394 lines
14 KiB
Python
394 lines
14 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import List, Dict, Optional, Tuple, Any
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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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from markdown_it import MarkdownIt
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from markdown_it.token import Token
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import asyncio
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# NEUE IMPORTS
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try:
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from app.services.semantic_analyzer import SemanticAnalyzer, SemanticChunkResult
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except ImportError:
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# Fallback für Tests
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print("WARNUNG: SemanticAnalyzer Service nicht gefunden. Semantic Chunking wird fehlschlagen.")
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class SemanticAnalyzer:
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async def analyze_and_chunk(self, text, type): return [SemanticChunkResult(content=text, suggested_edges=[])]
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@dataclass
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class SemanticChunkResult:
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content: str
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suggested_edges: List[str]
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# ==========================================
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# 1. FUNKTION ZUM AUSLESEN DES FRONTMATTERS (Lokalisiert und stabil)
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# ==========================================
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def extract_frontmatter_from_text(md_text: str) -> Tuple[Dict[str, Any], str]:
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"""
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Extrakte das YAML Frontmatter aus dem Markdown-Text und gibt den Body zurück.
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"""
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fm_match = re.match(r'^---\s*\n(.*?)\n---', md_text, re.DOTALL)
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if not fm_match:
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return {}, md_text
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frontmatter_yaml = fm_match.group(1)
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try:
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# Nutzung von safe_load
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frontmatter = yaml.safe_load(frontmatter_yaml)
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if not isinstance(frontmatter, dict):
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frontmatter = {}
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except yaml.YAMLError:
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frontmatter = {}
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# Entferne den Frontmatter Block aus dem Text
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text_without_fm = re.sub(r'^---\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. CONFIGURATION LOADER
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# ==========================================
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# Pfad-Logik: app/core/chunker.py -> app/core -> app -> root/config/types.yaml
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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 Values
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DEFAULT_PROFILE = {
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"strategy": "sliding_window",
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"target": 400,
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"max": 600,
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"overlap": (50, 80)
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}
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_CONFIG_CACHE = None
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def _load_yaml_config() -> Dict[str, Any]:
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"""Lädt die config/types.yaml und cached das Ergebnis."""
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global _CONFIG_CACHE
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if _CONFIG_CACHE is not None:
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return _CONFIG_CACHE
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if not CONFIG_PATH.exists():
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print(f"WARNUNG: types.yaml nicht gefunden unter: {CONFIG_PATH}")
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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 as e:
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print(f"FEHLER beim Laden von {CONFIG_PATH}: {e}")
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return {}
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def get_chunk_config(note_type: str) -> Dict[str, Any]:
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"""Löst Typ -> Profil -> Konfiguration auf."""
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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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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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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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# Legacy Support
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def get_sizes(note_type: str):
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cfg = get_chunk_config(note_type)
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return {
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"target": (cfg["target"], cfg["target"]),
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"max": cfg["max"],
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"overlap": cfg["overlap"]
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}
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# ==========================================
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# 3. DATA CLASSES & HELPERS
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# ==========================================
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# --- Hilfen ---
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_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])')
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_WS = re.compile(r'\s+')
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def estimate_tokens(text: str) -> int:
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t = len(text.strip())
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return max(1, math.ceil(t / 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
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text: str
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level: Optional[int]
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section_path: str
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section_title: Optional[str]
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@dataclass
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class Chunk:
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id: str
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note_id: str
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index: int
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text: str # Reintext für Anzeige (inkl. injizierter Links bei LLM/Heading)
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window: str # Text + Context für Embeddings
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token_count: int
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section_title: Optional[str]
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section_path: str
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neighbors_prev: Optional[str]
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neighbors_next: Optional[str]
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char_start: int
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char_end: int
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# --- Markdown Parser ---
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def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
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"""Parst MD und gibt Blöcke UND den H1 Titel zurück."""
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md = MarkdownIt("commonmark").enable("table")
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tokens: List[Token] = md.parse(md_text)
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blocks: List[RawBlock] = []
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h1_title = "Dokument"
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h2, h3 = None, None
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section_path = "/"
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# Rudimentäres Block-Parsing für non-LLM Strategien
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fm, text_without_fm = extract_frontmatter_from_text(md_text)
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if text_without_fm.strip():
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blocks.append(RawBlock(kind="paragraph", text=text_without_fm.strip(),
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level=None, section_path=section_path, section_title=h2))
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# H1 Titel Extraktion (für Context Injection in by_heading)
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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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return blocks, h1_title
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# ==========================================
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# 4. STRATEGIES (SYNCHRON)
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# ==========================================
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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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"""Klassisches Sliding Window."""
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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: List[Chunk] = []
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buf: List[RawBlock] = []
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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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window_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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sentences = split_sentences(text_body)
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current_sents = []
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cur_toks = 0
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for s in sentences:
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st = estimate_tokens(s)
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if cur_toks + st > target and current_sents:
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txt = "\n".join(current_sents)
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win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
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_add_chunk(txt, win, sec_title, sec_path)
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ov_txt = " ".join(current_sents)[-overlap*4:]
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current_sents = [ov_txt, s] if ov_txt else [s]
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cur_toks = estimate_tokens(" ".join(current_sents))
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else:
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current_sents.append(s)
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cur_toks += st
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if current_sents:
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txt = "\n".join(current_sents)
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win = f"{context_prefix}\n{txt}".strip() if context_prefix else txt
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_add_chunk(txt, win, sec_title, sec_path)
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else:
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_add_chunk(text_body, window_body, sec_title, sec_path)
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buf = []
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def _add_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,
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neighbors_prev=None, neighbors_next=None, char_start=0, char_end=0
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))
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for b in blocks:
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if estimate_tokens("\n\n".join([x.text for x in buf] + [b.text])) >= target:
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flush_buffer()
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buf.append(b)
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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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"""Harter Split an Überschriften mit Context Injection."""
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chunks: List[Chunk] = []
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sections: Dict[str, List[RawBlock]] = {}
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ordered = []
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for b in blocks:
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if b.kind == "heading": continue
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if b.section_path not in sections:
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sections[b.section_path] = []
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ordered.append(b.section_path)
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sections[b.section_path].append(b)
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for path in ordered:
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s_blocks = sections[path]
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if not s_blocks: continue
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breadcrumbs = path.strip("/").replace("/", " > ")
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context_header = f"# {doc_title}\n## {breadcrumbs}"
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full_text = "\n\n".join([b.text for b in s_blocks])
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if estimate_tokens(full_text) <= config.get("max", 600):
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chunks.append(Chunk(
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id=f"{note_id}#c{len(chunks):02d}", note_id=note_id, index=len(chunks),
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text=full_text, window=f"{context_header}\n{full_text}",
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token_count=estimate_tokens(full_text),
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section_title=s_blocks[0].section_title if s_blocks else None,
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section_path=path,
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neighbors_prev=None, neighbors_next=None, char_start=0, char_end=0
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))
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else:
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# Fallback auf Sliding Window mit Context Injection
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sub = _strategy_sliding_window(s_blocks, config, note_id, context_prefix=context_header)
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base = len(chunks)
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for i, sc in enumerate(sub):
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sc.index = base + i
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sc.id = f"{note_id}#c{sc.index:02d}"
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chunks.append(sc)
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return chunks
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# ==========================================
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# 5. STRATEGY (ASYNCHRON)
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# ==========================================
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# Singleton Instanz für den Analyzer
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_semantic_analyzer_instance = None
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def _get_semantic_analyzer_instance() -> SemanticAnalyzer:
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"""Liefert die Singleton-Instanz des SemanticAnalyzer."""
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global _semantic_analyzer_instance
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if _semantic_analyzer_instance is None:
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_semantic_analyzer_instance = SemanticAnalyzer()
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return _semantic_analyzer_instance
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async def _strategy_semantic_llm(md_text: str, config: Dict[str, Any], note_id: str, note_type: str) -> List[Chunk]:
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"""
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Strategie: Delegiert die Zerlegung und Kanten-Extraktion an ein LLM (Async).
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"""
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analyzer = _get_semantic_analyzer_instance()
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# Text-Splitting wird hier vom LLM übernommen
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semantic_chunks: List[SemanticChunkResult] = await analyzer.analyze_and_chunk(md_text, note_type)
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chunks: List[Chunk] = []
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for i, sc in enumerate(semantic_chunks):
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# 1. Edge Injection für derive_edges.py
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injection_block = "\n"
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for edge_str in sc.suggested_edges:
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# Stellt sicher, dass das Split-Ergebnis 2 Teile hat
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if ":" in edge_str:
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kind, target = edge_str.split(":", 1)
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# Nutzt die Syntax: [[rel:kind | Target]]
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injection_block += f"[[rel:{kind} | {target}]] "
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full_text = sc.content + injection_block
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# 2. Chunk Objekt bauen
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chunks.append(Chunk(
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id=f"{note_id}#sem{i:02d}",
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note_id=note_id,
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index=i,
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text=full_text.strip(),
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window=full_text.strip(),
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token_count=estimate_tokens(full_text),
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section_title="Semantic Section",
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section_path="/LLM",
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neighbors_prev=None, neighbors_next=None,
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char_start=0, char_end=0
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))
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return chunks
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# ==========================================
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# 6. MAIN ENTRY POINT (ASYNC)
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# ==========================================
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async def assemble_chunks(note_id: str, md_text: str, note_type: str) -> List[Chunk]:
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"""
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Hauptfunktion. Analysiert Config und wählt Strategie (MUSS ASYNC SEIN).
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Enthält die Logik zur Vermeidung des Double-LLM-Effekts.
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"""
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# 1. Frontmatter prüfen (Double-LLM-Prevention)
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# Nutzen der lokalen, robusten Funktion
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fm, body = extract_frontmatter_from_text(md_text)
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note_status = fm.get("status", "").lower()
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config = get_chunk_config(note_type)
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strategy = config.get("strategy", "sliding_window")
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# 2. Strategie-Auswahl
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# Wenn der Typ LLM-Chunking nutzt (semantic_llm),
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# ABER der Status ist 'draft' (wahrscheinlich vom LLM generiert):
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if strategy == "semantic_llm" and note_status in ["draft", "initial_gen"]:
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# Setze auf die zweitbeste, aber synchrone und deterministische Strategie
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print(f"INFO: Overriding '{strategy}' for draft status. Using 'by_heading' instead.")
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strategy = "by_heading"
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# 3. Execution (Dispatcher)
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# Der Text, der an die Chunker-Strategie geht.
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# Da extract_frontmatter_from_text den Frontmatter entfernt hat,
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# ist der Body der saubere Text. md_text enthält ihn noch für non-Frontmatter-Logik.
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md_to_chunk = md_text
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if strategy == "semantic_llm":
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# LLM-Strategie nutzt den gesamten MD-Text zur Orientierung
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chunks = await _strategy_semantic_llm(md_to_chunk, config, note_id, note_type)
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elif strategy == "by_heading":
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blocks, doc_title = parse_blocks(md_to_chunk)
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# Synchronen Code in einem Thread ausführen
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chunks = await asyncio.to_thread(_strategy_by_heading, blocks, config, note_id, doc_title)
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else: # sliding_window (Default)
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blocks, doc_title = parse_blocks(md_to_chunk)
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# Synchronen Code in einem Thread ausführen
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chunks = await asyncio.to_thread(_strategy_sliding_window, blocks, config, note_id)
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# 4. Post-Process: Neighbors setzen
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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 |