107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""
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scripts/import_markdown.py
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CLI-Tool zum Importieren von Markdown-Dateien in Qdrant.
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Updated for Mindnet v2.3.6 (Async Ingestion Support).
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"""
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import asyncio
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import os
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import argparse
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import logging
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from pathlib import Path
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from dotenv import load_dotenv
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import logging
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# Setzt das Level global auf INFO, damit Sie den Fortschritt sehen
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logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
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# Wenn Sie TIEFE Einblicke wollen, setzen Sie den SemanticAnalyzer spezifisch auf DEBUG:
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logging.getLogger("app.services.semantic_analyzer").setLevel(logging.DEBUG)
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# Importiere den neuen Async Service
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# Stellen wir sicher, dass der Pfad stimmt (Pythonpath)
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import sys
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sys.path.append(os.getcwd())
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from app.core.ingestion import IngestionService
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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logger = logging.getLogger("importer")
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async def main_async(args):
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vault_path = Path(args.vault).resolve()
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if not vault_path.exists():
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logger.error(f"Vault path does not exist: {vault_path}")
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return
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# Service initialisieren (startet Async Clients)
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logger.info(f"Initializing IngestionService (Prefix: {args.prefix})")
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service = IngestionService(collection_prefix=args.prefix)
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logger.info(f"Scanning {vault_path}...")
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files = list(vault_path.rglob("*.md"))
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# Exclude .obsidian folder if present
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files = [f for f in files if ".obsidian" not in str(f)]
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files.sort()
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logger.info(f"Found {len(files)} markdown files.")
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stats = {"processed": 0, "skipped": 0, "errors": 0}
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# Wir nutzen eine Semaphore, um nicht zu viele Files gleichzeitig zu öffnen/embedden
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sem = asyncio.Semaphore(5) # Max 5 concurrent files to avoid OOM or Rate Limit
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async def process_with_limit(f_path):
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async with sem:
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try:
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res = await service.process_file(
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file_path=str(f_path),
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vault_root=str(vault_path),
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force_replace=args.force,
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apply=args.apply,
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purge_before=True
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)
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return res
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except Exception as e:
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return {"status": "error", "error": str(e), "path": str(f_path)}
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# Batch Processing
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# Wir verarbeiten in Chunks, um den Progress zu sehen
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batch_size = 20
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for i in range(0, len(files), batch_size):
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batch = files[i:i+batch_size]
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logger.info(f"Processing batch {i} to {i+len(batch)}...")
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tasks = [process_with_limit(f) for f in batch]
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results = await asyncio.gather(*tasks)
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for res in results:
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if res.get("status") == "success":
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stats["processed"] += 1
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elif res.get("status") == "error":
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stats["errors"] += 1
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logger.error(f"Error in {res.get('path')}: {res.get('error')}")
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else:
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stats["skipped"] += 1
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logger.info(f"Done. Stats: {stats}")
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if not args.apply:
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logger.info("DRY RUN. Use --apply to write to DB.")
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def main():
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load_dotenv()
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default_prefix = os.getenv("COLLECTION_PREFIX", "mindnet")
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parser = argparse.ArgumentParser(description="Import Vault to Qdrant (Async)")
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parser.add_argument("--vault", default="./vault", help="Path to vault root")
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parser.add_argument("--prefix", default=default_prefix, help="Collection prefix")
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parser.add_argument("--force", action="store_true", help="Force re-index all files")
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parser.add_argument("--apply", action="store_true", help="Perform writes to Qdrant")
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args = parser.parse_args()
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# Starte den Async Loop
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asyncio.run(main_async(args))
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if __name__ == "__main__":
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main() |