mindnet/app/core/ingestion/ingestion_processor.py

"""
FILE: app/core/ingestion/ingestion_processor.py
DESCRIPTION: Der zentrale IngestionService (Orchestrator). 
             WP-24c: Integration der Symmetrie-Logik (Automatische inverse Kanten).
             WP-25a: Integration der Mixture of Experts (MoE) Architektur.
             WP-15b: Two-Pass Workflow mit globalem Kontext-Cache.
             WP-20/22: Cloud-Resilienz und Content-Lifecycle integriert.
             AUDIT v3.2.1: Fix für ID-Kanonisierung in Phase 1 & 2, 
                          robuster Smart-Edge-Logger und Business-Logging.
VERSION: 3.2.1 (WP-24c: Canonical Authority Protection)
STATUS: Active
"""
import logging
import asyncio
import os
import re
from typing import Dict, List, Optional, Tuple, Any

# Core Module Imports
from app.core.parser import (
    read_markdown, pre_scan_markdown, normalize_frontmatter, 
    validate_required_frontmatter, NoteContext
)
from app.core.chunking import assemble_chunks
# WP-24c: Import für die deterministische ID-Vorabberechnung
from app.core.graph.graph_utils import _mk_edge_id 

# MODULARISIERUNG: Neue Import-Pfade für die Datenbank-Ebene
from app.core.database.qdrant import QdrantConfig, get_client, ensure_collections, ensure_payload_indexes
from app.core.database.qdrant_points import points_for_chunks, points_for_note, points_for_edges, upsert_batch
from qdrant_client.http import models as rest 

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

# Package-Interne Imports
from .ingestion_utils import load_type_registry, resolve_note_type, get_chunk_config_by_profile
from .ingestion_db import fetch_note_payload, artifacts_missing, purge_artifacts, is_explicit_edge_present
from .ingestion_validation import validate_edge_candidate
from .ingestion_note_payload import make_note_payload
from .ingestion_chunk_payload import make_chunk_payloads

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

logger = logging.getLogger(__name__)

class IngestionService:
    def __init__(self, collection_prefix: str = None):
        """Initialisiert den Service und nutzt die neue database-Infrastruktur."""
        from app.config import get_settings
        self.settings = get_settings()
        
        # --- LOGGING CLEANUP (Business Focus) ---
        # Unterdrückt Bibliotheks-Lärm in Konsole und Datei (via tee)
        logging.getLogger("httpx").setLevel(logging.WARNING)
        logging.getLogger("httpcore").setLevel(logging.WARNING)
        logging.getLogger("qdrant_client").setLevel(logging.WARNING)
        logging.getLogger("urllib3").setLevel(logging.WARNING)

        self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX
        self.cfg = QdrantConfig.from_env()
        # Synchronisierung der Konfiguration mit dem Instanz-Präfix
        self.cfg.prefix = self.prefix 
        self.client = get_client(self.cfg)
        
        self.registry = load_type_registry()
        self.embedder = EmbeddingsClient()
        self.llm = LLMService() 
        
        # WP-25a: Auflösung der Dimension über das Embedding-Profil (MoE)
        embed_cfg = self.llm.profiles.get("embedding_expert", {})
        self.dim = embed_cfg.get("dimensions") or self.settings.VECTOR_SIZE
        
        # Festlegen, welcher Hash für die Change-Detection maßgeblich ist
        self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
        self.batch_cache: Dict[str, NoteContext] = {} # WP-15b LocalBatchCache
        
        # WP-24c: Laufzeit-Speicher für explizite Kanten-IDs im aktuellen Batch
        self.processed_explicit_ids = set()

        try:
            # Aufruf der modularisierten Schema-Logik
            ensure_collections(self.client, self.prefix, self.dim)
            ensure_payload_indexes(self.client, self.prefix)
        except Exception as e: 
            logger.warning(f"DB initialization warning: {e}")

    def _is_valid_note_id(self, text: str, provenance: str = "explicit") -> bool:
        """
        WP-24c: Prüft Ziel-Strings auf Validität.
        User-Links (explicit) werden weniger gefiltert als System-Symmetrien.
        """
        if not text or len(text.strip()) < 2:
            return False
            
        # Nur System-Kanten (Symmetrie) filtern wir gegen die Typ-Blacklist
        if provenance != "explicit":
            blacklisted = {"insight", "event", "source", "task", "project", "person", "concept", "related_to", "referenced_by"}
            if text.lower().strip() in blacklisted:
                return False
            
        if len(text) > 150: return False # Vermutlich ein ganzer Satz
        return True

    async def run_batch(self, file_paths: List[str], vault_root: str) -> List[Dict[str, Any]]:
        """
        WP-15b: Two-Pass Ingestion Workflow.
        Pass 1: Pre-Scan füllt den Context-Cache.
        Pass 2: Verarbeitung nutzt den Cache für die semantische Prüfung.
        """
        self.processed_explicit_ids.clear()
        logger.info(f"--- 🔍 START BATCH IMPORT ({len(file_paths)} Dateien) ---")

        for path in file_paths:
            try:
                # Übergabe der Registry für dynamische Scan-Tiefe
                ctx = pre_scan_markdown(path, registry=self.registry)
                if ctx:
                    # Mehrfache Indizierung für robusten Look-up (ID, Titel, Dateiname)
                    self.batch_cache[ctx.note_id] = ctx
                    self.batch_cache[ctx.title] = ctx
                    fname = os.path.splitext(os.path.basename(path))[0]
                    self.batch_cache[fname] = ctx
            except Exception as e:
                logger.warning(f"⚠️ Pre-scan failed for {path}: {e}")

        results = []
        for p in file_paths:
            res = await self.process_file(p, vault_root, apply=True, purge_before=True)
            results.append(res)
            
        logger.info(f"--- ✅ BATCH IMPORT BEENDET ---")
        return results

    async def process_file(self, file_path: str, vault_root: str, **kwargs) -> Dict[str, Any]:
        """Transformiert eine Markdown-Datei in den Graphen."""
        apply = kwargs.get("apply", False)
        force_replace = kwargs.get("force_replace", False)
        purge_before = kwargs.get("purge_before", False)
        note_scope_refs = kwargs.get("note_scope_refs", False)
        hash_source = kwargs.get("hash_source", "parsed")
        hash_normalize = kwargs.get("hash_normalize", "canonical")
        
        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)}"}

        ingest_cfg = self.registry.get("ingestion_settings", {})
        ignore_list = ingest_cfg.get("ignore_statuses", ["system", "template", "archive", "hidden"])
        
        current_status = fm.get("status", "draft").lower().strip()
        if current_status in ignore_list:
            return {**result, "status": "skipped", "reason": "lifecycle_filter"}

        # 2. Payload & Change Detection
        note_type = resolve_note_type(self.registry, fm.get("type"))
        note_pl = make_note_payload(
            parsed, vault_root=vault_root, file_path=file_path, 
            hash_source=hash_source, hash_normalize=hash_normalize, 
            types_cfg=self.registry
        )
        note_id = note_pl["note_id"]

        # BUSINESS LOG: Aktuelle Notiz
        logger.info(f"📄 Bearbeite: '{note_id}' (Typ: {note_type})")

        old_payload = None if force_replace else fetch_note_payload(self.client, self.prefix, 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)
        
        c_miss, e_miss = artifacts_missing(self.client, self.prefix, note_id)
        
        if not (force_replace or not old_payload or old_hash != new_hash or c_miss or e_miss):
            return {**result, "status": "unchanged", "note_id": note_id}
        
        if not apply: 
            return {**result, "status": "dry-run", "changed": True, "note_id": note_id}

        # 3. Deep Processing (Chunking, Validation, Embedding)
        try:
            body_text = getattr(parsed, "body", "") or ""
            edge_registry.ensure_latest()
            profile = note_pl.get("chunk_profile", "sliding_standard")
            
            chunk_cfg = get_chunk_config_by_profile(self.registry, profile, note_type)
            enable_smart = chunk_cfg.get("enable_smart_edge_allocation", False)
            
            chunks = await assemble_chunks(note_id, body_text, note_type, config=chunk_cfg)
            
            # --- WP-25a: MoE Semantische Kanten-Validierung ---
            for ch in chunks:
                new_pool = []
                for cand in getattr(ch, "candidate_pool", []):
                    if cand.get("provenance") == "global_pool" and enable_smart:
                        is_valid = await validate_edge_candidate(
                            ch.text, cand, self.batch_cache, self.llm, profile_name="ingest_validator"
                        )
                        # Fix v3.2.1: Robuste ID-Auflösung für den Logger
                        t_label = cand.get('target_id') or cand.get('note_id') or cand.get('to') or "Unknown"
                        logger.info(f"  🧠 [SMART EDGE] {t_label} -> {'✅ OK' if is_valid else '❌ SKIP'}")
                        if is_valid: new_pool.append(cand)
                    else: 
                        new_pool.append(cand)
                ch.candidate_pool = new_pool

            chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, file_path=file_path, types_cfg=self.registry)
            vecs = await self.embedder.embed_documents([c.get("window") or "" for c in chunk_pls]) if chunk_pls else []
            
            # Aggregation aller finalen Kanten (Edges)
            raw_edges = build_edges_for_note(
                note_id, chunk_pls, 
                note_level_references=note_pl.get("references", []),
                include_note_scope_refs=note_scope_refs
            )
            
            # --- WP-24c: Symmetrie-Injektion (Authority Implementation) ---
            final_edges = []
            
            # PHASE 1: Alle expliziten Kanten registrieren
            for e in raw_edges:
                target_raw = e.get("target_id")
                if not self._is_valid_note_id(target_raw, provenance="explicit"):
                    continue
                
                resolved_kind = edge_registry.resolve(
                    e.get("kind", "related_to"), 
                    provenance=e.get("provenance", "explicit"), 
                    context={"file": file_path, "note_id": note_id}
                )
                e["kind"] = resolved_kind
                e["origin_note_id"] = note_id 
                e["virtual"] = False 
                e["confidence"] = e.get("confidence", 1.0)
                
                # Fix v3.2.1: Kanonisierung der Target-ID vor der Registrierung!
                # Nur wenn wir hier die echte Note-ID nutzen, erkennt Phase 2 die Kollision.
                t_ctx = self.batch_cache.get(target_raw)
                t_canonical = t_ctx.note_id if t_ctx else target_raw
                
                edge_id = _mk_edge_id(resolved_kind, note_id, t_canonical, e.get("scope", "note"))
                self.processed_explicit_ids.add(edge_id)
                final_edges.append(e)

            # PHASE 2: Symmetrische Kanten (Invers)
            explicit_only = [x for x in final_edges if not x.get("virtual")]
            for e in explicit_only:
                kind = e["kind"]
                inv_kind = edge_registry.get_inverse(kind)
                target_raw = e.get("target_id")
                target_ctx = self.batch_cache.get(target_raw)
                target_id = target_ctx.note_id if target_ctx else target_raw
                
                if (inv_kind and target_id and target_id != note_id and self._is_valid_note_id(target_id, provenance="structure")):
                    # ID der potenziellen virtuellen Kante
                    potential_id = _mk_edge_id(inv_kind, target_id, note_id, e.get("scope", "note"))
                    
                    is_in_batch = potential_id in self.processed_explicit_ids
                    
                    # Real-Time DB Check (Sync)
                    is_in_db = False
                    if not is_in_batch:
                        is_in_db = is_explicit_edge_present(self.client, self.prefix, potential_id)

                    if not is_in_batch and not is_in_db:
                        if (inv_kind != kind or kind not in ["related_to", "references"]):
                            inv_edge = e.copy()
                            inv_edge.update({
                                "note_id": target_id, "target_id": note_id, "kind": inv_kind,
                                "virtual": True, "provenance": "structure", "confidence": 1.0,
                                "origin_note_id": note_id 
                            })
                            final_edges.append(inv_edge)
                            logger.info(f"  🔄 [SYMMETRY] Gegenkante: {target_id} --({inv_kind})--> {note_id}")

            edges = final_edges

            # 4. DB Upsert via modularisierter Points-Logik
            if apply:
                if purge_before and old_payload: 
                    purge_artifacts(self.client, self.prefix, note_id)
                
                # Speichern
                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_pts = points_for_chunks(self.prefix, chunk_pls, vecs)[1]
                    upsert_batch(self.client, f"{self.prefix}_chunks", c_pts)
                
                if edges: 
                    e_pts = points_for_edges(self.prefix, edges)[1]
                    upsert_batch(self.client, f"{self.prefix}_edges", e_pts)
            
            logger.info(f"  ✨ Fertig: {len(chunk_pls)} Chunks, {len(edges)} Kanten.")
            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:
            logger.error(f"❌ Fehler bei {file_path}: {e}", exc_info=True)
            return {**result, "error": str(e)}

    async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
        """Erstellt eine Note aus einem Textstream."""
        target_path = os.path.join(vault_root, folder, filename)
        os.makedirs(os.path.dirname(target_path), exist_ok=True)
        with open(target_path, "w", encoding="utf-8") as f: 
            f.write(markdown_content)
        await asyncio.sleep(0.1) 
        return await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)