Refactor graph_utils.py and ingestion_processor.py: Update documentation for deterministic UUIDs to enhance Qdrant compatibility. Improve logging and ID validation in ingestion_processor.py, including adjustments to edge processing logic and batch import handling for better clarity and robustness. Version updates to 1.2.0 and 3.1.9 respectively.

This commit is contained in:
Lars 2026-01-09 22:05:50 +01:00
parent 7ed82ad82e
commit 008a470f02
2 changed files with 48 additions and 49 deletions

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@ -2,7 +2,7 @@
FILE: app/core/graph/graph_utils.py
DESCRIPTION: Basale Werkzeuge, ID-Generierung und Provenance-Konfiguration für den Graphen.
WP-24c: Integration der EdgeRegistry für dynamische Topologie-Defaults.
FIX v1.2.0: Umstellung auf deterministische UUIDs (Qdrant Kompatibilität).
FIX v1.2.0: Umstellung auf deterministische UUIDs für Qdrant-Kompatibilität.
VERSION: 1.2.0
STATUS: Active
"""

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@ -5,8 +5,8 @@ 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 v3.1.9: Fix für TypeError (Sync-Check), ID-Validierung und UUID-Support.
VERSION: 3.1.9 (WP-24c: Robust Symmetry & Sync Fix)
AUDIT v3.1.9: Vollständiges Script mit Business-Logging, UUIDs und Edge-Fix.
VERSION: 3.1.9 (WP-24c: Robust Orchestration & Full Feature Set)
STATUS: Active
"""
import logging
@ -51,13 +51,18 @@ logger = logging.getLogger(__name__)
class IngestionService:
def __init__(self, collection_prefix: str = None):
"""Initialisiert den Service und nutzt die neue database-Infrastruktur."""
"""Initialisiert den Service und bereinigt das Logging von technischem Lärm."""
from app.config import get_settings
self.settings = get_settings()
# --- LOGGING CLEANUP (Business Focus) ---
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)
@ -69,48 +74,40 @@ class IngestionService:
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) -> bool:
def _is_valid_note_id(self, text: str, provenance: str = "explicit") -> bool:
"""
WP-24c: Prüft, ob ein String eine plausible Note-ID oder ein gültiger Titel ist.
Verhindert Symmetrie-Kanten zu Meta-Begriffen wie 'insight' oder 'event'.
WP-24c: Prüft Ziel-Strings auf Validität.
User-Authority (explicit) wird weniger gefiltert als System-Strukturen.
"""
if not text or len(text.strip()) < 3:
if not text or len(text.strip()) < 2:
return False
blacklisted = {
"insight", "event", "source", "task", "project",
"person", "concept", "value", "principle", "lesson",
"decision", "requirement", "related_to", "referenced_by"
}
if text.lower().strip() in blacklisted:
return False
if len(text) > 100:
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: Implementiert den Two-Pass Ingestion Workflow.
WP-15b: Two-Pass Ingestion Workflow.
"""
self.processed_explicit_ids.clear()
logger.info(f"--- 🔍 START BATCH IMPORT ({len(file_paths)} Dateien) ---")
logger.info(f"🔍 [Pass 1] Pre-Scanning {len(file_paths)} files for Context Cache...")
for path in file_paths:
try:
ctx = pre_scan_markdown(path, registry=self.registry)
@ -122,8 +119,13 @@ class IngestionService:
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]
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."""
@ -161,6 +163,8 @@ class IngestionService:
)
note_id = note_pl["note_id"]
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)
@ -174,7 +178,7 @@ class IngestionService:
if not apply:
return {**result, "status": "dry-run", "changed": True, "note_id": note_id}
# 3. Deep Processing
# 3. Deep Processing (Chunking, Validation, Embedding)
try:
body_text = getattr(parsed, "body", "") or ""
edge_registry.ensure_latest()
@ -185,6 +189,7 @@ class IngestionService:
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", []):
@ -192,6 +197,7 @@ class IngestionService:
is_valid = await validate_edge_candidate(
ch.text, cand, self.batch_cache, self.llm, profile_name="ingest_validator"
)
logger.info(f" 🧠 [SMART EDGE] {cand['target_id']} -> {'✅ OK' if is_valid else '❌ SKIP'}")
if is_valid: new_pool.append(cand)
else:
new_pool.append(cand)
@ -212,7 +218,9 @@ class IngestionService:
# 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): continue
if not self._is_valid_note_id(target_raw, provenance="explicit"):
logger.warning(f" ⚠️ Ignoriere Kante zu '{target_raw}' (Ungültige ID)")
continue
resolved_kind = edge_registry.resolve(e.get("kind", "related_to"), provenance=e.get("provenance", "explicit"))
e.update({
@ -233,12 +241,12 @@ class IngestionService:
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)):
if (inv_kind and target_id and target_id != note_id and self._is_valid_note_id(target_id, provenance="structure")):
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
# FIX v3.1.9: Kein 'await' verwenden, da die DB-Funktion synchron ist!
# 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)
@ -252,39 +260,30 @@ class IngestionService:
"origin_note_id": note_id
})
final_edges.append(inv_edge)
logger.info(f"🔄 [SYMMETRY] Built inverse: {target_id} --({inv_kind})--> {note_id}")
logger.info(f" 🔄 [SYMMETRY] Gegenkante: {target_id} --({inv_kind})--> {note_id}")
edges = final_edges
# 4. DB Upsert
if apply:
if purge_before and old_payload:
purge_artifacts(self.client, self.prefix, 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 purge_before: 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])
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)
upsert_batch(self.client, f"{self.prefix}_chunks", points_for_chunks(self.prefix, chunk_pls, vecs)[1])
if edges:
e_pts = points_for_edges(self.prefix, edges)[1]
upsert_batch(self.client, f"{self.prefix}_edges", e_pts)
upsert_batch(self.client, f"{self.prefix}_edges", points_for_edges(self.prefix, edges)[1])
return {
"path": file_path, "status": "success", "changed": True, "note_id": note_id,
"chunks_count": len(chunk_pls), "edges_count": len(edges)
}
logger.info(f" ✨ Fertig: {len(chunk_pls)} Chunks, {len(edges)} Kanten.")
return {"status": "success", "note_id": note_id, "edges_count": len(edges)}
except Exception as e:
logger.error(f"Processing failed: {e}", exc_info=True)
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)
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)