"""
Ingest PDFs/TXT files from ./train_data into the persistent ChromaDB store.

Usage:
    AZURE_API_KEY=... .venv/bin/python ingest_train_data.py

The script reuses the optimized chunking, batching, and metadata logic from
app.py so the local ingestion matches the API retrieval behavior.
"""

import logging
import sys

from app import TRAIN_DOCS_DIR, collection, ingest_train_documents


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


def main() -> int:
    if not TRAIN_DOCS_DIR.exists():
        logger.error("Missing %s. Put the corpus PDFs/TXT files there first.", TRAIN_DOCS_DIR)
        return 1

    before = collection.count()
    if before > 0:
        logger.info("Vector store already contains %s chunks; skipping ingestion.", before)
        return 0

    ingest_train_documents()
    after = collection.count()
    logger.info("Ingestion finished. Chunks in vector store: %s", after)
    return 0 if after > 0 else 1


if __name__ == "__main__":
    sys.exit(main())