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474 lines
17 KiB
Python
474 lines
17 KiB
Python
"""
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FILE: app/core/chunker.py
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DESCRIPTION: Zerlegt Texte in Chunks (Sliding Window oder nach Headings).
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Orchestriert die Smart-Edge-Allocation via SemanticAnalyzer.
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FIX V3: Support für mehrzeilige Callouts und Section-Propagation.
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VERSION: 3.1.0 (Full Compatibility Merge)
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import List, Dict, Optional, Tuple, Any, Set
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import re
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import math
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import yaml
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from pathlib import Path
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import asyncio
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import logging
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# Services
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from app.services.semantic_analyzer import get_semantic_analyzer
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# Core Imports
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# Wir importieren build_edges_for_note nur, um kompatibel zur Signatur zu bleiben
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# oder für den Fallback.
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try:
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from app.core.derive_edges import build_edges_for_note
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except ImportError:
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# Mock für Tests
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def build_edges_for_note(note_id, chunks, note_level_references=None, include_note_scope_refs=False): return []
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logger = logging.getLogger(__name__)
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# ==========================================
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# 1. HELPER & CONFIG
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# ==========================================
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BASE_DIR = Path(__file__).resolve().parent.parent.parent
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CONFIG_PATH = BASE_DIR / "config" / "types.yaml"
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# Fallback Default, falls types.yaml fehlt
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DEFAULT_PROFILE = {"strategy": "sliding_window", "target": 400, "max": 600, "overlap": (50, 80)}
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_CONFIG_CACHE = None
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def _load_yaml_config() -> Dict[str, Any]:
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global _CONFIG_CACHE
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if _CONFIG_CACHE is not None: return _CONFIG_CACHE
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if not CONFIG_PATH.exists(): return {}
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try:
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with open(CONFIG_PATH, "r", encoding="utf-8") as f:
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data = yaml.safe_load(f)
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_CONFIG_CACHE = data
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return data
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except Exception: return {}
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def get_chunk_config(note_type: str) -> Dict[str, Any]:
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"""
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Lädt die Chunking-Strategie basierend auf dem Note-Type aus types.yaml.
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Dies sichert die Kompatibilität zu WP-15 (Profile).
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"""
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full_config = _load_yaml_config()
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profiles = full_config.get("chunking_profiles", {})
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type_def = full_config.get("types", {}).get(note_type.lower(), {})
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# Welches Profil nutzt dieser Typ? (z.B. 'sliding_smart_edges')
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profile_name = type_def.get("chunking_profile")
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if not profile_name:
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profile_name = full_config.get("defaults", {}).get("chunking_profile", "sliding_standard")
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config = profiles.get(profile_name, DEFAULT_PROFILE).copy()
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# Tupel-Konvertierung für Overlap (YAML liest oft Listen)
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if "overlap" in config and isinstance(config["overlap"], list):
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config["overlap"] = tuple(config["overlap"])
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return config
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def extract_frontmatter_from_text(md_text: str) -> Tuple[Dict[str, Any], str]:
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fm_match = re.match(r'^\s*---\s*\n(.*?)\n---', md_text, re.DOTALL)
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if not fm_match: return {}, md_text
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try:
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frontmatter = yaml.safe_load(fm_match.group(1))
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if not isinstance(frontmatter, dict): frontmatter = {}
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except yaml.YAMLError:
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frontmatter = {}
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text_without_fm = re.sub(r'^\s*---\s*\n(.*?)\n---', '', md_text, flags=re.DOTALL)
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return frontmatter, text_without_fm.strip()
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# ==========================================
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# 2. DATA CLASSES & TEXT TOOLS
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# ==========================================
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_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])'); _WS = re.compile(r'\s+')
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def estimate_tokens(text: str) -> int:
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return max(1, math.ceil(len(text.strip()) / 4))
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def split_sentences(text: str) -> list[str]:
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text = _WS.sub(' ', text.strip())
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if not text: return []
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parts = _SENT_SPLIT.split(text)
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return [p.strip() for p in parts if p.strip()]
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@dataclass
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class RawBlock:
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kind: str; text: str; level: Optional[int]; section_path: str; section_title: Optional[str]
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@dataclass
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class Chunk:
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id: str; note_id: str; index: int; text: str; window: str; token_count: int
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section_title: Optional[str]; section_path: str
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neighbors_prev: Optional[str]; neighbors_next: Optional[str]
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suggested_edges: Optional[List[str]] = None
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# ==========================================
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# 3. PARSING & STRATEGIES
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# ==========================================
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def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
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"""
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Zerlegt Text in logische Blöcke (Absätze, Header).
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Wichtig für die Strategie 'by_heading'.
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"""
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blocks = []
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h1_title = "Dokument"
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section_path = "/"
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current_h2 = None
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fm, text_without_fm = extract_frontmatter_from_text(md_text)
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h1_match = re.search(r'^#\s+(.*)', text_without_fm, re.MULTILINE)
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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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if stripped.startswith('# '):
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continue
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elif stripped.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_h2))
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buffer = []
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current_h2 = stripped[3:].strip()
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section_path = f"/{current_h2}"
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blocks.append(RawBlock("heading", stripped, 2, section_path, current_h2))
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elif not stripped:
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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_h2))
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buffer = []
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else:
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buffer.append(line)
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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_h2))
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return blocks, h1_title
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def _strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "", context_prefix: str = "") -> List[Chunk]:
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"""
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Die Standard-Strategie aus WP-15.
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Fasst Blöcke zusammen und schneidet bei 'target' Tokens (mit Satz-Rücksicht).
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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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overlap_val = config.get("overlap", (50, 80))
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overlap = sum(overlap_val) // 2 if isinstance(overlap_val, tuple) else overlap_val
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chunks = []; buf = []
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def _create_chunk(txt, win, sec, path):
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idx = len(chunks)
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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=estimate_tokens(txt),
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section_title=sec, section_path=path, neighbors_prev=None, neighbors_next=None,
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suggested_edges=[]
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))
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def flush_buffer():
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nonlocal buf
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if not buf: return
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text_body = "\n\n".join([b.text for b in buf])
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sec_title = buf[-1].section_title if buf else None
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sec_path = buf[-1].section_path if buf else "/"
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# Context Prefix (z.B. H1) voranstellen für Embedding-Qualität
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win_body = f"{context_prefix}\n{text_body}".strip() if context_prefix else text_body
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if estimate_tokens(text_body) <= max_tokens:
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_create_chunk(text_body, win_body, sec_title, sec_path)
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else:
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# Zu groß -> Satzweiser Split
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sentences = split_sentences(text_body)
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current_chunk_sents = []
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current_len = 0
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for sent in sentences:
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sent_len = estimate_tokens(sent)
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if current_len + sent_len > target and current_chunk_sents:
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c_txt = " ".join(current_chunk_sents)
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c_win = f"{context_prefix}\n{c_txt}".strip() if context_prefix else c_txt
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_create_chunk(c_txt, c_win, sec_title, sec_path)
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# Overlap für nächsten Chunk
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overlap_sents = []
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ov_len = 0
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for s in reversed(current_chunk_sents):
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if ov_len + estimate_tokens(s) < overlap:
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overlap_sents.insert(0, s)
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ov_len += estimate_tokens(s)
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else:
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break
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current_chunk_sents = list(overlap_sents)
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current_chunk_sents.append(sent)
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current_len = ov_len + sent_len
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else:
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current_chunk_sents.append(sent)
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current_len += sent_len
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# Rest
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if current_chunk_sents:
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c_txt = " ".join(current_chunk_sents)
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c_win = f"{context_prefix}\n{c_txt}".strip() if context_prefix else c_txt
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_create_chunk(c_txt, c_win, sec_title, sec_path)
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buf = []
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for b in blocks:
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if b.kind == "heading": continue
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current_buf_text = "\n\n".join([x.text for x in buf])
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if estimate_tokens(current_buf_text) + estimate_tokens(b.text) >= target:
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flush_buffer()
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buf.append(b)
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if estimate_tokens(b.text) >= target:
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flush_buffer()
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flush_buffer()
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return chunks
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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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Strategie für strukturierte Daten (Profile, Werte).
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Nutzt sliding_window, forciert aber Schnitte an Headings (via parse_blocks Vorarbeit).
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"""
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return _strategy_sliding_window(blocks, config, note_id, doc_title, context_prefix=f"# {doc_title}")
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# ==========================================
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# 4. ROBUST EDGE PARSING & PROPAGATION (NEU)
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# ==========================================
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def _parse_edges_robust(text: str) -> Set[str]:
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"""
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NEU: Findet Kanten im Text, auch wenn sie mehrzeilig oder 'kaputt' formatiert sind.
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Erkennt:
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> [!edge] type
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> [[Link]]
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Returns: Set von Strings "kind:target"
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"""
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found_edges = set()
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# A. Inline [[rel:type|target]] (Standard)
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inlines = re.findall(r'\[\[rel:([^\|\]]+)\|?([^\]]*)\]\]', text)
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for kind, target in inlines:
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k = kind.strip()
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t = target.strip()
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if k and t: found_edges.add(f"{k}:{t}")
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# B. Multiline Callouts Parsing (Der Fix für dein Problem)
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lines = text.split('\n')
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current_edge_type = None
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for line in lines:
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stripped = line.strip()
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# 1. Start Blockquote: > [!edge] type
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# (Erlaubt optionalen Doppelpunkt)
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callout_match = re.match(r'>\s*\[!edge\]\s*([^:\s]+)', stripped)
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if callout_match:
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current_edge_type = callout_match.group(1).strip()
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# Check: Sind Links noch in der GLEICHEN Zeile?
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links = re.findall(r'\[\[([^\]]+)\]\]', stripped)
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for l in links:
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if "rel:" not in l:
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found_edges.add(f"{current_edge_type}:{l}")
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continue
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# 2. Continuation Line: > [[Target]]
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# Wenn wir noch im 'edge mode' sind und die Zeile ein Zitat ist
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if current_edge_type and stripped.startswith('>'):
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links = re.findall(r'\[\[([^\]]+)\]\]', stripped)
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for l in links:
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if "rel:" not in l:
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found_edges.add(f"{current_edge_type}:{l}")
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# 3. End of Blockquote (kein '>') -> Reset Type
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elif not stripped.startswith('>'):
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current_edge_type = None
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return found_edges
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def _propagate_section_edges(chunks: List[Chunk]) -> List[Chunk]:
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"""
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NEU: Verteilt Kanten innerhalb einer Sektion.
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Löst das Problem: Callout steht oben im Kapitel, gilt aber für alle Chunks darunter.
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"""
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# Step 1: Sammeln pro Sektion
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section_map = {} # path -> set(kind:target)
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for ch in chunks:
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# Root-Level "/" ignorieren wir meist, da zu global
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if not ch.section_path or ch.section_path == "/": continue
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edges = _parse_edges_robust(ch.text)
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if edges:
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if ch.section_path not in section_map:
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section_map[ch.section_path] = set()
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section_map[ch.section_path].update(edges)
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# Step 2: Injizieren (Broadcasting)
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for ch in chunks:
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if ch.section_path in section_map:
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edges_to_add = section_map[ch.section_path]
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if not edges_to_add: continue
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injections = []
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for e_str in edges_to_add:
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kind, target = e_str.split(':', 1)
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# Check: Kante schon im Text?
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token = f"[[rel:{kind}|{target}]]"
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if token not in ch.text:
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injections.append(token)
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if injections:
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# Wir schreiben die Kanten "hart" in den Text.
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# Damit findet sie derive_edges.py später garantiert.
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block = "\n\n\n" + " ".join(injections)
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ch.text += block
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# Auch ins Window schreiben für Embedding-Kontext
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ch.window += block
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return chunks
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# ==========================================
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# 5. ORCHESTRATION (ASYNC)
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# ==========================================
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async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Optional[Dict] = None) -> List[Chunk]:
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"""
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Hauptfunktion. Verbindet Parsing, Splitting und Edge-Allocation.
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"""
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# 1. Config laden (WP-15 Kompatibilität)
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if config is None:
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config = get_chunk_config(note_type)
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fm, body_text = extract_frontmatter_from_text(md_text)
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note_status = fm.get("status", "").lower()
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primary_strategy = config.get("strategy", "sliding_window")
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enable_smart_edges = config.get("enable_smart_edge_allocation", False)
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# Drafts skippen LLM um Kosten/Zeit zu sparen
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if enable_smart_edges and note_status in ["draft", "initial_gen"]:
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logger.info(f"Chunker: Skipping Smart Edges for draft '{note_id}'.")
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enable_smart_edges = False
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# 2. Parsing & Splitting
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blocks, doc_title = parse_blocks(md_text)
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if primary_strategy == "by_heading":
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chunks = await asyncio.to_thread(_strategy_by_heading, blocks, config, note_id, doc_title)
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else:
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chunks = await asyncio.to_thread(_strategy_sliding_window, blocks, config, note_id, doc_title)
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if not chunks:
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return []
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# 3. NEU: Propagation VOR Smart Edge Allocation
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# Das repariert die fehlenden Kanten aus deinen Callouts.
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chunks = _propagate_section_edges(chunks)
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# 4. Smart Edges (LLM)
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if enable_smart_edges:
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chunks = await _run_smart_edge_allocation(chunks, md_text, note_id, note_type)
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# 5. Linking
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for i, ch in enumerate(chunks):
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ch.neighbors_prev = chunks[i-1].id if i > 0 else None
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ch.neighbors_next = chunks[i+1].id if i < len(chunks)-1 else None
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return chunks
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def _extract_all_edges_from_md(md_text: str, note_id: str, note_type: str) -> List[str]:
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"""
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Hilfsfunktion: Sammelt ALLE Kanten für den LLM-Kandidaten-Pool.
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"""
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# A. Via derive_edges (Standard)
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dummy_chunk = {
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"chunk_id": f"{note_id}#full",
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"text": md_text,
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"content": md_text,
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"window": md_text,
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"type": note_type
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}
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# Signatur-Anpassung beachten (WP-15 Fix)
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raw_edges = build_edges_for_note(
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note_id,
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[dummy_chunk],
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note_level_references=None,
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include_note_scope_refs=False
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)
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all_candidates = set()
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for e in raw_edges:
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kind = e.get("kind")
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target = e.get("target_id")
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if target and kind not in ["belongs_to", "next", "prev", "backlink"]:
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all_candidates.add(f"{kind}:{target}")
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# B. Via Robust Parser (NEU) - fängt die multiline Callouts
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robust_edges = _parse_edges_robust(md_text)
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all_candidates.update(robust_edges)
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return list(all_candidates)
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async def _run_smart_edge_allocation(chunks: List[Chunk], full_text: str, note_id: str, note_type: str) -> List[Chunk]:
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"""
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Der LLM-Schritt (WP-15). Filtert irrelevante Kanten.
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"""
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analyzer = get_semantic_analyzer()
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candidate_list = _extract_all_edges_from_md(full_text, note_id, note_type)
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if not candidate_list:
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return chunks
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tasks = []
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for chunk in chunks:
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tasks.append(analyzer.assign_edges_to_chunk(chunk.text, candidate_list, note_type))
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results_per_chunk = await asyncio.gather(*tasks)
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assigned_edges_global = set()
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for i, confirmed_edges in enumerate(results_per_chunk):
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chunk = chunks[i]
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chunk.suggested_edges = confirmed_edges
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assigned_edges_global.update(confirmed_edges)
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if confirmed_edges:
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# Wir schreiben auch Smart Edges hart in den Text
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injection_str = "\n" + " ".join([f"[[rel:{e.split(':')[0]}|{e.split(':')[1]}]]" for e in confirmed_edges if ':' in e])
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chunk.text += injection_str
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chunk.window += injection_str
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# Fallback für Kanten, die das LLM nirgendwo zugeordnet hat
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# (Damit nichts verloren geht -> Safety Fallback)
|
|
unassigned = set(candidate_list) - assigned_edges_global
|
|
if unassigned:
|
|
fallback_str = "\n" + " ".join([f"[[rel:{e.split(':')[0]}|{e.split(':')[1]}]]" for e in unassigned if ':' in e])
|
|
for chunk in chunks:
|
|
chunk.text += fallback_str
|
|
chunk.window += fallback_str
|
|
if chunk.suggested_edges is None: chunk.suggested_edges = []
|
|
chunk.suggested_edges.extend(list(unassigned))
|
|
|
|
return chunks |