mindnet/app/core/chunker.py

from __future__ import annotations
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple, Any, Set
import re
import math
import yaml
from pathlib import Path
from markdown_it import MarkdownIt
from markdown_it.token import Token
import asyncio 
import logging

# Services
from app.services.semantic_analyzer import get_semantic_analyzer

# Core Imports
try:
    from app.core.derive_edges import build_edges_for_note
except ImportError:
    # Mock für Tests
    def build_edges_for_note(md_text, note_id, note_type, chunks=[], references=[]): return []

logger = logging.getLogger(__name__)

# ==========================================
# 1. HELPER & CONFIG
# ==========================================

BASE_DIR = Path(__file__).resolve().parent.parent.parent
CONFIG_PATH = BASE_DIR / "config" / "types.yaml"
DEFAULT_PROFILE = {"strategy": "sliding_window", "target": 400, "max": 600, "overlap": (50, 80)}
_CONFIG_CACHE = None

def _load_yaml_config() -> Dict[str, Any]:
    global _CONFIG_CACHE
    if _CONFIG_CACHE is not None: return _CONFIG_CACHE
    if not CONFIG_PATH.exists(): return {}
    try:
        with open(CONFIG_PATH, "r", encoding="utf-8") as f: 
            data = yaml.safe_load(f)
            _CONFIG_CACHE = data
            return data
    except Exception: return {}

def get_chunk_config(note_type: str) -> Dict[str, Any]:
    full_config = _load_yaml_config()
    profiles = full_config.get("chunking_profiles", {})
    type_def = full_config.get("types", {}).get(note_type.lower(), {})
    profile_name = type_def.get("chunking_profile")
    if not profile_name: 
        profile_name = full_config.get("defaults", {}).get("chunking_profile", "sliding_standard")
    
    config = profiles.get(profile_name, DEFAULT_PROFILE).copy()
    if "overlap" in config and isinstance(config["overlap"], list): 
        config["overlap"] = tuple(config["overlap"])
    return config

def extract_frontmatter_from_text(md_text: str) -> Tuple[Dict[str, Any], str]:
    fm_match = re.match(r'^\s*---\s*\n(.*?)\n---', md_text, re.DOTALL)
    if not fm_match: return {}, md_text
    try:
        frontmatter = yaml.safe_load(fm_match.group(1))
        if not isinstance(frontmatter, dict): frontmatter = {}
    except yaml.YAMLError:
        frontmatter = {}
    text_without_fm = re.sub(r'^\s*---\s*\n(.*?)\n---', '', md_text, flags=re.DOTALL)
    return frontmatter, text_without_fm.strip()

# ==========================================
# 2. DATA CLASSES
# ==========================================

_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])')
_WS = re.compile(r'\s+')

def estimate_tokens(text: str) -> int:
    return max(1, math.ceil(len(text.strip()) / 4))

def split_sentences(text: str) -> list[str]:
    text = _WS.sub(' ', text.strip())
    if not text: return []
    parts = _SENT_SPLIT.split(text)
    return [p.strip() for p in parts if p.strip()]

@dataclass
class RawBlock:
    kind: str; text: str; level: Optional[int]; section_path: str; section_title: Optional[str]

@dataclass
class Chunk:
    id: str; note_id: str; index: int; text: str; window: str; token_count: int
    section_title: Optional[str]; section_path: str
    neighbors_prev: Optional[str]; neighbors_next: Optional[str]
    suggested_edges: Optional[List[str]] = None 

# ==========================================
# 3. PARSING & STRATEGIES (SYNCHRON)
# ==========================================

def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
    """
    Zerlegt Text in logische Blöcke (Absätze, Header).
    Verbesserte Version: Splittet auch reine Absätze.
    """
    blocks = []
    h1_title = "Dokument"
    section_path = "/"
    current_h2 = None
    
    fm, text_without_fm = extract_frontmatter_from_text(md_text)
    
    # H1 suchen
    h1_match = re.search(r'^#\s+(.*)', text_without_fm, re.MULTILINE)
    if h1_match: 
        h1_title = h1_match.group(1).strip()

    # Rudimentäres Parsing (Markdown-It ist komplex einzubinden ohne vollen Visitor)
    # Wir splitten hier einfach an Doppel-Newlines für Paragraphen, wenn keine Header da sind.
    
    # Zuerst Header-Struktur bewahren
    lines = text_without_fm.split('\n')
    buffer = []
    
    for line in lines:
        stripped = line.strip()
        if stripped.startswith('# '): # H1 ignorieren wir im Body meist
            continue 
        elif stripped.startswith('## '):
            # Flush buffer
            if buffer:
                content = "\n".join(buffer).strip()
                if content:
                    blocks.append(RawBlock("paragraph", content, None, section_path, current_h2))
                buffer = []
            current_h2 = stripped[3:].strip()
            section_path = f"/{current_h2}"
            blocks.append(RawBlock("heading", stripped, 2, section_path, current_h2))
        elif not stripped:
            # Leere Zeile -> Absatzende
            if buffer:
                content = "\n".join(buffer).strip()
                if content:
                    blocks.append(RawBlock("paragraph", content, None, section_path, current_h2))
                buffer = []
        else:
            buffer.append(line)
            
    if buffer:
        content = "\n".join(buffer).strip()
        if content:
            blocks.append(RawBlock("paragraph", content, None, section_path, current_h2))
            
    return blocks, h1_title

def _strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "", context_prefix: str = "") -> List[Chunk]:
    target = config.get("target", 400)
    max_tokens = config.get("max", 600)
    overlap_val = config.get("overlap", (50, 80))
    overlap = sum(overlap_val) // 2 if isinstance(overlap_val, tuple) else overlap_val
    
    chunks = []
    buf = [] # Buffer für Blöcke

    def _create_chunk(txt, win, sec, path):
        idx = len(chunks)
        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=sec, section_path=path, neighbors_prev=None, neighbors_next=None,
            suggested_edges=[]
        ))

    def flush_buffer():
        nonlocal buf
        if not buf: return
        
        # Kombiniere Blöcke im Buffer
        text_body = "\n\n".join([b.text for b in buf])
        sec_title = buf[-1].section_title if buf else None
        sec_path = buf[-1].section_path if buf else "/"
        
        # Check Größe
        if estimate_tokens(text_body) <= max_tokens:
            win_body = f"{context_prefix}\n{text_body}".strip() if context_prefix else text_body
            _create_chunk(text_body, win_body, sec_title, sec_path)
        else:
            # Text ist zu groß -> Splitte nach Sätzen
            sentences = split_sentences(text_body)
            current_chunk_sents = []
            current_len = 0
            
            for sent in sentences:
                sent_len = estimate_tokens(sent)
                if current_len + sent_len > target and current_chunk_sents:
                    # Chunk abschließen
                    c_txt = " ".join(current_chunk_sents)
                    c_win = f"{context_prefix}\n{c_txt}".strip() if context_prefix else c_txt
                    _create_chunk(c_txt, c_win, sec_title, sec_path)
                    
                    # Overlap für nächsten Chunk
                    # Wir nehmen die letzten Sätze, die in den Overlap passen
                    overlap_sents = []
                    ov_len = 0
                    for s in reversed(current_chunk_sents):
                        if ov_len + estimate_tokens(s) < overlap:
                            overlap_sents.insert(0, s)
                            ov_len += estimate_tokens(s)
                        else:
                            break
                    
                    current_chunk_sents = list(overlap_sents)
                    current_chunk_sents.append(sent)
                    current_len = ov_len + sent_len
                else:
                    current_chunk_sents.append(sent)
                    current_len += sent_len
            
            # Rest verarbeiten
            if current_chunk_sents:
                c_txt = " ".join(current_chunk_sents)
                c_win = f"{context_prefix}\n{c_txt}".strip() if context_prefix else c_txt
                _create_chunk(c_txt, c_win, sec_title, sec_path)

        buf = []

    for b in blocks:
        if b.kind == "heading": continue # Header nicht direkt in Text mischen, dienen nur Struktur
        
        # Wenn Buffer + neuer Block zu groß -> Flush
        current_buf_text = "\n\n".join([x.text for x in buf])
        if estimate_tokens(current_buf_text) + estimate_tokens(b.text) >= target:
            flush_buffer()
        
        buf.append(b)
        
        # Wenn der Block selbst riesig ist (größer als Target), sofort flushen und splitten
        if estimate_tokens(b.text) >= target:
            flush_buffer()

    flush_buffer()
    return chunks

def _strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
    return _strategy_sliding_window(blocks, config, note_id, doc_title, context_prefix=f"# {doc_title}")

# ==========================================
# 4. ORCHESTRATION (ASYNC) - WP-15 CORE
# ==========================================

async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Optional[Dict] = None) -> List[Chunk]:
    if config is None:
        config = get_chunk_config(note_type)
        
    fm, body_text = extract_frontmatter_from_text(md_text)
    note_status = fm.get("status", "").lower()
    
    primary_strategy = config.get("strategy", "sliding_window")
    enable_smart_edges = config.get("enable_smart_edge_allocation", False)

    if enable_smart_edges and note_status in ["draft", "initial_gen"]:
        logger.info(f"Chunker: Skipping Smart Edges for draft '{note_id}'.")
        enable_smart_edges = False

    blocks, doc_title = parse_blocks(md_text)
    
    if primary_strategy == "by_heading":
        chunks = await asyncio.to_thread(_strategy_by_heading, blocks, config, note_id, doc_title)
    else:
        chunks = await asyncio.to_thread(_strategy_sliding_window, blocks, config, note_id, doc_title)

    if not chunks:
        return []

    if enable_smart_edges:
        chunks = await _run_smart_edge_allocation(chunks, md_text, note_id, note_type)

    for i, ch in enumerate(chunks):
        ch.neighbors_prev = chunks[i-1].id if i > 0 else None
        ch.neighbors_next = chunks[i+1].id if i < len(chunks)-1 else None

    return chunks

async def _run_smart_edge_allocation(chunks: List[Chunk], full_text: str, note_id: str, note_type: str) -> List[Chunk]:
    analyzer = get_semantic_analyzer()
    
    # FIX: Positional Argument für text übergeben, um TypeError zu vermeiden
    raw_edges = build_edges_for_note(
        full_text,
        note_id=note_id,
        note_type=note_type,
        chunks=[], 
        references=[] # Falls die Signatur references erwartet
    )
    
    all_candidates = set()
    # Robustheit: raw_edges könnte None sein, falls der Mock schlecht ist
    if raw_edges:
        for e in raw_edges:
            if e.get("target_id") and e.get("kind") not in ["next", "prev", "belongs_to"]:
                all_candidates.add(f"{e['kind']}:{e['target_id']}")
    
    candidate_list = list(all_candidates)
    
    if not candidate_list:
        return chunks

    tasks = []
    for chunk in chunks:
        tasks.append(analyzer.assign_edges_to_chunk(chunk.text, candidate_list, note_type))
    
    results_per_chunk = await asyncio.gather(*tasks)
    
    assigned_edges_global = set()
    
    for i, confirmed_edges in enumerate(results_per_chunk):
        chunk = chunks[i]
        chunk.suggested_edges = confirmed_edges
        assigned_edges_global.update(confirmed_edges)
        
        if confirmed_edges:
            injection_str = "\n" + " ".join([f"[[rel:{e.split(':')[0]}|{e.split(':')[1]}]]" for e in confirmed_edges if ':' in e])
            chunk.text += injection_str
            chunk.window += injection_str

    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