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390 lines
18 KiB
Python
390 lines
18 KiB
Python
"""
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FILE: app/core/ingestion.py
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DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen.
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WP-20: Optimiert für OpenRouter (mistralai/mistral-7b-instruct:free).
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WP-22: Content Lifecycle, Edge Registry Validation & Multi-Hash.
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FIX: Deep Fallback Logic (v2.11.14). Erkennt Policy Violations auch in validen
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JSON-Objekten und erzwingt den lokalen Ollama-Sprung, um Kantenverlust
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bei umfangreichen Protokollen zu verhindern.
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VERSION: 2.11.14
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STATUS: Active
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DEPENDENCIES: app.core.parser, app.core.note_payload, app.core.chunker, app.services.llm_service, app.services.edge_registry
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"""
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import os
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import json
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import re
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import logging
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import asyncio
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import time
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from typing import Dict, List, Optional, Tuple, Any
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# Core Module Imports
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from app.core.parser import (
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read_markdown,
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normalize_frontmatter,
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validate_required_frontmatter,
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extract_edges_with_context,
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)
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from app.core.note_payload import make_note_payload
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from app.core.chunker import assemble_chunks, get_chunk_config
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from app.core.chunk_payload import make_chunk_payloads
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# Fallback für Edges
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try:
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from app.core.derive_edges import build_edges_for_note
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except ImportError:
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def build_edges_for_note(*args, **kwargs): return []
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from app.core.qdrant import QdrantConfig, get_client, ensure_collections, ensure_payload_indexes
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from app.core.qdrant_points import (
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points_for_chunks,
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points_for_note,
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points_for_edges,
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upsert_batch,
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)
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from app.services.embeddings_client import EmbeddingsClient
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from app.services.edge_registry import registry as edge_registry
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from app.services.llm_service import LLMService
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logger = logging.getLogger(__name__)
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# --- Global Helpers ---
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def extract_json_from_response(text: str) -> Any:
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"""
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Extrahiert JSON-Daten und bereinigt LLM-Steuerzeichen (Mistral/Llama).
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Entfernt <s>, [OUT], [/OUT] und Markdown-Blöcke für maximale Robustheit.
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"""
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if not text or not isinstance(text, str):
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return []
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# 1. Entferne Mistral/Llama Steuerzeichen und Tags
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clean = text.replace("<s>", "").replace("</s>", "")
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clean = clean.replace("[OUT]", "").replace("[/OUT]", "")
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clean = clean.strip()
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# 2. Suche nach Markdown JSON-Blöcken (```json ... ```)
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match = re.search(r"```(?:json)?\s*(.*?)\s*```", clean, re.DOTALL)
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payload = match.group(1) if match else clean
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try:
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return json.loads(payload.strip())
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except json.JSONDecodeError:
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# 3. Recovery: Suche nach der ersten [ und letzten ] (Liste)
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start = payload.find('[')
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end = payload.rfind(']') + 1
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if start != -1 and end > start:
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try:
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return json.loads(payload[start:end])
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except: pass
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# 4. Zweite Recovery: Suche nach der ersten { und letzten } (Objekt)
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start_obj = payload.find('{')
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end_obj = payload.rfind('}') + 1
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if start_obj != -1 and end_obj > start_obj:
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try:
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return json.loads(payload[start_obj:end_obj])
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except: pass
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return []
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def load_type_registry(custom_path: Optional[str] = None) -> dict:
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"""Lädt die types.yaml zur Steuerung der typ-spezifischen Ingestion."""
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import yaml
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from app.config import get_settings
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settings = get_settings()
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path = custom_path or settings.MINDNET_TYPES_FILE
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if not os.path.exists(path): return {}
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try:
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with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {}
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except Exception: return {}
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# --- Service Class ---
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class IngestionService:
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def __init__(self, collection_prefix: str = None):
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from app.config import get_settings
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self.settings = get_settings()
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self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX
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self.cfg = QdrantConfig.from_env()
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self.cfg.prefix = self.prefix
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self.client = get_client(self.cfg)
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self.dim = self.settings.VECTOR_SIZE
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self.registry = load_type_registry()
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self.embedder = EmbeddingsClient()
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self.llm = LLMService()
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self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
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try:
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ensure_collections(self.client, self.prefix, self.dim)
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ensure_payload_indexes(self.client, self.prefix)
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except Exception as e:
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logger.warning(f"DB init warning: {e}")
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def _resolve_note_type(self, requested: Optional[str]) -> str:
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"""Bestimmt den finalen Notiz-Typ (Fallback auf 'concept')."""
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types = self.registry.get("types", {})
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if requested and requested in types: return requested
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return "concept"
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def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]:
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"""Holt die Chunker-Parameter für ein spezifisches Profil aus der Registry."""
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profiles = self.registry.get("chunking_profiles", {})
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if profile_name in profiles:
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cfg = profiles[profile_name].copy()
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if "overlap" in cfg and isinstance(cfg["overlap"], list):
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cfg["overlap"] = tuple(cfg["overlap"])
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return cfg
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return get_chunk_config(note_type)
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async def _perform_smart_edge_allocation(self, text: str, note_id: str) -> List[Dict]:
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"""
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KI-Extraktion mit Deep-Fallback Logik.
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Erzwingt den lokalen Ollama-Sprung, wenn die Cloud-Antwort keine verwertbaren
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Kanten liefert (häufig bei Policy Violations auf OpenRouter).
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"""
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provider = self.settings.MINDNET_LLM_PROVIDER
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model = self.settings.OPENROUTER_MODEL if provider == "openrouter" else self.settings.GEMINI_MODEL
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logger.info(f"🚀 [Ingestion] Turbo-Mode: Extracting edges for '{note_id}' using {model} on {provider}")
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edge_registry.ensure_latest()
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valid_types_str = ", ".join(sorted(list(edge_registry.valid_types)))
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template = self.llm.get_prompt("edge_extraction", provider)
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try:
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try:
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# Wir begrenzen den Kontext auf 6000 Zeichen (ca. 1500 Token)
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prompt = template.format(
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text=text[:6000],
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note_id=note_id,
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valid_types=valid_types_str
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)
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except KeyError as ke:
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logger.error(f"❌ [Ingestion] Prompt-Template Fehler (Variable {ke} fehlt).")
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return []
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# 1. Versuch: Anfrage an den primären Cloud-Provider
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response_json = await self.llm.generate_raw_response(
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prompt=prompt, priority="background", force_json=True,
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provider=provider, model_override=model
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)
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# Initiales Parsing
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raw_data = extract_json_from_response(response_json)
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# 2. Dictionary Recovery (Versuche Liste aus Dict zu extrahieren)
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candidates = []
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if isinstance(raw_data, list):
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candidates = raw_data
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elif isinstance(raw_data, dict):
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logger.info(f"ℹ️ [Ingestion] LLM returned dict, checking for embedded lists in {note_id}")
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for k in ["edges", "links", "results", "kanten", "matches", "edge_list"]:
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if k in raw_data and isinstance(raw_data[k], list):
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candidates = raw_data[k]
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break
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# Wenn immer noch keine Liste gefunden, versuche Key-Value Paare (Dict Recovery)
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if not candidates:
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for k, v in raw_data.items():
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if isinstance(v, str): candidates.append(f"{k}:{v}")
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elif isinstance(v, list): [candidates.append(f"{k}:{i}") for i in v if isinstance(i, str)]
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# 3. DEEP FALLBACK: Wenn nach allen Recovery-Versuchen die Liste leer ist UND wir in der Cloud waren
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# Triggert den Fallback bei "Data Policy Violations" (leere oder Fehler-JSONs).
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if not candidates and provider != "ollama" and self.settings.LLM_FALLBACK_ENABLED:
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logger.warning(
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f"🛑 [Ingestion] Cloud-Antwort für {note_id} lieferte keine verwertbaren Kanten. "
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f"Mögliche Policy Violation oder Refusal. Erzwinge LOKALEN FALLBACK via Ollama..."
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)
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response_json_local = await self.llm.generate_raw_response(
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prompt=prompt, priority="background", force_json=True, provider="ollama"
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)
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raw_data_local = extract_json_from_response(response_json_local)
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# Wiederhole Recovery für lokale Antwort
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if isinstance(raw_data_local, list):
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candidates = raw_data_local
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elif isinstance(raw_data_local, dict):
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for k in ["edges", "links", "results"]:
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if k in raw_data_local and isinstance(raw_data_local[k], list):
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candidates = raw_data_local[k]; break
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if not candidates:
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logger.warning(f"⚠️ [Ingestion] Auch nach Fallback keine extrahierbaren Kanten für {note_id}")
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return []
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processed = []
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for item in candidates:
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if isinstance(item, dict) and "to" in item:
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item["provenance"] = "semantic_ai"
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item["line"] = f"ai-{provider}"
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processed.append(item)
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elif isinstance(item, str) and ":" in item:
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parts = item.split(":", 1)
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processed.append({
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"to": parts[1].strip(),
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"kind": parts[0].strip(),
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"provenance": "semantic_ai",
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"line": f"ai-{provider}"
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})
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return processed
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except Exception as e:
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logger.warning(f"⚠️ [Ingestion] Smart Edge Allocation failed for {note_id}: {e}")
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return []
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async def process_file(
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self, file_path: str, vault_root: str,
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force_replace: bool = False, apply: bool = False, purge_before: bool = False,
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note_scope_refs: bool = False, hash_source: str = "parsed", hash_normalize: str = "canonical"
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) -> Dict[str, Any]:
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"""Transformiert eine Markdown-Datei in den Graphen (Notes, Chunks, Edges)."""
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result = {"path": file_path, "status": "skipped", "changed": False, "error": None}
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# 1. Parse & Lifecycle Gate
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try:
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parsed = read_markdown(file_path)
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if not parsed: return {**result, "error": "Empty file"}
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fm = normalize_frontmatter(parsed.frontmatter)
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validate_required_frontmatter(fm)
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except Exception as e:
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return {**result, "error": f"Validation failed: {str(e)}"}
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# WP-22: Filter für Systemdateien und Entwürfe
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status = fm.get("status", "draft").lower().strip()
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if status in ["system", "template", "archive", "hidden"]:
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return {**result, "status": "skipped", "reason": f"lifecycle_{status}"}
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# 2. Config Resolution & Payload Construction
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note_type = self._resolve_note_type(fm.get("type"))
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fm["type"] = note_type
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try:
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note_pl = make_note_payload(parsed, vault_root=vault_root, hash_normalize=hash_normalize, hash_source=hash_source, file_path=file_path)
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note_id = note_pl["note_id"]
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except Exception as e:
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return {**result, "error": f"Payload failed: {str(e)}"}
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# 3. Change Detection (Strikte DoD Umsetzung)
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old_payload = None if force_replace else self._fetch_note_payload(note_id)
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check_key = f"{self.active_hash_mode}:{hash_source}:{hash_normalize}"
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old_hash = (old_payload or {}).get("hashes", {}).get(check_key)
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new_hash = note_pl.get("hashes", {}).get(check_key)
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# Prüfung auf fehlende Artefakte in Qdrant
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chunks_missing, edges_missing = self._artifacts_missing(note_id)
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should_write = force_replace or (not old_payload) or (old_hash != new_hash) or chunks_missing or edges_missing
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if not should_write:
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return {**result, "status": "unchanged", "note_id": note_id}
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if not apply:
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return {**result, "status": "dry-run", "changed": True, "note_id": note_id}
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# 4. Processing (Chunking, Embedding, AI Edges)
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try:
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body_text = getattr(parsed, "body", "") or ""
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edge_registry.ensure_latest()
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# Profil-gesteuertes Chunking
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profile = fm.get("chunk_profile") or fm.get("chunking_profile") or "sliding_standard"
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chunk_cfg = self._get_chunk_config_by_profile(profile, note_type)
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chunks = await assemble_chunks(fm["id"], body_text, fm["type"], config=chunk_cfg)
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chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
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# Vektorisierung
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vecs = []
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if chunk_pls:
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texts = [c.get("window") or c.get("text") or "" for c in chunk_pls]
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vecs = await self.embedder.embed_documents(texts)
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# Kanten-Extraktion
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edges = []
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context = {"file": file_path, "note_id": note_id}
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# A. Explizite Kanten (User / Wikilinks)
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for e in extract_edges_with_context(parsed):
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e["kind"] = edge_registry.resolve(edge_type=e["kind"], provenance="explicit", context={**context, "line": e.get("line")})
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edges.append(e)
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# B. KI Kanten (Turbo Mode mit v2.11.14 Fallback)
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ai_edges = await self._perform_smart_edge_allocation(body_text, note_id)
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for e in ai_edges:
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valid_kind = edge_registry.resolve(edge_type=e.get("kind"), provenance="semantic_ai", context={**context, "line": e.get("line")})
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e["kind"] = valid_kind
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edges.append(e)
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# C. System Kanten (Struktur)
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try:
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sys_edges = build_edges_for_note(note_id, chunk_pls, note_level_references=note_pl.get("references", []), include_note_scope_refs=note_scope_refs)
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except:
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sys_edges = build_edges_for_note(note_id, chunk_pls)
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for e in sys_edges:
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valid_kind = edge_registry.resolve(edge_type=e.get("kind", "belongs_to"), provenance="structure", context={**context, "line": "system"})
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if valid_kind:
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e["kind"] = valid_kind
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edges.append(e)
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except Exception as e:
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logger.error(f"Processing failed for {file_path}: {e}", exc_info=True)
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return {**result, "error": f"Processing failed: {str(e)}"}
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# 5. DB Upsert
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try:
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if purge_before and old_payload: self._purge_artifacts(note_id)
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n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
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upsert_batch(self.client, n_name, n_pts)
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if chunk_pls and vecs:
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c_name, c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)
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upsert_batch(self.client, c_name, c_pts)
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if edges:
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e_name, e_pts = points_for_edges(self.prefix, edges)
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upsert_batch(self.client, e_name, e_pts)
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return {"path": file_path, "status": "success", "changed": True, "note_id": note_id, "chunks_count": len(chunk_pls), "edges_count": len(edges)}
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except Exception as e:
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return {**result, "error": f"DB Upsert failed: {e}"}
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def _fetch_note_payload(self, note_id: str) -> Optional[dict]:
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"""Holt die Metadaten einer Note aus Qdrant."""
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from qdrant_client.http import models as rest
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try:
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f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
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pts, _ = self.client.scroll(collection_name=f"{self.prefix}_notes", scroll_filter=f, limit=1, with_payload=True)
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return pts[0].payload if pts else None
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except: return None
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def _artifacts_missing(self, note_id: str) -> Tuple[bool, bool]:
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"""Prüft Qdrant aktiv auf vorhandene Chunks und Edges."""
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from qdrant_client.http import models as rest
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try:
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f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
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c_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_chunks", scroll_filter=f, limit=1)
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e_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_edges", scroll_filter=f, limit=1)
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return (not bool(c_pts)), (not bool(e_pts))
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except: return True, True
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def _purge_artifacts(self, note_id: str):
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"""Löscht verwaiste Chunks/Edges vor einem Re-Import."""
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from qdrant_client.http import models as rest
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f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
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for suffix in ["chunks", "edges"]:
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try: self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=rest.FilterSelector(filter=f))
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except: pass
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async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
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"""Hilfsmethode zur Erstellung einer Note aus einem Textstream."""
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target_dir = os.path.join(vault_root, folder)
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os.makedirs(target_dir, exist_ok=True)
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file_path = os.path.join(target_dir, filename)
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with open(file_path, "w", encoding="utf-8") as f:
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f.write(markdown_content)
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await asyncio.sleep(0.1)
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return await self.process_file(file_path=file_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True) |