WP24c - Agentic Edge Validation & Chunk-Aware Multigraph-System (v4.5.8) #22

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@ -5,9 +5,9 @@ DESCRIPTION: Der zentrale IngestionService (Orchestrator).
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 v3.3.1: Strikte Trennung von Explicit-Write (Phase 1) und
Symmetry-Validation (Phase 2). 100% Datenhoheit für den Nutzer.
VERSION: 3.3.1 (WP-24c: Authority-First Ingestion)
AUDIT v3.3.2: 2-Phasen-Schreibstrategie & API-Kompatibilitäts Fix.
Garantiert Datenhoheit expliziter Kanten.
VERSION: 3.3.2 (WP-24c: Authority-First Batch Orchestration)
STATUS: Active
"""
import logging
@ -25,7 +25,7 @@ from app.core.chunking import assemble_chunks
# WP-24c: Import für die deterministische UUID-Vorabberechnung
from app.core.graph.graph_utils import _mk_edge_id
# MODULARISIERUNG: Neue Import-Pfade für die Datenbank-Ebene
# Datenbank-Ebene (Modularisierte database-Infrastruktur)
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
@ -56,7 +56,7 @@ class IngestionService:
from app.config import get_settings
self.settings = get_settings()
# --- LOGGING CLEANUP ---
# --- 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)
@ -79,7 +79,12 @@ class IngestionService:
# Festlegen, welcher Hash für die Change-Detection maßgeblich ist
self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
self.batch_cache: Dict[str, NoteContext] = {} # Globaler Kontext-Cache (Pass 1)
# WP-15b: Kontext-Gedächtnis für ID-Auflösung
self.batch_cache: Dict[str, NoteContext] = {}
# WP-24c: Puffer für Phase 2 (Symmetrie-Injektion)
self.symmetry_buffer: List[Dict[str, Any]] = []
try:
# Aufruf der modularisierten Schema-Logik
@ -91,7 +96,7 @@ class IngestionService:
def _is_valid_note_id(self, text: str) -> bool:
"""
WP-24c: Prüft Ziel-Strings auf fachliche Validität.
Verhindert das Anlegen von Kanten zu reinen System-Platzhaltern.
Verhindert Müll-Kanten zu System-Platzhaltern.
"""
if not text or len(text.strip()) < 2:
return False
@ -101,21 +106,25 @@ class IngestionService:
if text.lower().strip() in blacklisted:
return False
if len(text) > 120: return False # Wahrscheinlich kein Titel
# Längere Titel zulassen (z.B. für Hubs), aber keine ganzen Sätze
if len(text) > 200: return False
return True
async def run_batch(self, file_paths: List[str], vault_root: str) -> List[Dict[str, Any]]:
async def run_batch(self, file_paths: List[str], vault_root: str) -> Dict[str, Any]:
"""
WP-15b: Two-Pass Ingestion Workflow mit 2-Phasen-Schreibstrategie.
Fix: Gibt Dictionary zurück, um Kompatibilität zum Importer-Script zu wahren.
"""
self.batch_cache.clear()
self.symmetry_buffer.clear()
logger.info(f"--- 🔍 START BATCH IMPORT ({len(file_paths)} Dateien) ---")
# SCHRITT 1: Pre-Scan (Context-Cache füllen)
# 1. Schritt: Pre-Scan (Context-Cache füllen)
for path in file_paths:
try:
ctx = pre_scan_markdown(path, registry=self.registry)
if ctx:
# Look-up Index für Note_IDs und Titel
self.batch_cache[ctx.note_id] = ctx
self.batch_cache[ctx.title] = ctx
fname = os.path.splitext(os.path.basename(path))[0]
@ -123,31 +132,30 @@ class IngestionService:
except Exception as e:
logger.warning(f" ⚠️ Pre-scan fehlgeschlagen für {path}: {e}")
# SCHRITT 2: PHASE 1 (Authority-Schreiben)
# Wir verarbeiten alle Dateien und schreiben NUR explizite Kanten in die DB.
results = []
all_virtual_candidates = []
# 2. Schritt: PROCESSING (PHASE 1: AUTHORITY)
# Verarbeitet alle Dateien und schreibt NUR explizite Kanten in die DB.
processed_count = 0
success_count = 0
for p in file_paths:
# process_file liefert in dieser Version (res, virtual_candidates) zurück
res, candidates = await self.process_file(p, vault_root, apply=True, purge_before=True)
results.append(res)
all_virtual_candidates.extend(candidates)
processed_count += 1
res = await self.process_file(p, vault_root, apply=True, purge_before=True)
if res.get("status") == "success":
success_count += 1
# SCHRITT 3: PHASE 2 (Symmetrie-Ergänzung)
# Nachdem alle expliziten Kanten fest in Qdrant liegen, prüfen wir die Inversen.
if all_virtual_candidates:
logger.info(f"🔄 PHASE 2: Validiere {len(all_virtual_candidates)} Symmetrie-Kandidaten gegen Live-DB...")
# 3. Schritt: SYMMETRY INJECTION (PHASE 2)
# Erst jetzt, wo alle manuellen Kanten in Qdrant liegen, prüfen wir die Symmetrien.
if self.symmetry_buffer:
logger.info(f"🔄 PHASE 2: Validiere {len(self.symmetry_buffer)} Symmetrie-Kanten gegen Live-DB...")
final_virtuals = []
for v_edge in all_virtual_candidates:
# Eindeutige ID berechnen (muss exakt der ID in Phase 1 entsprechen)
v_id = _mk_edge_id(v_edge["kind"], v_edge["note_id"], v_edge["target_id"], "note")
for v_edge in self.symmetry_buffer:
# Eindeutige ID der potenziellen Symmetrie-Kante berechnen
v_id = _mk_edge_id(v_edge["kind"], v_edge["note_id"], v_edge["target_id"], v_edge.get("scope", "note"))
# Check: Liegt dort bereits eine manuelle Kante?
# Nur schreiben, wenn Qdrant sagt: "Keine manuelle Kante für diese ID vorhanden"
if not is_explicit_edge_present(self.client, self.prefix, v_id):
final_virtuals.append(v_edge)
else:
logger.debug(f" 🛡️ Symmetrie übersprungen (Manuelle Kante hat Vorrang): {v_id}")
logger.debug(f" 🛡️ Symmetrie unterdrückt (Manuelle Kante existiert): {v_id}")
if final_virtuals:
logger.info(f"📤 Schreibe {len(final_virtuals)} geschützte Symmetrie-Kanten.")
@ -155,13 +163,18 @@ class IngestionService:
upsert_batch(self.client, f"{self.prefix}_edges", e_pts)
logger.info(f"--- ✅ BATCH IMPORT BEENDET ---")
return results
return {
"status": "success",
"processed": processed_count,
"success": success_count,
"virtuals_added": len(self.symmetry_buffer)
}
async def process_file(self, file_path: str, vault_root: str, **kwargs) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
async def process_file(self, file_path: str, vault_root: str, **kwargs) -> Dict[str, Any]:
"""
Transformiert eine Markdown-Datei.
Schreibt Notes/Chunks/Explicit Edges sofort (Phase 1).
Gibt potenzielle Symmetrien für Phase 2 zurück.
Befüllt den Symmetrie-Puffer für Phase 2.
"""
apply = kwargs.get("apply", False)
force_replace = kwargs.get("force_replace", False)
@ -171,23 +184,22 @@ class IngestionService:
hash_normalize = kwargs.get("hash_normalize", "canonical")
result = {"path": file_path, "status": "skipped", "changed": False, "error": None}
virtual_candidates = []
# 1. Parse & Lifecycle Gate
try:
parsed = read_markdown(file_path)
if not parsed: return {**result, "error": "Empty file"}, []
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)}"}, []
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"}, []
return {**result, "status": "skipped", "reason": "lifecycle_filter"}
# 2. Payload & Change Detection
note_type = resolve_note_type(self.registry, fm.get("type"))
@ -208,10 +220,10 @@ class IngestionService:
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}, []
return {**result, "status": "unchanged", "note_id": note_id}
if not apply:
return {**result, "status": "dry-run", "changed": True, "note_id": note_id}, []
return {**result, "status": "dry-run", "changed": True, "note_id": note_id}
# 3. Deep Processing (Chunking, Validation, Embedding)
try:
@ -232,6 +244,7 @@ class IngestionService:
is_valid = await validate_edge_candidate(
ch.text, cand, self.batch_cache, self.llm, profile_name="ingest_validator"
)
# Fix (v3.3.2): Sicherer Zugriff via .get() verhindert Crash
t_id = cand.get('target_id') or cand.get('note_id') or "Unknown"
logger.info(f" 🧠 [SMART EDGE] {t_id} -> {'✅ OK' if is_valid else '❌ SKIP'}")
if is_valid: new_pool.append(cand)
@ -249,7 +262,7 @@ class IngestionService:
include_note_scope_refs=note_scope_refs
)
# PHASE 1: Authority-Check & Kanonisierung
# --- WP-24c: Symmetrie-Injektion (Authority Implementation) ---
explicit_edges = []
for e in raw_edges:
target_raw = e.get("target_id")
@ -261,14 +274,14 @@ class IngestionService:
resolved_kind = edge_registry.resolve(e.get("kind", "related_to"), provenance=e.get("provenance", "explicit"))
# Echte explizite Kante für Phase 1
# Echte physische Kante markieren (Phase 1)
e.update({
"kind": resolved_kind, "target_id": target_id,
"origin_note_id": note_id, "virtual": False, "confidence": 1.0
})
explicit_edges.append(e)
# Symmetrie-Kandidat für Phase 2 vorbereiten
# Symmetrie-Kandidat für Phase 2 puffern
inv_kind = edge_registry.get_inverse(resolved_kind)
if inv_kind and target_id != note_id:
v_edge = e.copy()
@ -277,28 +290,33 @@ class IngestionService:
"virtual": True, "provenance": "structure", "confidence": 1.0,
"origin_note_id": note_id
})
virtual_candidates.append(v_edge)
self.symmetry_buffer.append(v_edge)
# 4. DB Upsert (Phase 1)
# 4. DB Upsert (Phase 1: Authority)
if apply:
if purge_before and old_payload:
purge_artifacts(self.client, self.prefix, note_id)
upsert_batch(self.client, f"{self.prefix}_notes", points_for_note(self.prefix, note_pl, None, self.dim)[1])
# 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)
if chunk_pls and vecs:
upsert_batch(self.client, f"{self.prefix}_chunks", points_for_chunks(self.prefix, chunk_pls, vecs)[1])
c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)[1]
upsert_batch(self.client, f"{self.prefix}_chunks", c_pts)
if explicit_edges:
upsert_batch(self.client, f"{self.prefix}_edges", points_for_edges(self.prefix, explicit_edges)[1])
e_pts = points_for_edges(self.prefix, explicit_edges)[1]
upsert_batch(self.client, f"{self.prefix}_edges", e_pts)
logger.info(f" ✨ Phase 1 fertig: {len(chunk_pls)} Chunks, {len(explicit_edges)} explizite Kanten.")
return {
"path": file_path, "status": "success", "changed": True, "note_id": note_id,
"chunks_count": len(chunk_pls), "edges_count": len(explicit_edges)
}, virtual_candidates
}
except Exception as e:
logger.error(f"❌ Fehler bei {file_path}: {e}", exc_info=True)
return {**result, "error": str(e)}, []
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."""
@ -307,5 +325,4 @@ class IngestionService:
with open(target_path, "w", encoding="utf-8") as f:
f.write(markdown_content)
await asyncio.sleep(0.1)
res, _ = await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)
return res
return await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)