffer:
content = "\n".join(buffer).strip()
if content: blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
if content:
blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
buffer = []
if stripped == "---":
blocks.append(RawBlock("separator", "---", None, section_path, current_section_title))
elif not stripped and not line.startswith('>'):
if buffer:
content = "\n".join(buffer).strip()
if content: blocks.append(RawBlock("paragraph", content, None, section_path, current_section_title))
buffer = []
else:
buffer.append(line)

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@ -1,135 +1,143 @@
"""
FILE: app/core/chunking/chunking_strategies.py
DESCRIPTION: Strategie für atomares Sektions-Chunking v3.7.0.
Garantiert Sektions-Integrität durch ein flexibles Toleranz-Limit.
Kein Splitting von Sektionen, solange sie 'ungefähr' passen.
DESCRIPTION: Strategien für atomares Sektions-Chunking v3.8.5.
Implementiert das 'Pack-and-Carry-Over' Verfahren:
1. Packt ganze Abschnitte basierend auf Schätzung.
2. Kein physischer Overflow-Check während des Packens.
3. Smart-Zerlegung von Übergrößen mit Carry-Over in die Queue.
"""
import math
from typing import List, Dict, Any, Optional
from .chunking_models import RawBlock, Chunk
from .chunking_utils import estimate_tokens
from .chunking_parser import split_sentences
# Toleranz-Faktor: Erlaubt Chunks, bis zu 15% über 'max' zu wachsen,
# um eine Sektion vollständig zu erhalten.
FLEX_FACTOR = 1.15
def _safe_estimate(text: str) -> int:
"""Sicherere Token-Schätzung für MD/Deutsch (Faktor 3.0 statt 4.0)."""
return max(1, math.ceil(len(text.strip()) / 3.0))
def _create_context_win(doc_title: str, sec_title: Optional[str], text: str) -> str:
parts = []
if doc_title: parts.append(doc_title)
def _create_win(doc_title: str, sec_title: Optional[str], text: str) -> str:
parts = [doc_title] if doc_title else []
if sec_title and sec_title != doc_title: parts.append(sec_title)
prefix = " > ".join(parts); return f"{prefix}\n{text}".strip() if prefix else text
prefix = " > ".join(parts)
return f"{prefix}\n{text}".strip() if prefix else text
def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
"""
Universelle Heading-Strategie mit Fallunterscheidung für Smart-Edge-Allocation.
"""
smart_edge = config.get("enable_smart_edge_allocation", True)
target = config.get("target", 400)
max_tokens = config.get("max", 600)
split_level = config.get("split_level", 2)
overlap_cfg = config.get("overlap", (50, 80))
overlap = sum(overlap_cfg) // 2 if isinstance(overlap_cfg, (list, tuple)) else overlap_cfg
# Das flexible Maximum, das Sektionen unzertrennt lässt
soft_max = int(max_tokens * FLEX_FACTOR)
chunks: List[Chunk] = []
def _emit_chunk(block_list: List[RawBlock]):
"""Schreibt eine Liste von Blöcken als einen einzigen Chunk ohne internes Splitting."""
if not block_list: return
txt = "\n\n".join([b.text for b in block_list])
def _emit(txt, title, path):
idx = len(chunks)
title = block_list[0].section_title
path = block_list[0].section_path
win = _create_context_win(doc_title, title, txt)
win = _create_win(doc_title, title, txt)
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=title, section_path=path,
neighbors_prev=None, neighbors_next=None
text=txt, window=win, token_count=estimate_tokens(txt),
section_title=title, section_path=path, neighbors_prev=None, neighbors_next=None
))
def _split_giant_section(sec_blocks: List[RawBlock]):
"""Notfall-Split: Nur wenn eine EINZELNE Sektion bereits > soft_max ist."""
full_text = "\n\n".join([b.text for b in sec_blocks])
main_title = sec_blocks[0].section_title; main_path = sec_blocks[0].section_path
header_text = sec_blocks[0].text if sec_blocks[0].kind == "heading" else ""
# --- FALL A: HARD SPLIT (enable_smart_edge_allocation: false) ---
if not smart_edge:
buf = []
for b in blocks:
# Trenne bei jeder Überschrift <= split_level
if b.kind == "heading" and b.level <= split_level:
# Nur flashen, wenn der Puffer nicht nur aus der aktuellen Überschrift besteht
if buf and not (len(buf) == 1 and buf[0].kind == "heading"):
_emit("\n\n".join([x.text for x in buf]), buf[0].section_title, buf[0].section_path)
buf = []
buf.append(b)
if buf: _emit("\n\n".join([x.text for x in buf]), buf[0].section_title, buf[0].section_path)
return chunks
sents = split_sentences(full_text)
cur_sents = []; sub_len = 0
for s in sents:
slen = _safe_estimate(s)
if sub_len + slen > target and cur_sents:
_emit_chunk([RawBlock("paragraph", " ".join(cur_sents), None, main_path, main_title)])
ov_s = [header_text] if header_text else []
ov_l = _safe_estimate(header_text) if header_text else 0
for os in reversed(cur_sents):
if os == header_text: continue
t_len = _safe_estimate(os)
if ov_l + t_len < overlap:
ov_s.insert(len(ov_s)-1 if header_text else 0, os); ov_l += t_len
else: break
cur_sents = list(ov_s); cur_sents.append(s); sub_len = ov_l + slen
else: cur_sents.append(s); sub_len += slen
if cur_sents: _emit_chunk([RawBlock("paragraph", " ".join(cur_sents), None, main_path, main_title)])
# 1. Gruppierung in atomare Sektions-Einheiten
# --- FALL B: SMART EDGE ALLOCATION (Pack-and-Carry-Over) ---
# 1. Gruppierung in atomare Sektions-Einheiten (Sektions-Isolation)
sections: List[List[RawBlock]] = []
curr_sec: List[RawBlock] = []
curr = []
for b in blocks:
if b.kind == "heading" and b.level <= split_level:
if curr_sec: sections.append(curr_sec)
curr_sec = [b]
else: curr_sec.append(b)
if curr_sec: sections.append(curr_sec)
if curr: sections.append(curr)
curr = [b]
else: curr.append(b)
if curr: sections.append(curr)
# 2. Das flexible Pack-Verfahren
current_chunk_buf: List[RawBlock] = []
current_tokens = 0
# 2. Queue-Management für Carry-Over
processing_queue = [{"blocks": s, "text": "\n\n".join([b.text for b in s])} for s in sections]
current_chunk_text = ""
current_meta = {"title": None, "path": "/"}
for sec in sections:
sec_text = "\n\n".join([b.text for b in sec])
sec_tokens = _safe_estimate(sec_text)
while processing_queue:
item = processing_queue.pop(0)
item_text = item["text"]
item_tokens = estimate_tokens(item_text)
if current_chunk_buf:
# PRÜFUNG: Würde die neue Sektion das FLEXIBLE Limit sprengen?
if (current_tokens + sec_tokens > soft_max):
_emit_chunk(current_chunk_buf)
current_chunk_buf = []
current_tokens = 0
# Haben wir das Ziel-Maß erreicht und es kommt eine neue Sektion?
elif (current_tokens >= target):
_emit_chunk(current_chunk_buf)
current_chunk_buf = []
current_tokens = 0
# Metadaten-Initialisierung falls Chunk leer
if not current_chunk_text and "blocks" in item:
current_meta["title"] = item["blocks"][0].section_title
current_meta["path"] = item["blocks"][0].section_path
# Wenn eine EINZELNE Sektion alleine schon das weiche Limit sprengt
if not current_chunk_buf and sec_tokens > soft_max:
_split_giant_section(sec)
combined_est = estimate_tokens(current_chunk_text + "\n\n" + item_text) if current_chunk_text else item_tokens
# Regel 1: Passt die vollständige Sektion nach Schätzung rein? (Kein harter Overflow-Check)
if combined_est <= max_tokens:
current_chunk_text = (current_chunk_text + "\n\n" + item_text).strip()
else:
current_chunk_buf.extend(sec)
current_tokens = _safe_estimate("\n\n".join([b.text for b in current_chunk_buf]))
# Regel 2: Wenn Puffer voll -> Emittieren und Sektion zurücklegen
if current_chunk_text:
_emit(current_chunk_text, current_meta["title"], current_meta["path"])
current_chunk_text = ""
processing_queue.insert(0, item)
else:
# Regel 3: Einzelne Sektion zu groß -> Smart Zerlegung
sents = split_sentences(item_text)
header_text = ""
if "blocks" in item and item["blocks"][0].kind == "heading":
header_text = item["blocks"][0].text
if current_chunk_buf:
_emit_chunk(current_chunk_buf)
take_sents = []; take_len = 0
while sents:
s = sents.pop(0)
slen = estimate_tokens(s)
if take_len + slen > target and take_sents:
sents.insert(0, s); break
take_sents.append(s); take_len += slen
_emit(" ".join(take_sents), current_meta["title"], current_meta["path"])
# Carry-Over: Der Rest wird an den Anfang der Queue geschoben
if sents:
remainder_text = " ".join(sents)
# Falls wir einen Header haben, fügen wir ihn dem Rest für den Kontext hinzu
if header_text and not remainder_text.startswith(header_text):
remainder_text = header_text + "\n\n" + remainder_text
processing_queue.insert(0, {"text": remainder_text, "is_split": True})
if current_chunk_text:
_emit(current_chunk_text, current_meta["title"], current_meta["path"])
return chunks
def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, context_prefix: str = "") -> List[Chunk]:
"""Sliding Window Strategie: Erhalten für alternative Anwendungsfälle."""
target = config.get("target", 400); max_tokens = config.get("max", 600)
chunks: List[Chunk] = []; buf: List[RawBlock] = []
for b in blocks:
b_tokens = _safe_estimate(b.text)
current_tokens = sum(_safe_estimate(x.text) for x in buf) if buf else 0
b_tokens = estimate_tokens(b.text)
current_tokens = sum(estimate_tokens(x.text) for x in buf) if buf else 0
if current_tokens + b_tokens > max_tokens and buf:
txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=current_tokens, section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
buf = []
buf.append(b)
if buf:
txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
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))
chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=estimate_tokens(txt), section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
return chunks