feat(embedding): implement legal-aware chunking pipeline
Replace plain recursive chunker with legal-aware chunking that: - Detects legal section headers (§, Art., Section, Chapter, Annex) - Adds section context prefix to every chunk - Splits on paragraph boundaries then sentence boundaries - Protects DE + EN abbreviations (80+ patterns) from false splits - Supports language detection for locale-specific processing - Force-splits overlong sentences at word boundaries The old plain_recursive API option is removed — all non-semantic strategies now route through chunk_text_legal(). Includes 40 tests covering header detection, abbreviation protection, sentence splitting, and legal chunking behavior. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -251,14 +251,251 @@ async def rerank_cohere(query: str, documents: List[str], top_k: int = 5) -> Lis
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GERMAN_ABBREVIATIONS = {
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'bzw', 'ca', 'chr', 'd.h', 'dr', 'etc', 'evtl', 'ggf', 'inkl', 'max',
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'min', 'mio', 'mrd', 'nr', 'prof', 's', 'sog', 'u.a', 'u.ä', 'usw',
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'v.a', 'vgl', 'vs', 'z.b', 'z.t', 'zzgl'
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'v.a', 'vgl', 'vs', 'z.b', 'z.t', 'zzgl', 'abs', 'art', 'abschn',
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'anh', 'anl', 'aufl', 'bd', 'bes', 'bzgl', 'dgl', 'einschl', 'entspr',
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'erg', 'erl', 'gem', 'grds', 'hrsg', 'insb', 'ivm', 'kap', 'lit',
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'nachf', 'rdnr', 'rn', 'rz', 'ua', 'uvm', 'vorst', 'ziff'
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}
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# English abbreviations that don't end sentences
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ENGLISH_ABBREVIATIONS = {
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'e.g', 'i.e', 'etc', 'vs', 'al', 'approx', 'avg', 'dept', 'dr', 'ed',
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'est', 'fig', 'govt', 'inc', 'jr', 'ltd', 'max', 'min', 'mr', 'mrs',
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'ms', 'no', 'prof', 'pt', 'ref', 'rev', 'sec', 'sgt', 'sr', 'st',
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'vol', 'cf', 'ch', 'cl', 'col', 'corp', 'cpl', 'def', 'dist', 'div',
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'gen', 'hon', 'illus', 'intl', 'natl', 'org', 'para', 'pp', 'repr',
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'resp', 'supp', 'tech', 'temp', 'treas', 'univ'
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}
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# Combined abbreviations for both languages
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ALL_ABBREVIATIONS = GERMAN_ABBREVIATIONS | ENGLISH_ABBREVIATIONS
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# Regex pattern for legal section headers (§, Art., Article, Section, etc.)
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import re
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_LEGAL_SECTION_RE = re.compile(
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r'^(?:'
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r'§\s*\d+' # § 25, § 5a
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r'|Art(?:ikel|icle|\.)\s*\d+' # Artikel 5, Article 12, Art. 3
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r'|Section\s+\d+' # Section 4.2
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r'|Abschnitt\s+\d+' # Abschnitt III
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r'|Kapitel\s+\d+' # Kapitel 2
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r'|Chapter\s+\d+' # Chapter 3
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r'|Anhang\s+[IVXLC\d]+' # Anhang III
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r'|Annex\s+[IVXLC\d]+' # Annex XII
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r'|TEIL\s+[IVXLC\d]+' # TEIL II
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r'|Part\s+[IVXLC\d]+' # Part III
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r'|Recital\s+\d+' # Recital 42
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r'|Erwaegungsgrund\s+\d+' # Erwaegungsgrund 26
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r')',
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re.IGNORECASE | re.MULTILINE
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)
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# Regex for any heading-like line (Markdown ## or ALL-CAPS line)
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_HEADING_RE = re.compile(
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r'^(?:'
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r'#{1,6}\s+.+' # Markdown headings
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r'|[A-ZÄÖÜ][A-ZÄÖÜ\s\-]{5,}$' # ALL-CAPS lines (>5 chars)
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r')',
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re.MULTILINE
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)
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def _detect_language(text: str) -> str:
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"""Simple heuristic: count German vs English marker words."""
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sample = text[:5000].lower()
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de_markers = sum(1 for w in ['der', 'die', 'das', 'und', 'ist', 'für', 'von',
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'werden', 'nach', 'gemäß', 'sowie', 'durch']
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if f' {w} ' in sample)
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en_markers = sum(1 for w in ['the', 'and', 'for', 'that', 'with', 'shall',
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'must', 'should', 'which', 'from', 'this']
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if f' {w} ' in sample)
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return 'de' if de_markers > en_markers else 'en'
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def _protect_abbreviations(text: str) -> str:
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"""Replace dots in abbreviations with placeholders to prevent false sentence splits."""
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protected = text
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for abbrev in ALL_ABBREVIATIONS:
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pattern = re.compile(r'\b(' + re.escape(abbrev) + r')\.', re.IGNORECASE)
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# Use lambda to preserve original case of the matched abbreviation
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protected = pattern.sub(lambda m: m.group(1).replace('.', '<DOT>') + '<ABBR>', protected)
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# Protect decimals (3.14) and ordinals (1. Absatz)
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protected = re.sub(r'(\d)\.(\d)', r'\1<DECIMAL>\2', protected)
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protected = re.sub(r'(\d+)\.\s', r'\1<ORD> ', protected)
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return protected
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def _restore_abbreviations(text: str) -> str:
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"""Restore placeholders back to dots."""
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return (text
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.replace('<DOT>', '.')
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.replace('<ABBR>', '.')
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.replace('<DECIMAL>', '.')
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.replace('<ORD>', '.'))
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def _split_sentences(text: str) -> List[str]:
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"""Split text into sentences, respecting abbreviations in DE and EN."""
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protected = _protect_abbreviations(text)
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# Split after sentence-ending punctuation followed by uppercase or newline
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sentence_pattern = r'(?<=[.!?])\s+(?=[A-ZÄÖÜÀ-Ý])|(?<=[.!?])\s*\n'
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raw = re.split(sentence_pattern, protected)
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sentences = []
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for s in raw:
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s = _restore_abbreviations(s).strip()
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if s:
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sentences.append(s)
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return sentences
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def _extract_section_header(line: str) -> Optional[str]:
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"""Extract a legal section header from a line, or None."""
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m = _LEGAL_SECTION_RE.match(line.strip())
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if m:
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return line.strip()
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m = _HEADING_RE.match(line.strip())
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if m:
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return line.strip()
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return None
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def chunk_text_legal(text: str, chunk_size: int, overlap: int) -> List[str]:
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"""
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Legal-document-aware chunking.
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Strategy:
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1. Split on legal section boundaries (§, Art., Section, Chapter, etc.)
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2. Within each section, split on paragraph boundaries (double newline)
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3. Within each paragraph, split on sentence boundaries
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4. Prepend section header as context prefix to every chunk
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5. Add overlap from previous chunk
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Works for both German (DSGVO, BGB, AI Act DE) and English (NIST, SLSA, CRA EN) texts.
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"""
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if not text or len(text) <= chunk_size:
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return [text.strip()] if text and text.strip() else []
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# --- Phase 1: Split into sections by legal headers ---
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lines = text.split('\n')
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sections = [] # list of (header, content)
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current_header = None
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current_lines = []
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for line in lines:
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header = _extract_section_header(line)
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if header and current_lines:
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sections.append((current_header, '\n'.join(current_lines)))
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current_header = header
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current_lines = [line]
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elif header and not current_lines:
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current_header = header
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current_lines = [line]
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else:
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current_lines.append(line)
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if current_lines:
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sections.append((current_header, '\n'.join(current_lines)))
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# --- Phase 2: Within each section, split on paragraphs, then sentences ---
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raw_chunks = []
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for section_header, section_text in sections:
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# Build context prefix (max 120 chars to leave room for content)
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prefix = ""
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if section_header:
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truncated = section_header[:120]
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prefix = f"[{truncated}] "
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paragraphs = re.split(r'\n\s*\n', section_text)
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current_chunk = prefix
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current_length = len(prefix)
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for para in paragraphs:
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para = para.strip()
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if not para:
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continue
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# If paragraph fits in remaining space, append
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if current_length + len(para) + 1 <= chunk_size:
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if current_chunk and not current_chunk.endswith(' '):
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current_chunk += '\n\n'
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current_chunk += para
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current_length = len(current_chunk)
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continue
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# Paragraph doesn't fit — flush current chunk if non-empty
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if current_chunk.strip() and current_chunk.strip() != prefix.strip():
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raw_chunks.append(current_chunk.strip())
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# If entire paragraph fits in a fresh chunk, start new chunk
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if len(prefix) + len(para) <= chunk_size:
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current_chunk = prefix + para
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current_length = len(current_chunk)
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continue
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# Paragraph too long — split by sentences
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sentences = _split_sentences(para)
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current_chunk = prefix
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current_length = len(prefix)
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for sentence in sentences:
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sentence_len = len(sentence)
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# Single sentence exceeds chunk_size — force-split
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if len(prefix) + sentence_len > chunk_size:
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if current_chunk.strip() and current_chunk.strip() != prefix.strip():
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raw_chunks.append(current_chunk.strip())
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# Hard split the long sentence
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remaining = sentence
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while remaining:
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take = chunk_size - len(prefix)
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chunk_part = prefix + remaining[:take]
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raw_chunks.append(chunk_part.strip())
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remaining = remaining[take:]
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current_chunk = prefix
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current_length = len(prefix)
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continue
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if current_length + sentence_len + 1 > chunk_size:
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if current_chunk.strip() and current_chunk.strip() != prefix.strip():
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raw_chunks.append(current_chunk.strip())
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current_chunk = prefix + sentence
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current_length = len(current_chunk)
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else:
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if current_chunk and not current_chunk.endswith(' '):
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current_chunk += ' '
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current_chunk += sentence
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current_length = len(current_chunk)
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# Flush remaining content for this section
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if current_chunk.strip() and current_chunk.strip() != prefix.strip():
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raw_chunks.append(current_chunk.strip())
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if not raw_chunks:
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return [text.strip()] if text.strip() else []
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# --- Phase 3: Add overlap ---
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final_chunks = []
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for i, chunk in enumerate(raw_chunks):
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if i > 0 and overlap > 0:
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prev = raw_chunks[i - 1]
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# Take overlap from end of previous chunk (but not the prefix)
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overlap_text = prev[-min(overlap, len(prev)):]
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# Only add overlap if it doesn't start mid-word
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space_idx = overlap_text.find(' ')
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if space_idx > 0:
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overlap_text = overlap_text[space_idx + 1:]
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if overlap_text:
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chunk = overlap_text + ' ' + chunk
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final_chunks.append(chunk.strip())
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return [c for c in final_chunks if c]
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def chunk_text_recursive(text: str, chunk_size: int, overlap: int) -> List[str]:
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"""Recursive character-based chunking."""
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import re
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"""Recursive character-based chunking (legacy, use legal_recursive for legal docs)."""
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if not text or len(text) <= chunk_size:
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return [text] if text else []
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@@ -315,36 +552,23 @@ def chunk_text_recursive(text: str, chunk_size: int, overlap: int) -> List[str]:
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def chunk_text_semantic(text: str, chunk_size: int, overlap_sentences: int = 1) -> List[str]:
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"""Semantic sentence-aware chunking."""
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import re
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if not text:
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return []
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if len(text) <= chunk_size:
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return [text.strip()]
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# Split into sentences (simplified for German)
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text = re.sub(r'\s+', ' ', text).strip()
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# Protect abbreviations
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protected = text
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for abbrev in GERMAN_ABBREVIATIONS:
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pattern = re.compile(r'\b' + re.escape(abbrev) + r'\.', re.IGNORECASE)
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protected = pattern.sub(abbrev.replace('.', '<DOT>') + '<ABBR>', protected)
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# Protect decimals and ordinals
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protected = re.sub(r'(\d)\.(\d)', r'\1<DECIMAL>\2', protected)
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protected = re.sub(r'(\d+)\.(\s)', r'\1<ORD>\2', protected)
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protected = _protect_abbreviations(text)
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# Split on sentence endings
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sentence_pattern = r'(?<=[.!?])\s+(?=[A-ZÄÖÜ])|(?<=[.!?])$'
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sentence_pattern = r'(?<=[.!?])\s+(?=[A-ZÄÖÜÀ-Ý])|(?<=[.!?])$'
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raw_sentences = re.split(sentence_pattern, protected)
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# Restore protected characters
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sentences = []
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for s in raw_sentences:
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s = s.replace('<DOT>', '.').replace('<ABBR>', '.').replace('<DECIMAL>', '.').replace('<ORD>', '.')
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s = s.strip()
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s = _restore_abbreviations(s).strip()
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if s:
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sentences.append(s)
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@@ -638,7 +862,16 @@ async def rerank_documents(request: RerankRequest):
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@app.post("/chunk", response_model=ChunkResponse)
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async def chunk_text(request: ChunkRequest):
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"""Chunk text into smaller pieces."""
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"""Chunk text into smaller pieces.
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Strategies:
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- "recursive" (default): Legal-document-aware chunking with §/Art./Section
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boundary detection, section context headers, paragraph-level splitting,
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and sentence-level splitting respecting DE + EN abbreviations.
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- "semantic": Sentence-aware chunking with overlap by sentence count.
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The old plain recursive chunker has been retired and is no longer available.
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"""
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if not request.text:
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return ChunkResponse(chunks=[], count=0, strategy=request.strategy)
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@@ -647,7 +880,9 @@ async def chunk_text(request: ChunkRequest):
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overlap_sentences = max(1, request.overlap // 100)
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chunks = chunk_text_semantic(request.text, request.chunk_size, overlap_sentences)
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else:
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chunks = chunk_text_recursive(request.text, request.chunk_size, request.overlap)
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# All strategies (recursive, legal_recursive, etc.) use the legal-aware chunker.
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# The old plain recursive chunker is no longer exposed via the API.
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chunks = chunk_text_legal(request.text, request.chunk_size, request.overlap)
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return ChunkResponse(
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chunks=chunks,
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