ProjectTech4DevAI / ProjectTech4DevAI/kaapi-backend

Cleanup: Automated data retention system

Offen
#1,183 0 Kommentare 0 Reaktionen 1 zugewiesene Person Auf GitHub ansehen

@Prajna1999 arbeitet bereits daran.

Seit 04.9.2026.

Vorherrschende Sprache
Python
Sterne
18
Forks
10
Ø Merge
2 T. 20 Std.
Gemergte PRs (30 T.)
14

Beschreibung

Is your feature request related to a problem?
We need a system to automatically clean up query and response data in the LLM calls table after a specified retention period. Without this, the database may become cluttered and inefficient.

Describe the solution you'd like

  • Implement a cron job to purge query/response data after a configurable retention window (default 1 week).
  • Handle one-time backlog separately from regular cron operations.
  • Write a cleanup strategy document detailing cron design, batching, and backlog handling.
  • Conduct a staging load test by duplicating ~20k LLM call rows ~5x in the copy_dev database to measure cleanup time. Do not use production data.
  • Ensure S3 cleanup is managed separately and not included in the DB cron/migration code.
Original issue

Context

query + response data in the LLM calls table will be cleaned up via a cron job after a configurable retention window (starting with 1 week).

A step-by-step cleanup strategy doc is to be written (cron design, batching, one-time backlog handling).

Scope / Acceptance criteria

  • Cron job to purge query/response data in the LLM calls table after a configurable retention window (default 1 week).
  • Handle one-time backlog separately from steady-state cron.
  • Write cleanup strategy doc (cron design, batching, backlog).
  • Staging load test: reuse the empty copy_dev database, duplicate staging's ~20k LLM call rows ~5x to reach ~1 lakh, and measure cleanup time. Do NOT pull prod data.
  • Keep S3 cleanup separate — do not bundle S3 deletion into the DB cron/migration code.

Notes

  • Guardrails input/output data is retained separately (anonymized, tagged as product enhancement) and is NOT part of this cleanup.

Beitragsleitfaden

Beitragsleitfaden öffnen

Erste Schritte

  1. Lies das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreib ins Issue, dass du es übernimmst — das erspart doppelte Arbeit.
  3. Forke das Repository und arbeite in einem Branch.
  4. Öffne einen Pull Request, der die Issue-Nummer nennt.

Bewertung

Dieses Issue wurde noch nicht bewertet.

Neue Issues direkt in Ihr Postfach

Eine kurze Übersicht über anfängerfreundliche GitHub-Issues.