mindnet/app/core/ingestion.py

"""
FILE: app/core/ingestion.py
DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen.
             WP-20: Optimiert für OpenRouter (mistralai/mistral-7b-instruct:free).
             WP-22: Content Lifecycle, Edge Registry Validation & Multi-Hash.
FIX: Policy-Violation Detection & erzwungener Ollama-Fallback bei Cloud-Refusal.
     Dies löst das Problem leerer Kantenlisten bei umfangreichen Protokollen.
VERSION: 2.11.13
STATUS: Active
DEPENDENCIES: app.core.parser, app.core.note_payload, app.core.chunker, app.services.llm_service
"""
import os
import json
import re
import logging
import asyncio
import time
from typing import Dict, List, Optional, Tuple, Any

# Core Module Imports
from app.core.parser import (
    read_markdown,
    normalize_frontmatter,
    validate_required_frontmatter,
    extract_edges_with_context, 
)
from app.core.note_payload import make_note_payload
from app.core.chunker import assemble_chunks, get_chunk_config
from app.core.chunk_payload import make_chunk_payloads

# Fallback für Edges
try:
    from app.core.derive_edges import build_edges_for_note
except ImportError:
    def build_edges_for_note(*args, **kwargs): return []

from app.core.qdrant import QdrantConfig, get_client, ensure_collections, ensure_payload_indexes
from app.core.qdrant_points import (
    points_for_chunks,
    points_for_note,
    points_for_edges,
    upsert_batch,
)

from app.services.embeddings_client import EmbeddingsClient
from app.services.edge_registry import registry as edge_registry
from app.services.llm_service import LLMService 

logger = logging.getLogger(__name__)

# --- Global Helpers ---
def extract_json_from_response(text: str) -> Any:
    """
    Extrahiert JSON-Daten und bereinigt LLM-Steuerzeichen (Mistral/Llama).
    Entfernt <s>, [OUT], [/OUT] und Markdown-Blöcke für maximale Robustheit.
    """
    if not text or not isinstance(text, str): 
        return []
    
    # 1. Entferne Mistral/Llama Steuerzeichen und Tags (BOS/EOS)
    clean = text.replace("<s>", "").replace("</s>", "")
    clean = clean.replace("[OUT]", "").replace("[/OUT]", "")
    clean = clean.strip()
    
    # 2. Suche nach Markdown JSON-Blöcken (```json ... ```)
    match = re.search(r"```(?:json)?\s*(.*?)\s*```", clean, re.DOTALL)
    payload = match.group(1) if match else clean
    
    try:
        return json.loads(payload.strip())
    except json.JSONDecodeError:
        # 3. Recovery: Suche nach der ersten [ und letzten ] (Liste)
        start = payload.find('[')
        end = payload.rfind(']') + 1
        if start != -1 and end > start:
            try:
                return json.loads(payload[start:end])
            except: pass
        
        # 4. Zweite Recovery: Suche nach der ersten { und letzten } (Objekt)
        start_obj = payload.find('{')
        end_obj = payload.rfind('}') + 1
        if start_obj != -1 and end_obj > start_obj:
            try:
                return json.loads(payload[start_obj:end_obj])
            except: pass
            
    return []

def load_type_registry(custom_path: Optional[str] = None) -> dict:
    """Lädt die types.yaml zur Steuerung der typ-spezifischen Ingestion."""
    import yaml
    from app.config import get_settings
    settings = get_settings()
    path = custom_path or settings.MINDNET_TYPES_FILE
    if not os.path.exists(path): return {}
    try:
        with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {}
    except Exception: return {}

# --- Service Class ---
class IngestionService:
    def __init__(self, collection_prefix: str = None):
        from app.config import get_settings
        self.settings = get_settings()
        
        self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX
        self.cfg = QdrantConfig.from_env()
        self.cfg.prefix = self.prefix 
        self.client = get_client(self.cfg)
        self.dim = self.settings.VECTOR_SIZE
        self.registry = load_type_registry()
        self.embedder = EmbeddingsClient()
        self.llm = LLMService() 
        
        self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
        
        try:
            ensure_collections(self.client, self.prefix, self.dim)
            ensure_payload_indexes(self.client, self.prefix)
        except Exception as e:
            logger.warning(f"DB init warning: {e}")

    def _resolve_note_type(self, requested: Optional[str]) -> str:
        """Bestimmt den finalen Notiz-Typ (Fallback auf 'concept')."""
        types = self.registry.get("types", {})
        if requested and requested in types: return requested
        return "concept" 

    def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]:
        """Holt die Chunker-Parameter für ein spezifisches Profil aus der Registry."""
        profiles = self.registry.get("chunking_profiles", {})
        if profile_name in profiles:
            cfg = profiles[profile_name].copy()
            if "overlap" in cfg and isinstance(cfg["overlap"], list): 
                cfg["overlap"] = tuple(cfg["overlap"])
            return cfg
        return get_chunk_config(note_type)

    async def _perform_smart_edge_allocation(self, text: str, note_id: str) -> List[Dict]:
        """
        KI-Extraktion mit aktiver Erkennung von Cloud-Ablehnungen (Policy Violations).
        Erzwingt bei leeren Cloud-Antworten einen automatischen lokalen Ollama-Fallback.
        """
        provider = self.settings.MINDNET_LLM_PROVIDER
        model = self.settings.OPENROUTER_MODEL if provider == "openrouter" else self.settings.GEMINI_MODEL
        
        logger.info(f"🚀 [Ingestion] Turbo-Mode: Extracting edges for '{note_id}' using {model} on {provider}")
        
        edge_registry.ensure_latest()
        valid_types_str = ", ".join(sorted(list(edge_registry.valid_types)))
        
        template = self.llm.get_prompt("edge_extraction", provider)
        
        try:
            # Sicherheits-Check: Formatierung des Templates gegen KeyError schützen
            try:
                # Wir senden max 6000 Zeichen (ca. 1500 Token) an das LLM für die Extraktion
                prompt = template.format(
                    text=text[:6000], 
                    note_id=note_id,
                    valid_types=valid_types_str
                )
            except KeyError as ke:
                logger.error(f"❌ [Ingestion] Prompt-Template Fehler (Variable {ke} fehlt).")
                return []

            # Schritt 1: Anfrage an den primären Provider (Cloud)
            response_json = await self.llm.generate_raw_response(
                prompt=prompt, priority="background", force_json=True,
                provider=provider, model_override=model
            )
            
            # Nutzt den verbesserten Mistral-sicheren JSON-Extraktor
            raw_data = extract_json_from_response(response_json)
            
            # FALLBACK-LOGIK: Wenn Cloud leer liefert (Policy Violation / No data training), erzwinge lokal
            if not raw_data and provider != "ollama" and self.settings.LLM_FALLBACK_ENABLED:
                logger.warning(
                    f"🛑 [Ingestion] Cloud-Provider '{provider}' lieferte keine Daten für {note_id}. "
                    f"Mögliche Policy Violation. Erzwinge LOKALEN FALLBACK via Ollama..."
                )
                response_json = await self.llm.generate_raw_response(
                    prompt=prompt, priority="background", force_json=True,
                    provider="ollama"
                )
                raw_data = extract_json_from_response(response_json)

            # Recovery: Suche nach Listen in Dictionaries (z.B. {"matches": [...]})
            if isinstance(raw_data, dict):
                logger.info(f"ℹ️ [Ingestion] LLM returned dict, trying recovery for {note_id}")
                found_list = False
                for k in ["edges", "links", "results", "kanten", "matches", "edge_list"]:
                    if k in raw_data and isinstance(raw_data[k], list):
                        raw_data = raw_data[k]
                        found_list = True
                        break
                # Ultimativer Dict-Fallback: Key-Value Paare als Kanten interpretieren
                if not found_list:
                    new_list = []
                    for k, v in raw_data.items():
                        if isinstance(v, str): new_list.append(f"{k}:{v}")
                        elif isinstance(v, list): 
                            for target in v:
                                if isinstance(target, str): new_list.append(f"{k}:{target}")
                    raw_data = new_list
            
            if not isinstance(raw_data, list) or not raw_data:
                logger.warning(f"⚠️ [Ingestion] LLM lieferte keine extrahierbaren Kanten für {note_id}")
                return []

            processed = []
            for item in raw_data:
                # Erkennt sowohl Dict als auch String ["kind:target"]
                if isinstance(item, dict) and "to" in item:
                    item["provenance"] = "semantic_ai"
                    item["line"] = f"ai-{provider}"
                    processed.append(item)
                elif isinstance(item, str) and ":" in item:
                    parts = item.split(":", 1)
                    processed.append({
                        "to": parts[1].strip(),
                        "kind": parts[0].strip(),
                        "provenance": "semantic_ai",
                        "line": f"ai-{provider}"
                    })
            return processed

        except Exception as e:
            logger.warning(f"⚠️ [Ingestion] Smart Edge Allocation failed for {note_id}: {e}")
            return []

    async def process_file(
        self, file_path: str, vault_root: str,
        force_replace: bool = False, apply: bool = False, purge_before: bool = False,
        note_scope_refs: bool = False, hash_source: str = "parsed", hash_normalize: str = "canonical"
    ) -> Dict[str, Any]:
        """Transformiert eine Markdown-Datei in den Graphen (Notes, Chunks, Edges)."""
        result = {"path": file_path, "status": "skipped", "changed": False, "error": None}

        # 1. Parse & Lifecycle Gate
        try:
            parsed = read_markdown(file_path)
            if not parsed: return {**result, "error": "Empty file"}
            fm = normalize_frontmatter(parsed.frontmatter)
            validate_required_frontmatter(fm)
        except Exception as e:
            return {**result, "error": f"Validation failed: {str(e)}"}

        status = fm.get("status", "draft").lower().strip()
        if status in ["system", "template", "archive", "hidden"]:
            return {**result, "status": "skipped", "reason": f"lifecycle_{status}"}

        # 2. Config Resolution & Payload Construction
        note_type = self._resolve_note_type(fm.get("type"))
        fm["type"] = note_type
        
        try:
            note_pl = make_note_payload(parsed, vault_root=vault_root, hash_normalize=hash_normalize, hash_source=hash_source, file_path=file_path)
            note_id = note_pl["note_id"]
        except Exception as e:
             return {**result, "error": f"Payload failed: {str(e)}"}

        # 3. Change Detection
        old_payload = None if force_replace else self._fetch_note_payload(note_id)
        check_key = f"{self.active_hash_mode}:{hash_source}:{hash_normalize}"
        old_hash = (old_payload or {}).get("hashes", {}).get(check_key)
        new_hash = note_pl.get("hashes", {}).get(check_key)
        
        # Prüfung auf fehlende Artefakte in Qdrant
        chunks_missing, edges_missing = self._artifacts_missing(note_id)
        
        should_write = force_replace or (not old_payload) or (old_hash != new_hash) or chunks_missing or edges_missing

        if not should_write:
            return {**result, "status": "unchanged", "note_id": note_id}

        if not apply:
            return {**result, "status": "dry-run", "changed": True, "note_id": note_id}

        # 4. Processing (Chunking, Embedding, AI Edges)
        try:
            body_text = getattr(parsed, "body", "") or ""
            edge_registry.ensure_latest()

            # Profil-gesteuertes Chunking
            profile = fm.get("chunk_profile") or fm.get("chunking_profile") or "sliding_standard"
            chunk_cfg = self._get_chunk_config_by_profile(profile, note_type)
            chunks = await assemble_chunks(fm["id"], body_text, fm["type"], config=chunk_cfg)
            chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
            
            # Vektorisierung
            vecs = []
            if chunk_pls:
                texts = [c.get("window") or c.get("text") or "" for c in chunk_pls]
                vecs = await self.embedder.embed_documents(texts)
            
            # Kanten-Extraktion
            edges = []
            context = {"file": file_path, "note_id": note_id}

            # A. Explizite Kanten (User)
            for e in extract_edges_with_context(parsed):
                e["kind"] = edge_registry.resolve(edge_type=e["kind"], provenance="explicit", context={**context, "line": e.get("line")})
                edges.append(e)

            # B. KI Kanten (Turbo)
            ai_edges = await self._perform_smart_edge_allocation(body_text, note_id)
            for e in ai_edges:
                valid_kind = edge_registry.resolve(edge_type=e.get("kind"), provenance="semantic_ai", context={**context, "line": e.get("line")})
                e["kind"] = valid_kind
                edges.append(e)

            # C. System Kanten (Struktur)
            try:
                sys_edges = build_edges_for_note(note_id, chunk_pls, note_level_references=note_pl.get("references", []), include_note_scope_refs=note_scope_refs)
            except: 
                sys_edges = build_edges_for_note(note_id, chunk_pls)
            
            for e in sys_edges:
                valid_kind = edge_registry.resolve(edge_type=e.get("kind", "belongs_to"), provenance="structure", context={**context, "line": "system"})
                if valid_kind:
                    e["kind"] = valid_kind
                    edges.append(e)

        except Exception as e:
            logger.error(f"Processing failed for {file_path}: {e}", exc_info=True)
            return {**result, "error": f"Processing failed: {str(e)}"}

        # 5. DB Upsert
        try:
            if purge_before and old_payload: self._purge_artifacts(note_id)

            n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
            upsert_batch(self.client, n_name, n_pts)

            if chunk_pls and vecs:
                c_name, c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)
                upsert_batch(self.client, c_name, c_pts)
            
            if edges:
                e_name, e_pts = points_for_edges(self.prefix, edges)
                upsert_batch(self.client, e_name, e_pts)

            return {"path": file_path, "status": "success", "changed": True, "note_id": note_id, "chunks_count": len(chunk_pls), "edges_count": len(edges)}
        except Exception as e:
            return {**result, "error": f"DB Upsert failed: {e}"}
            
    def _fetch_note_payload(self, note_id: str) -> Optional[dict]:
        from qdrant_client.http import models as rest
        try:
            f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
            pts, _ = self.client.scroll(collection_name=f"{self.prefix}_notes", scroll_filter=f, limit=1, with_payload=True)
            return pts[0].payload if pts else None
        except: return None

    def _artifacts_missing(self, note_id: str) -> Tuple[bool, bool]:
        """Prüft Qdrant aktiv auf vorhandene Chunks und Edges."""
        from qdrant_client.http import models as rest
        try:
            f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
            c_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_chunks", scroll_filter=f, limit=1)
            e_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_edges", scroll_filter=f, limit=1)
            return (not bool(c_pts)), (not bool(e_pts))
        except: return True, True

    def _purge_artifacts(self, note_id: str):
        from qdrant_client.http import models as rest
        f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
        for suffix in ["chunks", "edges"]:
            try: self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=rest.FilterSelector(filter=f))
            except: pass
    
    async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
        """Hilfsmethode zur Erstellung einer Note aus einem Textstream."""
        target_dir = os.path.join(vault_root, folder)
        os.makedirs(target_dir, exist_ok=True)
        file_path = os.path.join(target_dir, filename)
        with open(file_path, "w", encoding="utf-8") as f:
            f.write(markdown_content)
        await asyncio.sleep(0.1) 
        return await self.process_file(file_path=file_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)