Adds an Ollama-based content quality classifier and exposes the
results via the search API. 1,407 existing Q/A pairs were scored
in 3.5h via qwen3:14b (1,405 succeeded, 2 failed).
Distribution: 37% scored 4-5 (useful), 41% scored 1-2 (banter/promo/
off-topic). 43% flagged as banter overall. Default-on filtering at
search time will hide ~half of the noise without losing any real
listener questions.
Files:
- new classify_qa_quality.py: walks qa_pairs, calls Ollama qwen3:14b
per row, writes usefulness_score/topic_class/is_banter back to DB.
Idempotent (--rebuild to reprocess), --smoke for sample check, --limit
for partial runs. Detached run handles 1407 rows in ~3.5h on a 4090.
- server/main.py: /api/search accepts min_score (0-5) and exclude_banter
query params. NULL scores treat as "include" so unprocessed rows still
appear. Episode detail endpoint includes the new fields in qa results.
Schema migration in import_to_sqlite.py was made by the same agent run
(visible on the live archive.db: usefulness_score / topic_class /
is_banter columns now exist on qa_pairs).
Local archive.db updated; Jupiter container has NOT been redeployed
yet — that is a separate manual step.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mike Swanson
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