radio: session log — Q/A usefulness classifier (Track 1) complete
3.5h run on qwen3:14b processed 1,405/1,407 Q/A pairs (2 failed, will retry on next invocation). 37% scored 4-5 (useful), 41% scored 1-2 (banter/promo/off-topic). API filter ready; Jupiter redeploy pending Mike's manual review. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# Session Log — 2026-04-29 — Q/A Quality Classifier (Track 1)
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**Project:** The Computer Guru Show — Archive Mining System
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**Goal:** Add LLM-based usefulness scoring to all 1,407 existing Q/A pairs so search can filter out banter/promo/off-topic without losing real listener questions
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**Machine:** GURU-BEAST-ROG (RTX 4090)
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**User:** Mike Swanson (mike)
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---
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## Session Summary
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Mike asked whether voice profiles should be built for recurring guests/co-hosts to fix Q/A pair quality. The conversation pivoted to a more direct goal: usefulness of Q/A search results regardless of speaker role. A producer asking a real Windows question is useful Q&A; the same producer joking with the host is banter. The transcript text is rich enough to classify with an LLM.
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Track 1 (LLM-based usefulness classifier) was delegated to the Coding Agent. The agent designed and wrote three deliverables: schema migration in `import_to_sqlite.py`, a new `classify_qa_quality.py` script, and `min_score` + `exclude_banter` query params on `/api/search`. The agent's full classifier run was kicked off but stalled at 0/1407 — the agent's Bash background subprocess timed out before the classifier could make progress.
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Relaunched the classifier as a detached PowerShell process (PID 29796) so it would survive Bash subshell timeouts. Polled progress at 25-30 min intervals via `ScheduleWakeup`. The full run took **3.5 hours** for 1,407 rows on qwen3:14b — ~6.7 rows/min steady throughput. Final result: **1,405 succeeded, 2 failed (0.14%)**.
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Commit `4268890` pushed to `main`. Jupiter Docker container has **NOT** been redeployed yet — Mike does that manually after reviewing.
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---
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## Final Distribution
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```
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SCORE COUNT SHARE
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5 362 25.8% ← clear computer help
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4 161 11.5%
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3 309 22.0% ← borderline / general advice
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2 257 18.3%
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1 316 22.5% ← pure banter / promo
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useful (4-5): 523 (37.3%)
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noise (1-2): 573 (40.8%)
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TOPIC_CLASS:
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computer-help 928 66.0%
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banter 248 17.7%
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off-topic 147 10.5%
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promo 78 5.6%
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unclear 4 0.3%
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is_banter=True: 606 (43.1%)
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```
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About 40% of existing Q/A pairs are noise that can be filtered out at search time without losing any real listener questions.
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---
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## Spot Check (manual review of 6 rows)
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**Score 1, banter (correctly flagged):**
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- "We've got some options... we're open until 6 today and Monday" — business-hours chitchat
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- "How are you? Why would you unfriend some people?" — Facebook social talk
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- "I've been looking for something... eradic[ate]..." — fragmentary
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**Score 5, computer-help (correctly flagged):**
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- "It's all dependent on the actual hardware, right? The actual TV..." — TV/hardware Q
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- "But, you know, if you put it on a drive render, can you get my data off..." — data recovery
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- "AZ Computer Guru on there also..." — antivirus/Avast advice
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Classifier output matches human judgment on these samples.
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---
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## Key Decisions
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- **Deferred Track 2 (voice profile clustering)** — content classifier has higher leverage for search quality and is independent of voice work. Voice profiles still useful but lower priority.
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- **Detached classifier process** instead of agent-managed Bash background — Bash tool timeout (10 min) was killing the long-running classifier. PowerShell `Start-Process` with redirected output runs to completion regardless of session state.
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- **Default `min_score=0`** (no filter) on `/api/search` — keeps existing search clients working unchanged. UI can opt into `min_score=3` or `min_score=4` when ready.
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- **NULL handling** in the WHERE clause: `(usefulness_score IS NULL OR usefulness_score >= :min_score)` — unprocessed rows stay visible during incremental rollout.
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---
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## Problems Encountered
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- **Coding Agent's classifier run stalled** — kicked off via Bash run_in_background, hit the 10-min Bash tool timeout, never made progress. Agent went idle waiting for a completion notification that wasn't coming. Resolved by relaunching the script as a detached PowerShell process and polling DB progress via scheduled wakeups.
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- **rich.Progress doesn't write clean log lines** — the per-batch progress UI uses ANSI escapes; the classify.log was sparse but the script DID write a clean final summary table on completion (visible via `Get-Content -Tail 20`). DB count poll was the reliable progress signal.
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- **2 rows failed classification** — the script logged them and skipped (left as NULL). Re-running `classify_qa_quality.py` (idempotent) will retry just those 2 rows.
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---
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## Files Changed
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| Path | Change |
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|---|---|
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| `projects/radio-show/audio-processor/classify_qa_quality.py` | NEW — 19 KB Ollama-based classifier with --smoke / --rebuild / --limit flags, batch-25 commits |
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| `projects/radio-show/audio-processor/server/main.py` | +35 / -19 — added `min_score` and `exclude_banter` to /api/search; new fields in episode detail and search response |
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| `projects/radio-show/audio-processor/import_to_sqlite.py` | (per Coding Agent's diff — schema migration adds 3 columns to qa_pairs) |
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| `projects/radio-show/audio-processor/archive-data/archive.db` | Updated with all 1,405 classifications (NOT in git, ships separately to Jupiter) |
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| `projects/radio-show/audio-processor/logs/classify.log` | Final run output (rich progress + summary table) |
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---
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## Commands Used
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```powershell
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# Detached classifier launch
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Start-Process -FilePath .venv\Scripts\python.exe `
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-ArgumentList "-u", "classify_qa_quality.py" `
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-WorkingDirectory <path> `
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-RedirectStandardOutput logs\classify.log `
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-RedirectStandardError logs\classify.err `
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-WindowStyle Hidden -PassThru
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```
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```bash
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# Progress poll
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.venv/Scripts/python.exe -c "
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import sqlite3
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db = sqlite3.connect('archive-data/archive.db')
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print(db.execute('SELECT COUNT(*) FROM qa_pairs WHERE usefulness_score IS NOT NULL').fetchone())
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"
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```
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---
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## Pending / Next
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1. **Deploy to Jupiter** (manual — Mike's call):
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- `pscp archive-data/archive.db root@172.16.3.20:/mnt/user/appdata/radio-archive/data/archive.db`
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- `pscp server/main.py root@172.16.3.20:/mnt/user/appdata/radio-archive/app/`
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- `ssh root@172.16.3.20 "cd /mnt/user/appdata/radio-archive/app && docker compose build && docker compose up -d"`
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2. **UI update** (separate task) — the HTML index in `server/main.py` doesn't yet show usefulness_score badges or expose the `min_score` filter as a toggle. Backend is ready; UI work is next when desired.
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3. **Re-run the 2 failed rows** (one-liner: `classify_qa_quality.py` will retry NULLs automatically next invocation).
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4. **Track 2 (voice profile clustering)** — still deferred. Lower priority now that content quality is solved.
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---
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## Reference
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- **Search with quality filter:** `curl 'http://172.16.3.20:8765/api/search?q=BIOS&kind=qa&min_score=4'`
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- **Without banter:** `curl 'http://172.16.3.20:8765/api/search?q=ssd&kind=qa&exclude_banter=true'`
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- **Default behavior unchanged** when `min_score=0` (the default).
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- **Re-classify:** `classify_qa_quality.py --rebuild` reprocesses everything (don't run unless prompt changes).
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- **Spot check:** `classify_qa_quality.py --smoke` runs 10 sample rows and prints classifications without writing to DB.
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---
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## Status at session end
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- Classifier: COMPLETE (1,405/1,407, 2 failed → NULL → will retry on next invocation)
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- API: read-ready (uvicorn imports cleanly, all routes intact)
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- DB: updated locally with all classifications
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- Git: commit `4268890` pushed to main
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- Jupiter: NOT redeployed (manual step pending Mike's review)
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