"""Embedding-Job-Queue für Tier 3."""

import logging
import time
from hermes_memory.api.memory_api import MemoryAPI
from hermes_memory.tier2.schema import connect
from hermes_memory.config import load_config
from pathlib import Path

logger = logging.getLogger(__name__)


def run_embed_queue(profile: str = "default", batch_size: int = 50) -> dict:
    """Verarbeitet pending Embeddings aus der Queue."""
    config = load_config(profile)
    db_path = Path(config["tier2"]["db_path"].format(
        HERMES_HOME=Path.home() / ".hermes",
        profile=profile,
    ))
    conn = connect(db_path)
    rows = conn.execute(
        "SELECT id, fact_id, content, source_type, session_id, message_id FROM embedding_queue WHERE processed = 0 ORDER BY queued_at LIMIT ?",
        (batch_size,),
    ).fetchall()

    if not rows:
        return {"processed": 0}

    api = MemoryAPI(profile=profile)
    processed = 0
    for row in rows:
        try:
            api.semantic_index(
                text=row["content"],
                source_type=row["source_type"],
                session_id=row["session_id"],
                message_id=row["message_id"],
            )
            conn.execute("UPDATE embedding_queue SET processed = 1 WHERE id = ?", (row["id"],))
            processed += 1
        except Exception as e:
            logger.error("Embedding failed for queue id %s: %s", row["id"], e)

    conn.commit()
    return {"processed": processed}