#!/usr/bin/env python3
"""
scripts/import_markdown.py
CLI-Tool zum Importieren von Markdown-Dateien in Qdrant.
Updated for Mindnet v2.3.6 (Async Ingestion Support).
"""
import asyncio
import os
import argparse
import logging
from pathlib import Path
from dotenv import load_dotenv

# Importiere den neuen Async Service
# Stellen wir sicher, dass der Pfad stimmt (Pythonpath)
import sys
sys.path.append(os.getcwd())

from app.core.ingestion import IngestionService

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("importer")

async def main_async(args):
    vault_path = Path(args.vault).resolve()
    if not vault_path.exists():
        logger.error(f"Vault path does not exist: {vault_path}")
        return

    # Service initialisieren (startet Async Clients)
    logger.info(f"Initializing IngestionService (Prefix: {args.prefix})")
    service = IngestionService(collection_prefix=args.prefix)
    
    logger.info(f"Scanning {vault_path}...")
    files = list(vault_path.rglob("*.md"))
    # Exclude .obsidian folder if present
    files = [f for f in files if ".obsidian" not in str(f)]
    files.sort()
    
    logger.info(f"Found {len(files)} markdown files.")

    stats = {"processed": 0, "skipped": 0, "errors": 0}

    # Wir nutzen eine Semaphore, um nicht zu viele Files gleichzeitig zu öffnen/embedden
    sem = asyncio.Semaphore(5) # Max 5 concurrent files to avoid OOM or Rate Limit

    async def process_with_limit(f_path):
        async with sem:
            try:
                res = await service.process_file(
                    file_path=str(f_path),
                    vault_root=str(vault_path),
                    force_replace=args.force,
                    apply=args.apply,
                    purge_before=True
                )
                return res
            except Exception as e:
                return {"status": "error", "error": str(e), "path": str(f_path)}

    # Batch Processing
    # Wir verarbeiten in Chunks, um den Progress zu sehen
    batch_size = 20
    for i in range(0, len(files), batch_size):
        batch = files[i:i+batch_size]
        logger.info(f"Processing batch {i} to {i+len(batch)}...")
        
        tasks = [process_with_limit(f) for f in batch]
        results = await asyncio.gather(*tasks)
        
        for res in results:
            if res.get("status") == "success":
                stats["processed"] += 1
            elif res.get("status") == "error":
                stats["errors"] += 1
                logger.error(f"Error in {res.get('path')}: {res.get('error')}")
            else:
                stats["skipped"] += 1

    logger.info(f"Done. Stats: {stats}")
    if not args.apply:
        logger.info("DRY RUN. Use --apply to write to DB.")

def main():
    load_dotenv()
    default_prefix = os.getenv("COLLECTION_PREFIX", "mindnet")

    parser = argparse.ArgumentParser(description="Import Vault to Qdrant (Async)")
    parser.add_argument("--vault", default="./vault", help="Path to vault root")
    parser.add_argument("--prefix", default=default_prefix, help="Collection prefix")
    parser.add_argument("--force", action="store_true", help="Force re-index all files")
    parser.add_argument("--apply", action="store_true", help="Perform writes to Qdrant")
    
    args = parser.parse_args()
    
    # Starte den Async Loop
    asyncio.run(main_async(args))

if __name__ == "__main__":
    main()