feat(eval): filterable item metadata and dataset runs for the Experiments tab

The filter bar matches on metadata only — not on input and not on the item id —
so a dataset seeded with repo/pr in `input` alone could not be sliced by repo
at all. Every facet worth filtering on is now a flat primitive in `metadata`:
repo, owner, repo_name, pr, head_sha, finding_count, has_findings,
max_severity, reviews_run and the review timestamp both ways. `owner` is split
out because a filter on the joined repo matches one repo, never a whole org,
and `max_severity` is "none" rather than absent because an absent key matches
no filter.

`eval_experiment.py` links reviews that already ran into a dataset run, one run
per model, so the Experiments tab is populated without re-running the reviewer.
One trace per (run, item), the most recent: a PR re-reviewed on every push has
many traces and a run is one output per input.

It posts to the deprecated /api/public/dataset-run-items — the notice exempts
self-hosted v3 from the cutoff date and the pilot is stdlib-only by design.
Revisit at v4.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Marcos
2026-08-31 15:53:35 +00:00
parent 1534a99630
commit 2e1ad817e7
5 changed files with 535 additions and 8 deletions
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@@ -75,6 +75,58 @@ regression baseline: re-run a candidate model over these PRs and the diff
against this column is the behaviour change. Promoting an item to real ground
truth means a human editing it in the dataset view after re-reading the PR.
### Item ids
`{owner}__{repo}__pr{n}`. The obvious `{repo}#{pr}` cannot be used: items are
routed as `/datasets/{id}/items/{item_id}`, so the `/` in `owner/repo` splits
into extra path segments and everything after `#` is a fragment the browser
never sends — the item is created fine by the API and then 404s when opened.
Session ids elsewhere keep `{repo}#{pr}`; those are never path segments.
### Filterable metadata
The filter bar matches on `metadata` only — not on `input`, and not on the item
id — so every facet worth slicing on is a flat, primitive key in `metadata`
even where it duplicates `input`:
| key | why it is there |
| --- | --- |
| `repo`, `owner`, `repo_name` | `owner` exists because a filter on the joined `repo` matches one repo, never a whole org |
| `pr`, `head_sha` | jump from a filtered row back to the actual PR |
| `finding_count`, `has_findings` | isolate the silent reviews, which are the interesting negatives |
| `max_severity` | `"none"` rather than absent — an absent key matches no filter |
| `reviews_run` | how churny the PR was; high values skew per-item averages |
| `last_reviewed_at` / `_iso` | epoch sorts, ISO reads |
| `labelled_by_human` | `false` everywhere today; the flag to filter on before trusting any of it |
Nested objects and lists are deliberately absent: the filter bar cannot reach
into them.
`max_severity` is derived from `feedback.db`, whose `severity` column is
re-parsed out of the rendered comment by `feedback_harvest._parse_severity` and
defaults to `INFO` when its regex misses the badge. Trust the `severity_max`
**score** (read from the model's structured output) over this facet.
## Experiments
`eval_experiment.py` links reviews that already ran into a dataset run, so the
Experiments tab is populated without re-running anything. Runs are grouped by
model — the comparison the pilot actually needs is the same PRs under a
candidate model with `finding_rate` and `cost_per_finding` side by side. A new
model produces a new run automatically on the next invocation.
One trace per (run, item), the most recent: a PR re-reviewed on every push has
many traces, and a run is one output per input.
It uses `POST /api/public/dataset-run-items`, which is deprecated in favour of
the SDK experiment runner and disappears in Langfuse v4. The deprecation notice
exempts self-hosted v3 from the cutoff date, and this pilot is stdlib-only by
design. Revisit when this deployment moves to v4.
Coverage is bounded by the dataset, not by the traces: items only exist for PRs
with a row in `feedback.db`, and a review that posted no comment leaves a trace
but no row. That is why a run links fewer items than there are traces.
## Running it
```bash
@@ -83,6 +135,10 @@ python3 pilot/eval_bootstrap.py --db /data/feedback.db --backfill-traces
# ship feedback verdicts (runs daily from the feedback CronJob)
python3 pilot/feedback_scores.py --db /data/feedback.db
# link already-traced reviews into a dataset run per model
python3 pilot/eval_experiment.py --dry-run
python3 pilot/eval_experiment.py
```
Both need `LANGFUSE_HOST`, `LANGFUSE_PUBLIC_KEY`, `LANGFUSE_SECRET_KEY`. In