fix(backend): seed E2E embeddings before CI dumps the database for its cache - #14454
fix(backend): seed E2E embeddings before CI dumps the database for its cache#14454Pwuts wants to merge 3 commits into
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…s cache The fullstack CI job caches a pg_dump of the seeded E2E database, but the dump fires the instant seeding finishes while the scheduler's embedding backfill is still running, so it captured 804 of ~6,000 embeddings. Every cache hit then re-ran the backfill it was supposed to skip. Drive coverage to 100% from the seeder itself, through the same backfill_all_content_types the scheduler calls, before the step that dumps. Polling the scheduler's own pass would not do: it starts before the store agents exist, so it completes without them. Bounded by a deadline and by a no-progress check, and skipped entirely when no embedding backend is configured, so the preview-seed job — which has no OpenAI key — is unaffected and a broken backfill cannot hang CI. Co-authored-by: Claude Opus 5 (Claude Code) <noreply@anthropic.com>
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WalkthroughThe E2E data seeder now backfills missing content embeddings after refreshing materialized views. The process uses configurable batch and timeout values, checks progress, and exits on errors, stalls, or completed coverage. Unit tests cover these paths. ChangesE2E embedding backfill
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to E2E database seeding now completes embedding backfill before caching dumps, improving cached search coverage while bounding stalled operations. No concrete current-head merge-blocking risk remains. Sequence Diagram(s)sequenceDiagram
participant TestDataCreator
participant OpenAIClient
participant EmbeddingStats
participant ContentBackfill
TestDataCreator->>OpenAIClient: Check embedding backend
TestDataCreator->>EmbeddingStats: Read embedding coverage
TestDataCreator->>ContentBackfill: Backfill one configured batch
ContentBackfill-->>TestDataCreator: Return progress totals
TestDataCreator->>EmbeddingStats: Recheck coverage
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get_embedding_stats returns without_embeddings=0 with an "error" key when it fails, which the wait loop would read as 100% coverage and cache a dump with no embeddings in it. Check the key the way ensure_embeddings_coverage already does. Co-authored-by: Claude Opus 5 (Claude Code) <noreply@anthropic.com>
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## dev #14454 +/- ##
==========================================
- Coverage 81.48% 81.48% -0.01%
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Files 3553 3553
Lines 265702 265704 +2
Branches 24616 24617 +1
==========================================
- Hits 216518 216497 -21
- Misses 43738 43763 +25
+ Partials 5446 5444 -2
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🤖 Measured: cold runRun 34285889326 on
The seeder log shows the loop draining to zero and stopping: Two things worth pulling out of that table. The Playwright suite got 43 s faster, which was not the goal. On The cache entry grew to 29 MiB, not the 60–90 MiB I estimated in the description. Each row carries a 1536-dim vector, so I sized it from the vector text; The warm-run half — restoring this cache and confirming the scheduler logs |
🤖 Measured: warm run — the backfill is skippedRe-ran 34285889326 so it would restore the cache its first attempt had just written. It hit, and the scheduler found nothing to do:
The 23 are Comparing the two job totals across the last two rows is not clean — they are different commits with different Docker layer-cache states, and 28 s is inside that noise. The load-bearing evidence is the log line and the 2,087 → 23 drop, not the wall clock. Where that leaves the three runs
A cold run costs 2 m 39 s more and produces a cache that is actually worth restoring. A warm run does no backfill at all, which is what this PR set out to do. The caveat from the description still stands and is the thing to weigh: at the measured 10% hit rate, most runs pay the cold cost and few collect the warm benefit. That ratio is set by cache eviction, not by anything here. |
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Inline comments:
In `@autogpt_platform/backend/test/e2e_test_data.py`:
- Line 1313: Update the embedding backfill flow around get_embedding_stats() and
backfill_all_content_types() to apply the remaining configured deadline to both
awaits, treating timeout cancellation as an incomplete backfill. Preserve normal
completion behavior, and add an E2E test using a hanging helper that verifies
seeding stops within EMBEDDING_BACKFILL_TIMEOUT_SECONDS.
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autogpt_platform/backend/test/e2e_test_data.pyautogpt_platform/backend/test/test_e2e_test_data_embeddings.py
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Format Python code with `poetry run format`
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Files:
autogpt_platform/backend/test/e2e_test_data.pyautogpt_platform/backend/test/test_e2e_test_data_embeddings.py
🧠 Learnings (1)
📚 Learning: 2026-03-05T15:42:08.207Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 12297
File: .claude/skills/backend-check/SKILL.md:14-16
Timestamp: 2026-03-05T15:42:08.207Z
Learning: In Python files under autogpt_platform/backend (recursively), rely on poetry run format to perform formatting (Black + isort) and linting (ruff). Do not run poetry run lint as a separate step after poetry run format, since format already includes linting checks.
Applied to files:
autogpt_platform/backend/test/test_e2e_test_data_embeddings.py
🪛 Ruff (0.16.3)
autogpt_platform/backend/test/test_e2e_test_data_embeddings.py
[warning] 48-48: Do not perform function call object in argument defaults; instead, perform the call within the function, or read the default from a module-level singleton variable
(B008)
Checking the clock between the two awaits bounded nothing: neither get_embedding_stats nor backfill_all_content_types was itself limited, and one stuck embedding call is 600s x 3 attempts under the OpenAI client's defaults — twice the whole 900s deadline — with gather waiting on all 100 in the batch. A stall there held E2E seeding open indefinitely. Wrap both in asyncio.wait_for with the remaining budget and treat a timeout as an incomplete backfill, on the same warn-and-continue path as before so the dump still happens. TimeoutError only: catching CancelledError would swallow an external cancellation. The two new tests hang until cancelled rather than returning early, and fail with TimeoutError when the wait_for calls are removed. Co-authored-by: Claude Opus 5 (Claude Code) <noreply@anthropic.com>
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Why / What / How
Makes the E2E test-data cache carry the search embeddings, so a cache hit no longer re-runs the embedding backfill it was supposed to skip.
The fullstack CI job seeds a database for the Playwright suite and
pg_dumps it into the Actions cache. The dump fires the instant seeding finishes, while the scheduler's startup backfill is still generating embeddings — so it captures whatever happened to be written by that moment. On run 34269702623 (dev, 2026-09-08) that was 804 of ~6,000 embeddings; 2,408 more were generated after the dump and thrown away. A later run that restores that cache still finds 4,823 items missing (19.7% coverage, documentation at 7.5%) and backfills them again.This drives coverage to 100% from the seeder itself, before the step that dumps, using the same
backfill_all_content_typesthe scheduler calls. Polling the scheduler's own pass instead would not work: it starts before the store agents exist, so it completes without them.Measured on this PR's own runs: a cache hit now generates 23 embeddings instead of 2,087 and the scheduler logs
All content has embeddings, skipping backfill; a cold run costs 2 m 39 s more and produces a dump worth restoring. Numbers and method in the two comments below.Read the cost section before the wall-clock claim. The measured cache hit rate is 10% — 2 of the last 20 runs — and the cause is cache eviction, not this bug. Until that is fixed, most runs pay the cold cost and few collect the warm benefit.
Changes 🏗️
test/e2e_test_data.pygains a finalbackfill_content_embeddings()phase that loopsbackfill_all_content_typesuntilget_embedding_stats()reports zero missing.E2E_EMBEDDING_TIMEOUT_SECONDS, default 900), a no-progress check that gives up when a whole batch fails, and a skip when no embedding backend is configured. On timeout it emits a::warning::and returns, so the dump still happens rather than CI hanging.E2E_EMBEDDING_BATCH_SIZE, default 100 — the value the scheduler already runs at against OpenAI's rate limits.get_embedding_statsreturnswithout_embeddings=0alongside anerrorkey when it fails, which would otherwise cache a dump with no embeddings and report success.No configuration changes: no new secret, no
.env.defaultordocker-compose.ymlchange. The two env vars are optional overrides with defaults in code. The seeder is also run byplatform-preview-seed-fixture.yml, which configures no OpenAI key — the skip-when-unconfigured guard means that job is unaffected.Two findings worth separating from this change
The cache key is not the problem, and
migrations/**is not why runs miss. The key was byte-identical (e2e-test-data-ffdd677f…) across every run sampled between 11:31Z and 21:46Z on 2026-09-08, and no migration landed ondevthat day — yet 18 of the last 20 runs missed. The cause is eviction: the repo holds 20 caches totalling 12 GiB against GitHub's 10 GiB limit, so entries turn over continuously, and every cache in the repo was created inside one 16-minute window. Nine ~1 GiB poetry caches dominate it (the same lockfile underpoetry-Linux-…,py3.11-…,py3.12-…,py3.13-…). The only survivinge2e-test-dataentry is scoped to agh-readonly-queue/…ref that no longer exists.So I have not touched the key. Removing
migrations/**would trade schema safety for no measured gain, andrestore-keyswould be worse — a partial match restores a dump built against a different schema. Making the poetry caches stop evicting everything else is what would raise the hit rate, and it belongs to whoever owns those keys.SCHEDULER_STARTUP_EMBEDDING_BACKFILL=falsein this workflow does nothing. Nothing in the codebase reads that name (grep -rn SCHEDULER_STARTUP_EMBEDDING_BACKFILLreturns only the workflow line); the startup backfill is gated onScheduler.register_system_tasks, which defaults toTrue. Anyone reading the workflow would reasonably conclude the backfill is off in CI. It is not — it runs on every run and, in both runs I measured, was still generating embeddings when the job ended. I have left the line alone rather than make it work, since the goal here is that the backfill has nothing to do, not that it is switched off.Honest cost
This moves embedding work onto the critical path: the seed step now waits for full coverage before dumping, where today that work happens later and overlaps the Playwright suite. Cold runs get longer; warm runs get shorter and stop racing the tests. The cached dump also grows, since each row carries a 1536-dim vector: 9.8 MiB to 29 MiB, measured.
At the measured 10% hit rate this is not yet a wall-clock win, and that claim stays contingent until the eviction above is fixed — a cache that does not survive cannot repay a cold-path cost. What it does buy on every run, hit or miss, is determinism: today the E2E suite runs against a database whose embeddings are ~40% complete and still changing underneath it.
Agents and large language models used
Claude Code with Claude Opus 5.
Checklist 📋
For code changes:
test/test_e2e_test_data_embeddings.py— 5 tests, all paths (no backend, already covered, converges, stalls, deadline)backend/util/architecture_test.py+test/targeted — 24 passedbackend/blocks/test/test_block.py— 1647 passed, 84 skippedFor configuration changes:
.env.defaultis updated or already compatible with my changesdocker-compose.ymlis updated or already compatible with my changes🤖 Generated with Claude Code