Each in-flight (or stuck) embedding job gets a colored health pill,
relative-activity timestamp, and chunk counter so users can tell at a
glance whether ingestion is making progress.
## Health states
- **🟢 Active** — last batch < 2 min ago
- **🟡 Slow** — last batch 2-5 min ago (CPU-paced multi-batch ingestion
lives here naturally; not always a problem)
- **🔴 Stalled** — last batch > 5 min ago (likely real problem)
- **⚪ Waiting** — queued, no batch started yet
- **🔴 Failed** — job recorded failed status
## What lands
- New backend util `kb_job_health.ts` with pure `computeJobHealth(input)`
decision function. Time-based thresholds (2 min / 5 min) inlined as
constants. 9 unit tests pin the boundaries.
- `EmbedJobWithProgress` gains `lastBatchAt`, `startedAt`, `chunks` —
already set by `EmbedFileJob.handle` on every batch transition, just
not previously surfaced through `listActiveJobs`.
- Frontend `kb_job_health_display.ts` maps each status to a Tailwind
dot color, label, and aria-label so backend and UI stay in sync.
- `ActiveEmbedJobs.tsx` renders the pill, "last activity Xs ago", and
chunk counter above each progress bar. Adds a manual Refresh button
and "Last updated Xs ago" line — the existing 2s/30s auto-poll
cadence in `useEmbedJobs` is left intact.
- Live tick at 5s keeps the relative timestamps current without
re-fetching from the API.
## Not in scope
- Per-card Cancel / Retry / Un-index — separate Phase 2 PR
- Conditional warnings A/B/C — separate Phase 2 PR
- Computing throughput rate (chunks/min) — needs ratio registry consumer
(Phase 2 follow-up); for now the pill answers the "is it stuck?"
question directly without a rate estimate.
Each continuation batch of a multi-batch ZIM embed runs as a fresh
BullMQ job, so handle() ran the hardcoded `safeUpdateProgress(job, 5)`
even when the file was already 100k articles into a 600k-article ZIM.
The UI gauge briefly dropped to 5% before the per-batch onProgress
callback caught up to the true overall percentage, reading as a
backward jump every time a new batch started.
Compute initialPercent from batchOffset / totalArticles when available,
falling back to 5 for single-batch files (uploaded PDFs, txts) where
totalArticles isn't set. Capped at 99 to leave headroom for the 100%
final-batch marker.
Follow-up to PR #880 (which fixed the 0-100% scaling during a batch
but still had the initial-frame regression).
Adds a persistent state machine for AI knowledge-base ingestion so the
scanner can distinguish "fully indexed", "user opted out", "failed", and
"stalled" from each other — none of which were derivable from the prior
binary "any chunks in Qdrant ⇒ embedded" check.
## What lands
- New table `kb_ingest_state` keyed by `file_path` with enum state column
(`pending_decision | indexed | browse_only | failed | stalled`).
Independent of `installed_resources` so it covers both curated downloads
and manually-uploaded KB files.
- New KV key `rag.defaultIngestPolicy` (string: `Always | Manual`).
Registered now but not consumed yet — JIT prompt + wizard step land in
Phase 3 of the RFC.
- `EmbedFileJob.handle` writes state on terminal outcomes:
- Success (final batch) → `indexed` + chunks count
- `UnrecoverableError` → `failed` + error message
- Retryable errors are left to BullMQ's existing retry path
- `scanAndSyncStorage` swaps the binary qdrant check for a state-aware
decision tree (see `decideScanAction`). Existing installs auto-backfill
on first scan: files with chunks in Qdrant but no state row become
`indexed`; new files start as `pending_decision`.
- `deleteFileBySource` drops the state row last, so removed files
disappear entirely instead of leaving an orphan that the next scan
would re-dispatch into nothing.
## What does NOT land here
- Ratio registry (separate PR) — needed for partial-stall detection and
cost estimates, but a separable concern.
- #880 follow-up initial-progress anchor (separate tiny PR).
- Phase 2 UI (status pill, per-card actions, conditional warnings).
- Phase 3 policy surfaces (wizard step, JIT prompt, guardrail modal).
- PR #886's bulk-action hookup — `_deletePointsBySource` / Re-embed All
/ Reset & Rebuild would also want to set state, but #886 isn't merged
yet; that wiring goes in a follow-up once #886 lands.
## Target
This is forward work for v1.40.0 (RFC #883). Branching off `rc` because
that's the current latest base and post-GA Jake will sync rc→dev; a
retarget at PR-open time is a fast-forward if requested.
## Tests
- 9 new unit tests for `decideScanAction` covering all five states plus
the no-row / chunks-present / chunks-missing combinations
- Type-check clean
- Smoke-tested end-to-end on NOMAD3 via hot-patch:
- Backfill: 5 ZIMs + 2 KB uploads with existing chunks in Qdrant all
came back `indexed` on first scan
- Pending dispatch: a video-only ZIM with no chunks (`lrnselfreliance`)
came back `pending_decision` and was correctly re-dispatched (Bull
deduped to its historical `:completed` jobId — bgauger's #886 fix
drains that)
- Delete hook: deleting a KB upload via `DELETE /api/rag/files`
removed both the disk file and the state row
Co-authored-by: Jake Turner <52841588+jakeaturner@users.noreply.github.com>
Before this change, the Active Downloads / Processing Queue UI showed the
ingestion progress gauge jumping wildly during multi-batch ZIM ingestion
(e.g. 5% → 88% → 27% → 5% → 56% → 36% over ~60 seconds for cooking SE).
Each continuation batch is a separate BullMQ job, and `EmbedFileJob.handle()`
reported `job.progress` in two different reference frames depending on
where it was in the batch lifecycle:
- During-batch (via the onProgress callback): 5% → 95% scaled across
"% through this batch's chunks"
- End-of-batch (just before dispatching the next): overwritten to
`(nextOffset / totalArticles) * 100` — % through the whole file
- Next continuation batch starts with progress = 5% explicitly, then
climbs through the per-batch range again
`listActiveJobs()` returns the latest active BullMQ job's progress. With
GPU-accelerated ingestion completing a batch every ~4 seconds, the UI
saw the jobId rotate constantly and the gauge whipsaw between the two
reference frames.
`totalArticles` was already wired through the EmbedFileJob params shape
and used end-of-batch — but RagService never actually populated it,
so any frame-scaling that depended on it silently fell back to the
per-batch range. Two fixes together:
1. `ZIMExtractionService.extractZIMContent()` now returns
`{ chunks: ZIMContentChunk[]; totalArticles: number }` instead of a
raw chunks array, surfacing `archive.articleCount` to the caller.
Single caller (rag_service) updated to destructure.
2. `RagService.processZimFile()` includes `totalArticles` in its result
so `EmbedFileJob.dispatch()` can propagate it to the continuation
batch (which the existing code already does via
`totalArticles: totalArticles || result.totalArticles`).
3. `EmbedFileJob`'s onProgress callback scales the service-reported
per-batch percent into the overall-file frame when `totalArticles`
is known: `((batchOffset + (percent/100) * ZIM_BATCH_SIZE) /
totalArticles) * 100`. Capped at 99% to leave room for the explicit
100% set at file completion. Falls back to the original 5-95% range
for single-batch files (uploaded PDFs/txts) where totalArticles is
undefined — the gauge then represents % through the only batch,
which is what the UI expects for one-shot files.
Validated on NOMAD8 (RX 6800, ROCm-accelerated nomic):
- devdocs python (small, ~1500 articles): batch progressions seen
monotonically across continuation jobIds:
1501@30% → 1510@33% → 1514@43% → 1518@52%.
- ifixit (huge, ~100k articles): stays near 3% for the first many
batches at offset 0..3000 — correct, the file is enormous.
- wikipedia_en_medicine (large, ~70k articles): stays near 0-1% for
the first batches — also correct.
- Brief 0-5% blip on continuation handoff (the explicit
`safeUpdateProgress(job, 5)` at batch start, before the first
onProgress callback fires) — visible but quickly resolves to the
overall-frame value. No more 5% ↔ 88% chaos.
Stacks on top of the multi-batch ZIM ingestion fix. After that fix,
multi-batch ZIM ingestion completes correctly — but on installs where
Ollama runs the embedding model on CPU (currently every AMD ROCm
install, since Ollama's ROCm build doesn't accelerate nomic-bert),
the now-correct sustained 100% CPU saturation across all cores can
starve other services hard enough to take the box down. Confirmed
on a Threadripper 3960X + RX 6800 NOMAD: a wikipedia-class ZIM
ingestion pegged 48 threads cleanly enough that sshd lost
banner-exchange responsiveness and the box ultimately required a
power-cycle.
NVIDIA installs aren't affected — nomic-embed-text:v1.5 runs at
100% GPU on RTX 5060 (verified via `ollama ps`).
Detect placement at runtime, pace only when needed:
1. OllamaService.isEmbeddingGpuAccelerated() — queries /api/ps and
returns true if any loaded embedding model reports size_vram > 0.
Fails closed (returns false) if /api/ps is unreachable or no embed
model is loaded yet — over-pacing is safer than crashing.
2. EmbedFileJob.handle() — between batches (hasMoreBatches: true
branch), check placement and `await setTimeout(CPU_BATCH_DELAY_MS)`
when CPU-only. CPU_BATCH_DELAY_MS = 1000 (1s) — enough to give the
OS scheduler a window for sshd/disk-collector/etc., small enough
that total ingestion time isn't meaningfully affected (each batch
is ~60-90s of work).
GPU-accelerated installs see zero behavior change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Every static call site instantiated a fresh QueueService (24 call sites
across 8 files). QueueService.getQueue() opens a BullMQ Queue per call
when not cached, and each Queue opens two ioredis connections (one for
commands, one blocking). Because every static call constructed a new
QueueService, its internal `queues` cache was never shared, every call
opened a fresh pair, and none were ever closed.
In normal operation this leaked a few connections per API hit. During
multi-batch ZIM ingestion after PR #872 (where EmbedFileJob.handle()
dispatches the next batch every 50 articles), every batch completion
opened two new connections. On NOMAD3 at ~one batch every 4s sustained,
that's ~1800 leaked connections/hour. Redis hit its 10,000-maxclient
ceiling in ~5 hours and the admin container fell into an EPIPE flood
that required a restart to recover.
Fix: collapse QueueService to a true process-wide singleton with a
private constructor and getInstance() accessor. The existing per-queue
Map is now shared across every dispatch / status / cleanup call, so each
queue's underlying connections are opened exactly once for the lifetime
of the process. close() now clears the map so the singleton can be torn
down cleanly if a graceful-shutdown hook is ever wired up.
Validated on NOMAD3 (RTX 5060, v1.32.0-rc.4 + this patch hot-applied):
under sustained multi-batch wikipedia_en_simple_all_nopic ingestion,
connected_clients held flat at 21-22 across a 5-minute window. Pre-fix
the same scenario climbed to 10,000+ over hours.
EmbedFileJob.dispatch() uses a deterministic per-file jobId
(sha256(filePath).slice(0,16)) for every batch. The parent batch's
handle() calls EmbedFileJob.dispatch({ batchOffset }) before returning,
so the parent is still in `active` state and locked when the
continuation tries to enqueue. BullMQ silently returns the locked
parent instead of creating a new job — and in newer BullMQ versions
it does so without throwing, so the existing
`catch (error.message.includes('job already exists'))` branch never
fires. After the parent completes, its entry stays in the `completed`
ZSET (held by `removeOnComplete: { count: 50 }`), continuing to trip
jobId dedupe for any subsequent re-dispatch attempts.
Result: every NOMAD install since 2026-02-08 (feat: zim content
embedding) with a multi-batch ZIM (wikipedia, cooking SE, ifixit,
lrnselfreliance, etc.) has only the first 50 articles indexed in
qdrant. The RAG feature has been silently degraded for ~3 months —
the user sees the file appear in their KB, qdrant accumulates ~50
articles' worth of vectors, and pagination quietly halts. No error
surfaces anywhere.
Fix: dispatch() skips the deterministic jobId for continuation batches
(batchOffset > 0), letting BullMQ auto-generate a unique one so each
batch stacks as an independent queue entry. Initial dispatches keep
the deterministic jobId so re-triggering an install (UI re-click,
sync rescan) remains idempotent. The existing 'job already exists'
branch is now gated on !isContinuation, since by construction
continuation batches will never hit dedupe.
Validated on NOMAD8 (RX 6800 / Threadripper 3960X, rc.3 + this patch):
devdocs_en_python (~1,500 chunks across multiple batches) correctly
paginates end-to-end. admin.log shows the expected sequence of
"Dispatched embedding job for file: X (continuation @ offset N)"
followed by "Starting embedding process for: X (batch offset: N)"
for each batch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When Ollama isn't installed, every ZIM download dispatches embedding jobs
that fail and retry 30x with 60s backoff. With many ZIM files downloading
in parallel, this exhausts Redis connections with EPIPE/ECONNRESET errors.
Two changes:
1. Don't dispatch embedding jobs when Ollama isn't installed (belt)
2. Use BullMQ UnrecoverableError for "not installed" so jobs fail
immediately without retrying (suspenders)
Closes#351
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Chris Sherwood
chriscrosstalk
Jake Turner
Jake Turner
Henry Estela