mindnet/app/core/ingestion/ingestion_processor.py

"""
FILE: app/core/ingestion/ingestion_processor.py
DESCRIPTION: Der zentrale IngestionService (Orchestrator). 
             WP-14: Modularisierung der Datenbank-Ebene (app.core.database).
             WP-15b: Two-Pass Workflow mit globalem Kontext-Cache.
             WP-20/22: Cloud-Resilienz und Content-Lifecycle integriert.
             AUDIT v2.13.12: Synchronisierung der Profil-Auflösung mit Registry-Defaults.
VERSION: 2.13.12
STATUS: Active
"""
import logging
import asyncio
import os
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

# 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

# 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 (Refactoring WP-14)
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
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()
        
        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.dim = self.settings.VECTOR_SIZE
        self.registry = load_type_registry()
        self.embedder = EmbeddingsClient()
        self.llm = LLMService() 
        
        # 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

        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}")

    async def run_batch(self, file_paths: List[str], vault_root: str) -> List[Dict[str, Any]]:
        """
        WP-15b: Implementiert den Two-Pass Ingestion Workflow.
        Pass 1: Pre-Scan füllt den Context-Cache (3-Wege-Indexierung).
        Pass 2: Verarbeitung nutzt den Cache für die semantische Prüfung.
        """
        logger.info(f"🔍 [Pass 1] Pre-Scanning {len(file_paths)} files for Context Cache...")
        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}")

        logger.info(f"🚀 [Pass 2] Semantic Processing of {len(file_paths)} files...")
        return [await self.process_file(p, vault_root, apply=True, purge_before=True) for p in file_paths]

    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)}"}

        # Dynamischer Lifecycle-Filter aus der Registry (WP-14)
        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 (Multi-Hash)
        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"]

        # Abgleich mit der Datenbank (Qdrant)
        old_payload = None if force_replace else fetch_note_payload(self.client, self.prefix, note_id)
        
        # Prüfung gegen den konfigurierten Hash-Modus (body vs. full)
        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)
        
        # Check ob Chunks oder Kanten in der DB fehlen (Reparatur-Modus)
        c_miss, e_miss = artifacts_missing(self.client, self.prefix, note_id)
        
        # Wenn Hash identisch und Artefakte vorhanden -> Skip
        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()
            
            # Profil-Auflösung via Registry
            # FIX: Wir nutzen das Profil, das bereits in make_note_payload unter 
            # Berücksichtigung der types.yaml (Registry) ermittelt wurde.
            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)
            
            # WP-15b: Chunker-Aufruf bereitet den Candidate-Pool pro Chunk vor.
            # assemble_chunks führt intern auch die Propagierung durch.
            chunks = await assemble_chunks(note_id, body_text, note_type, config=chunk_cfg)
            
            # Semantische Kanten-Validierung (Smart Edge Allocation)
            for ch in chunks:
                filtered = []
                for cand in getattr(ch, "candidate_pool", []):
                    # Nur global_pool Kandidaten (aus dem Pool am Ende) erfordern KI-Validierung
                    if cand.get("provenance") == "global_pool" and enable_smart:
                        if await validate_edge_candidate(ch.text, cand, self.batch_cache, self.llm, self.settings.MINDNET_LLM_PROVIDER):
                            filtered.append(cand)
                    else: 
                        # Explizite Kanten (Wikilinks/Callouts) werden ungeprüft übernommen
                        filtered.append(cand)
                ch.candidate_pool = filtered

            # Payload-Erstellung für die Chunks
            chunk_pls = make_chunk_payloads(
                fm, note_pl["path"], chunks, file_path=file_path, 
                types_cfg=self.registry
            )
            
            # Vektorisierung der Fenster-Texte
            vecs = await self.embedder.embed_documents([c.get("window") or "" for c in chunk_pls]) if chunk_pls else []
            
            # Aggregation aller finalen Kanten (Edges)
            edges = build_edges_for_note(
                note_id, chunk_pls, 
                note_level_references=note_pl.get("references", []),
                include_note_scope_refs=note_scope_refs
            )
            
            # Kanten-Typen via Registry validieren/auflösen
            for e in edges:
                e["kind"] = edge_registry.resolve(
                    e.get("kind", "related_to"), 
                    provenance=e.get("provenance", "explicit"), 
                    context={"file": file_path, "note_id": note_id, "line": e.get("line", "system")}
                )

            # 4. DB Upsert via modularisierter Points-Logik
            # WICHTIG: Wenn sich der Inhalt geändert hat, löschen wir erst alle alten Fragmente.
            if purge_before and old_payload: 
                purge_artifacts(self.client, self.prefix, note_id)
            
            # Speichern der Haupt-Note
            n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
            upsert_batch(self.client, n_name, n_pts)
            
            # Speichern der Chunks
            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)
            
            # Speichern der Kanten
            if edges: 
                e_pts = points_for_edges(self.prefix, edges)[1]
                upsert_batch(self.client, f"{self.prefix}_edges", 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:
            logger.error(f"Processing failed: {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 und triggert die Ingestion."""
        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) 
        # Triggert sofortigen Import mit force_replace/purge_before
        return await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)