""" 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 v3.1.2: Redundanz-Check, ID-Resolution & Origin-Tracking. VERSION: 3.1.2 (WP-24c: Redundancy-Aware Symmetric Ingestion) 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 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.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 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 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. chunks = await assemble_chunks(note_id, body_text, note_type, config=chunk_cfg) # Semantische Kanten-Validierung (Primärprüfung) for ch in chunks: new_pool = [] for cand in getattr(ch, "candidate_pool", []): # WP-25a: Profilgesteuerte binäre Validierung if cand.get("provenance") == "global_pool" and enable_smart: is_valid = await validate_edge_candidate( chunk_text=ch.text, edge=cand, batch_cache=self.batch_cache, llm_service=self.llm, profile_name="ingest_validator" ) if is_valid: new_pool.append(cand) else: # Explizite Kanten (Wikilinks/Callouts) werden übernommen new_pool.append(cand) ch.candidate_pool = new_pool # 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) 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 (Bidirektionale Graph-Logik) --- final_edges = [] for e in raw_edges: # 1. Primär-Kante kanonisieren & Owner setzen 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 # Markierung der Herkunft für selektiven Purge e["origin_note_id"] = note_id final_edges.append(e) # 2. Symmetrie-Ermittlung via Registry inverse_kind = edge_registry.get_inverse(resolved_kind) target_raw = e.get("target_id") # ID-Resolution: Finden der echten Note_ID im Cache target_ctx = self.batch_cache.get(target_raw) target_canonical_id = target_ctx.note_id if target_ctx else target_raw # Validierung für Symmetrie-Erzeugung (Kein Self-Loop, Existenz der Inversen) if (inverse_kind and target_canonical_id and target_canonical_id != note_id): # REDUNDANZ-CHECK: Existiert bereits eine explizite Gegenrichtung? is_redundant = any( ex.get("target_id") == target_canonical_id and edge_registry.resolve(ex.get("kind")) == inverse_kind for ex in raw_edges ) # Nur anlegen, wenn nicht redundant und kein simpler related_to Loop if not is_redundant and (inverse_kind != resolved_kind or resolved_kind not in ["related_to", "references"]): inv_edge = e.copy() # Richtungs-Umkehr inv_edge["note_id"] = target_canonical_id inv_edge["target_id"] = note_id inv_edge["kind"] = inverse_kind # Metadaten für Struktur-Kante inv_edge["virtual"] = True inv_edge["provenance"] = "structure" inv_edge["confidence"] = e.get("confidence", 0.9) * 0.9 # Lifecycle-Verankerung: Diese Kante gehört logisch zum Verursacher (Note A) inv_edge["origin_note_id"] = note_id final_edges.append(inv_edge) logger.info(f"🔄 [SYMMETRY] Built inverse: {target_canonical_id} --({inverse_kind})--> {note_id}") edges = final_edges # 4. DB Upsert via modularisierter Points-Logik if purge_before and old_payload: # Hinweis: purge_artifacts wird im nächsten Schritt auf origin_note_id umgestellt 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 (inklusive der virtuellen Inversen) 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)