Gan, Jimmy
a91a1132c9
Deploy Dashboard (Dev) / deploy-dev (push) Successful in 2m10s
Introduce a standalone scheduled info-engine worker with SQLite persistence and expose read-only dashboard APIs/UI so curated intelligence items can be collected and viewed with minimal architecture changes. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
38 lines
1010 B
Python
38 lines
1010 B
Python
import asyncio
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import logging
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import os
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from collector import collect_sources
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from db import init_db, upsert_items
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from processor import process_items
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s")
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log = logging.getLogger(__name__)
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COLLECT_INTERVAL_SECONDS = int(os.environ.get("INFO_ENGINE_INTERVAL_SECONDS", "900"))
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async def run_once() -> int:
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raw_items = await collect_sources()
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processed = process_items(raw_items)
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inserted = await upsert_items(processed)
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return inserted
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async def main():
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await init_db()
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log.info("Info engine started (interval=%ss)", COLLECT_INTERVAL_SECONDS)
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while True:
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try:
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inserted = await run_once()
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log.info("Info engine cycle complete (inserted=%d)", inserted)
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except Exception as exc:
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log.error("Info engine cycle failed: %s", exc)
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await asyncio.sleep(COLLECT_INTERVAL_SECONDS)
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if __name__ == "__main__":
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asyncio.run(main())
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