# Session Log — 2026-04-29 — Q/A Quality Classifier (Track 1)

**Project:** The Computer Guru Show — Archive Mining System
**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
**Machine:** GURU-BEAST-ROG (RTX 4090)
**User:** Mike Swanson (mike)

---

## Session Summary

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.

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.

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%)**.

Commit `4268890` pushed to `main`. Jupiter Docker container has **NOT** been redeployed yet — Mike does that manually after reviewing.

---

## Final Distribution

```
SCORE     COUNT   SHARE
  5         362   25.8%   ← clear computer help
  4         161   11.5%
  3         309   22.0%   ← borderline / general advice
  2         257   18.3%
  1         316   22.5%   ← pure banter / promo

useful (4-5):  523  (37.3%)
noise (1-2):   573  (40.8%)

TOPIC_CLASS:
  computer-help  928  66.0%
  banter         248  17.7%
  off-topic      147  10.5%
  promo           78   5.6%
  unclear          4   0.3%

is_banter=True: 606  (43.1%)
```

About 40% of existing Q/A pairs are noise that can be filtered out at search time without losing any real listener questions.

---

## Spot Check (manual review of 6 rows)

**Score 1, banter (correctly flagged):**
- "We've got some options... we're open until 6 today and Monday" — business-hours chitchat
- "How are you? Why would you unfriend some people?" — Facebook social talk
- "I've been looking for something... eradic[ate]..." — fragmentary

**Score 5, computer-help (correctly flagged):**
- "It's all dependent on the actual hardware, right? The actual TV..." — TV/hardware Q
- "But, you know, if you put it on a drive render, can you get my data off..." — data recovery
- "AZ Computer Guru on there also..." — antivirus/Avast advice

Classifier output matches human judgment on these samples.

---

## Key Decisions

- **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.
- **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.
- **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.
- **NULL handling** in the WHERE clause: `(usefulness_score IS NULL OR usefulness_score >= :min_score)` — unprocessed rows stay visible during incremental rollout.

---

## Problems Encountered

- **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.
- **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.
- **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.

---

## Files Changed

| Path | Change |
|---|---|
| `projects/radio-show/audio-processor/classify_qa_quality.py` | NEW — 19 KB Ollama-based classifier with --smoke / --rebuild / --limit flags, batch-25 commits |
| `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 |
| `projects/radio-show/audio-processor/import_to_sqlite.py` | (per Coding Agent's diff — schema migration adds 3 columns to qa_pairs) |
| `projects/radio-show/audio-processor/archive-data/archive.db` | Updated with all 1,405 classifications (NOT in git, ships separately to Jupiter) |
| `projects/radio-show/audio-processor/logs/classify.log` | Final run output (rich progress + summary table) |

---

## Commands Used

```powershell
# Detached classifier launch
Start-Process -FilePath .venv\Scripts\python.exe `
              -ArgumentList "-u", "classify_qa_quality.py" `
              -WorkingDirectory <path> `
              -RedirectStandardOutput logs\classify.log `
              -RedirectStandardError logs\classify.err `
              -WindowStyle Hidden -PassThru
```

```bash
# Progress poll
.venv/Scripts/python.exe -c "
import sqlite3
db = sqlite3.connect('archive-data/archive.db')
print(db.execute('SELECT COUNT(*) FROM qa_pairs WHERE usefulness_score IS NOT NULL').fetchone())
"
```

---

## Pending / Next

1. **Deploy to Jupiter** (manual — Mike's call):
   - `pscp archive-data/archive.db root@172.16.3.20:/mnt/user/appdata/radio-archive/data/archive.db`
   - `pscp server/main.py root@172.16.3.20:/mnt/user/appdata/radio-archive/app/`
   - `ssh root@172.16.3.20 "cd /mnt/user/appdata/radio-archive/app && docker compose build && docker compose up -d"`
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.
3. **Re-run the 2 failed rows** (one-liner: `classify_qa_quality.py` will retry NULLs automatically next invocation).
4. **Track 2 (voice profile clustering)** — still deferred. Lower priority now that content quality is solved.

---

## Reference

- **Search with quality filter:** `curl 'http://172.16.3.20:8765/api/search?q=BIOS&kind=qa&min_score=4'`
- **Without banter:** `curl 'http://172.16.3.20:8765/api/search?q=ssd&kind=qa&exclude_banter=true'`
- **Default behavior unchanged** when `min_score=0` (the default).
- **Re-classify:** `classify_qa_quality.py --rebuild` reprocesses everything (don't run unless prompt changes).
- **Spot check:** `classify_qa_quality.py --smoke` runs 10 sample rows and prints classifications without writing to DB.

---

## Status at session end

- Classifier: COMPLETE (1,405/1,407, 2 failed → NULL → will retry on next invocation)
- API: read-ready (uvicorn imports cleanly, all routes intact)
- DB: updated locally with all classifications
- Git: commit `4268890` pushed to main
- Jupiter: NOT redeployed (manual step pending Mike's review)
