There was one cutoff, Qdrant's score_threshold at 0.3, and nomic scores
unrelated text well above it, so every question retrieved *something*
(nonEmptyRateOnRefusal 1.0) and rag_context wasted four lines asking a 3B
model to "silently judge" relevance. That is a classification a number
should decide, not a micro model.
Adds `RAG_MIN_FINAL_SCORE`, a floor on the post-rerank score, applied before
source diversity. The floor judges relevance, the 0.85^n penalty judges
redundancy. Nothing clears it and nothing is injected. `score_threshold` stays
at 0.3 as the candidate net. Exposed as `rag.minRelevance` with a module-
scoped TTL cache invalidated from `updateSetting`.
The floor is calibrated and the harness was describing the wrong number:
aggregate() bucketed semanticScore, so its percentiles measured raw cosine
while the new knob filters the reranked score. Both axes now, and the gap
matters: recall falls at 0.60 on the semantic axis, 0.66 on the final one.
floor recall@5 precision@5 emptyOnAnswerable nonEmptyOnRefusal
0.00 0.991 0.221 0% 100%
0.62 0.991 0.524 0% 60%
0.66 0.991 0.695 0% 60%
0.67 0.972 0.704 1% 60%
Default 0.62, not the last-lossless 0.66: the refusal win is complete at
0.62 and 28 documents is a thin basis for spending margin. 0.60 is the
honest floor. Three of five refusal goldens are near-corpus by
construction, where declining is the generation tier's job.
Both eval tiers pin the floor to the constant and ignore the setting, for
the same reason the harness never sets skipRetrieval.
4 metrics improved against baseline, no regressions.
Ollama's format parameter turns a formatting request into a decoder
constraint, and NOMAD's three ancillary calls — chat title, suggestion
chips, RAG query rewrite — were the ones paying for not having it.
The rewrite is particularly important: whatever it returned got embedded
and sent to Qdrant, so a malformed response degraded retrieval for the
rest of the conversation.
Three explicit design choices made:
- Native-only. The compat path sends nothing new and keeps its string
parsing, so a non-Ollama backend that rejects response_format can't
take out title generation entirely.
- parseStructured returning null is the control flow. It's how each
caller falls back to the parser it already had, which is why it's written
to be total and never throw.
- {queries: []} not {query: ""} for the rewrite (tee-ing up future
improvements)
Chat was run through Ollama's OpenAI-compatible /v1 endpoint, which has no
context-size field, so the num_ctx we sent was silently discarded and the
entire NUM_CTX_LADDER was dead code. Every conversation ran at the backend
default and was truncated from the middle (where history and
retrieved context actually sit). Measured with identical ~7k-token prompts:
2,060 tokens processed against 7,049 on the native endpoint, and with the
needed fact in the discarded middle the old path hallucinated a plausible
wrong number.
Transport: add a native /api/chat path using the already-installed ollama
package, sending a real options object (num_ctx, num_predict, temperature,
seed) plus keep_alive. The OpenAI path is still used for LM Studio and llama.cpp,
minus the ineffective num_ctx and with include_usage so both paths return
prompt token counts. /api/show is now read for trained context, parameter
size, quantization and any modelfile num_ctx. The <think> stream normalizer
moves to app/utils/think_stream.ts so both transports share it; it gains a
flush() that fixes a dangling partial tag being dropped at end of stream.
Token accounting: replace chars/N with a structural segmenter scaled by a
per-model factor learned by EWMA from the prompt_eval_count every response
already returned and threw away. End-to-end estimator error is 4.6% against
18-20% for a fixed divisor — qwen2.5 converges to k=1.26 where llama3 sits
at 1.00, so no single global constant serves both.
Context window: resolved once per model from trained context, the exact KV
cost derived from GGUF metadata (head_count_kv, not head_count — GQA makes
them differ 4x), and available VRAM, then memoized. Memoization is
load-bearing: asking for a different num_ctx makes Ollama unload and reload
the model, stalling the turn and discarding the KV cache. The floor never
overrides the model's trained ceiling — tinyllama trains to 2048, and a
blind 4096 floor pushes RoPE past anything the weights saw.
Prompt budget: planPrompt allocates explicitly instead of delegating
overflow to silent backend truncation — response reserve, then fixed blocks
(system and current query, never dropped), then RAG chunks whole and
best-first, then history in whole user+assistant turns, evicted newest-first
in blocks with an elision marker. Block eviction keeps the prefix stable for
several turns rather than shifting on every one.
KV-cache ordering: the per-turn retrieved block moves behind history to the
tail, so the stable prefix stays byte-identical turn to turn. At turn 14 of
a long conversation, prefill is 231ms at the tail against 539ms at the front
(~1ms/turn growth against ~25ms). The win is conditional — with a large
retrieved block and short history the two measure the same — so both
orderings ship behind RAG_PLACEMENT. The per-turn query rewrite also moves
to the tasks model; on a single-slot Ollama it was evicting the chat model's
cached prefix on every single turn.
Also: OLLAMA_CONTEXT_LENGTH and OLLAMA_KV_CACHE_TYPE=q8_0 on the container
(q8_0 is treated as headroom, never as a budgeting assumption, since
unsupported architectures fall back to f16 silently), a Context Window
setting in Settings > Models, and budget fields on PipelineTrace.
Eval: golden suites (--suite) so the new long_context goldens cannot move
the aggregates the committed baselines were recorded against, plus budget
and estimator-error metrics through the report path. Retrieval is unchanged
and eval:compare reports no metric outside the tolerance band; the
long_context suite scores correctness 1.000 with historyElided 0.500 (the
two eviction cases, by design). Unit tests 337 -> 386 passing, same 7
pre-existing failures.
Ingest-side CHAR_TO_TOKEN_RATIO is deliberately untouched: changing it
re-chunks the corpus and invalidates retrieval baselines, which belongs in
its own measured change.
Allows users to optionally specify a model to use for ancillary tasks (chat suggestions, chat titles, etc.) instead of strictly using the last used model. This means aesthetic/non-critical work can be passed to a small, lightweight model instead of a heavy reasoning model used for chats.
If a model is not selected in the AI Assistant settings (`/settings/models`), the existing behavior of using the last used model is retained.
Shows a dismissible dashboard banner to users who submitted a benchmark under
v1 scoring but don't yet have a Score v2 result, nudging them to re-run so the
community leaderboard gets their v2 number.
- Show logic (benchmark_service.shouldShowRerunBanner): has a result with
submitted_to_repository=true AND no result with nomad_score_v2 AND the
dismiss KV isn't set. Self-clears two ways — dismiss sets the KV, and any v2
run gives a result a nomad_score_v2 so the condition flips off on its own.
- New GET /api/benchmark/rerun-banner + useBenchmarkRerunBanner hook (mirrors
useUpdateAvailable), rendered as a dismissible Alert on the dashboard with a
"Re-run benchmark" CTA to /settings/benchmark.
- New KV key benchmark.rerunBannerDismissed (boolean) in both KV_STORE_SCHEMA
and the SETTINGS_KEYS whitelist (the PATCH /system/settings validator enum);
dismiss writes it via the existing updateSetting endpoint + invalidates the
query.
Sibling to the Score v2 app client (#1094); stacked on it for the
nomad_score_v2 column. Browser-verified on the NOMAD3 dev env: show/dismiss/
reload-persist/self-clear-on-v2 all correct.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(AI): per-model thinking toggle with global default (off)
Stop forcing thinking on for every capable model. Adds a global default
(ai.autoThinking, ships OFF) and a per-model override in the chat window,
shown only for thinking-capable models and remembered client-side.
The /v1 (OpenAI-compat) endpoint ignores `think`; reasoning_effort is the
real lever. The controller resolves per-request preference -> global default
-> OFF (gated on capability), and the service maps that to reasoning_effort
('none' to suppress on a capable model, 'medium' for gpt-oss, unset to let a
capable model default thinking on). A new thinkingCapable flag keeps
non-Ollama backends from ever receiving reasoning_effort.
installed-models is enriched with a `thinking` flag (checkModelHasThinking,
now memoized) so the picker knows which models get the toggle.
Stacks on #1078 (reasoning-field read + client-disconnect abort).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(AI): thinking toggle as NOMAD Switch + info tooltip
Address review feedback on the per-model thinking control:
- Use the shared Switch component (matches AI Assistant settings) instead
of a raw checkbox.
- Label "Thinking:" with a colon to match the adjacent "Model:" label.
- Add an InfoTooltip explaining what thinking does and where the default
lives. Extend InfoTooltip with optional `position` ('top'|'bottom') and
`align` ('center'|'right') so it opens downward and expands leftward from
the right-edge header slot instead of being clipped/crushed against the
viewport edge. Defaults preserve existing (benchmark page) behavior.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(rag): add subject/collection organization to knowledge base
- Add nullable collection field to KbIngestState, propagated through the
embed job, RAG service, and Qdrant point payloads (indexed for filtering)
- Add upload-time category selection and per-file collection reassignment
in the Knowledge Base modal, with a filterable Stored Files table
- Add a 'Search in' collection filter to the chat interface, threaded
through to searchSimilarDocuments as an optional Qdrant filter
- Fix .docx extraction: previously routed through raw-text extraction
(garbage output for a ZIP-based XML format); adds a proper mammoth-based
extractor and a dedicated 'docx' file-type case
* feat(rag): support dynamic KB collection creation, rename, and removal
Extends collection organization with a Manage Collections UI: collections
are created on the fly when a file is assigned to a new name, can be
renamed (bulk-updates every tagged file and Qdrant point), and can be
removed (reassigns tagged files back to Uncategorized rather than
deleting anything).
* fix(rag): use dynamic collections query in chat search filter
chat/index.tsx still imported the static KB_COLLECTIONS constant for its
'Search in' dropdown, inconsistent with KnowledgeBaseModal.tsx which already
uses the live getKnowledgeCollections() query. Renamed/added collections
via the new Manage Collections UI weren't reflected in the chat filter.
* feat(rag): broaden preset tags and add creatable collection combobox
Replaces the survival-specific preset list with general-purpose starter
tags (recipes, diy, health, technology, finance, travel, hobbies,
reference, survival, energy) so the Knowledge Base reads well for
home-lab/reference use, not just prepping.
Adds sanitizeCollectionName() (trim, lowercase, length cap) applied on
every write path server-side, and a dependency-free CollectionCombobox
component replacing the plain <select> + window.prompt pattern for
tagging — autocompletes against presets + tags already in use, with a
'+ Create' option for anything new.
* chore(rag): remove .docx fix from this branch, split into #1100
Per review feedback, the .docx extraction fix is unrelated to the
collections feature and can merge independently. Moved to a standalone
PR (Crosstalk-Solutions/project-nomad#1100) off dev.
* chore: remove unrelated diff noise (lockfile, comments, indentation)
---------
Co-authored-by: John Cortright <jcortright@zscaler.com>
Phase 1 of the live benchmark-run experience. Replaces the opaque (and
in sync mode, simulated) progress bar with a real-time run view driven by
actual host telemetry.
- Async run path: the UI now dispatches to the queue worker and keys off
SSE instead of faking stage progress with client-side timers.
- New BenchmarkTelemetrySampler broadcasts per-core CPU load, CPU temp
(best-effort, hidden when unavailable), and disk MB/s at 1 Hz over a new
benchmark-telemetry SSE channel. Runs in the orchestration process, never
the sysbench container, so it cannot affect scores.
- BenchmarkProgress carries the ordered stage plan + index so the frontend
renders a live stage rail.
- AI benchmark streams /api/generate for live tokens/sec and true TTFT; the
scored numbers still come from Ollama's authoritative final eval fields.
- Frontend: useBenchmarkRun hook owns both subscriptions; self-contained SVG
components (StageRail, CoreGrid, Sparkline, LiveReadout) + BenchmarkRunView,
styled in the desert palette. No chart library added.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The uninstall and update scripts called header_red() and referenced color
variables (GREEN/RED/RESET) that were never defined, so running them printed
"header_red: command not found" instead of the section banners. Also corrects
the update script's success message (it said "installation completed" and
pointed at an undefined ${nomad_dir} path) and fixes two spelling typos.
- uninstall_nomad.sh: add Color Codes block + header_red()
- update_nomad.sh: add header_red(); fix success wording and start_nomad.sh path
- CONTRIBUTING.md, admin/constants/ollama.ts: spelling fixes
Reported by @fix2015 in #1058; reimplemented in-house.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat: replace legacy Kolibri image default with latest v19 image
* feat(supply-depot): add content migration instructions for Edu Platform Gen 1 to 2
Adds the MeshCore web client to the Supply Depot catalog (host port 8500),
alongside the existing Meshtastic apps. Uses aXistem's prebuilt image of Liam
Cottle's MeshCore client (MeshCore is a sibling LoRa mesh project to Meshtastic).
The image is stock nginx serving a static Flutter build over HTTP, but the
client reaches radios via Web Bluetooth / Web Serial, which browsers only allow
from a secure (HTTPS) context. So we serve it over HTTPS: a new preinstall hook
generates a self-signed cert + a small SSL nginx config into storage/meshcore-web,
both bind-mounted into the container (the config over the image's default.conf),
publishing 443. Same one-time browser-warning approach as Vaultwarden, whose
openssl cert generation is refactored into a shared _ensureSelfSignedCert helper.
Also adds a NOMAD-specific docs section + Manage>Docs anchor, and registers the
IconAntenna icon. Meshtastic Web left unchanged.
Validated on NOMAD3 (v1.33.0-rc.1): the image + SSL config + self-signed cert
serves the MeshCore Flutter app over HTTPS 200 with working SPA fallback.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds a self-update path for the NOMAD admin/core image that runs without a
human in the loop, gated by layered safety checks. Recreation remains the
sidecar's job; this only decides *whether* to update now and requests it.
An hourly job (AutoUpdateJob) evaluates a side-effect-free decision pipeline
(AutoUpdateService.evaluate):
- opt-in: disabled by default
- in a user-configured local-time window (handles midnight wrap)
- an eligible release: same major (major bumps stay manual), strictly newer,
past a configurable cool-off, GA only (no drafts/prereleases), strict semver
- pre-flight: sidecar present, no in-flight system update / downloads / app
installs, and sufficient host disk (estimated from the registry manifest)
When all pass, it drives SystemUpdateService.requestUpdate() with a vetted tag.
Failure backoff auto-disables after 3 genuine update-request failures; transient
release-lookup errors are skips (offline-first appliances are routinely without
connectivity). Re-enabling clears the backoff state.
Settings UI exposes the toggle, window, and cool-off, with live status (eligible
target, in/out of window, last result/error). A `node ace auto-update:dry-run`
command exercises the full pipeline — and a deterministic --scenarios suite the
pure decision logic — without ever triggering an update.
New KVStore keys under autoUpdate.* hold config + last-run state.
Add NOMAD-specific getting-started docs for all 9 curated Supply Depot apps,
the catalog/install fixes each one surfaced, and a way to reach the docs from
each app card.
Docs:
- New in-app Markdoc page admin/docs/supply-depot-apps.md covering all 9 apps
(Stirling PDF, File Browser, Calibre-Web, IT Tools, Excalidraw, Homebox,
Vaultwarden, Jellyfin, Meshtastic Web): first run/login, where data lives,
and offline behaviour. Registered in docs_service DOC_ORDER.
- Manage > Docs dropdown item linking each app to its section
(/docs/supply-depot-apps#<anchor>): anchor map in constants/supply_depot_docs.ts,
heading anchors via Markdoc {% #id %}, and hash-scroll on the docs page.
Install / catalog fixes:
- Stirling PDF: open straight to the tools (SECURITY_ENABLELOGIN=false; the old
v1 DOCKER_ENABLE_SECURITY flag was dead).
- File Browser: seed a known admin/nomad login (bcrypt) instead of a random
log-only password; scope visibility to content folders via mount selection and
move the DB out of the browsable root.
- Calibre-Web: bundle an empty Calibre library and seed it on install so setup
doesn't dead-end at db config (_runPreinstallActions__CalibreWeb).
- Homebox: swap the archived hay-kot image for the maintained sysadminsmedia fork.
- Vaultwarden: generate a self-signed cert on install and serve HTTPS by default
(_runPreinstallActions__Vaultwarden + ROCKET_TLS + ui_location https:8480), so
the web vault has the secure context it requires.
- Jellyfin: pre-create storage/media/{Movies,TV Shows,Music,Photos} so each
library points at its own subfolder, avoiding the overlapping-path issue that
silently hides content (_runPreinstallActions__Jellyfin).
- Seeder run() now also syncs ui_location for non-modified curated services, so a
catalog link/scheme/port change reaches existing installs on update.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Users often saw the assistant disclaim ("Sorry, I couldn't find specific
context regarding X, but here's a general answer...") even when material
that directly answered the query was embedded. Two compounding causes,
both on the read/generation side:
1. The rag_context system prompt explicitly authorized the hedge: it told
the model to fall back to general knowledge and "acknowledge the
limitations," and mandated "According to the information available..."
citations that pushed small models into meta-commentary preambles.
Rewrote it to treat the retrieved context as the primary, authoritative
source, lead with the answer, fall back to general knowledge *silently*,
and never emit "couldn't find specific context" phrasing, while still allowing
the model itself to make some "sanity-check" decisions if the retrieved context
seems wildly incorrect or unrelated to the user's query.
2. The injected context labeled each block with a raw relevance score
(e.g. "Relevance: 42.3%"). nomic-embed cosine scores for genuinely
relevant passages sit ~0.4-0.6, so the number primed the model to
distrust correct context. Dropped the model-visible score (it stays in
the logs) and replaced it with a neutral source-title label.
Also added a conservative, score-scaled heading boost in rerankResults:
when query keywords match a chunk's section/article title (ZIM metadata we
already fetch), nudge its rank. Same diminishing-returns shape as the
existing boosts and gated behind the existing semantic-quality threshold,
so it can't promote a weak match.
Scope note: this removes the visible hedge and improves ranking of
already-retrieved chunks. It does NOT fix retrieval recall — diluted
1500-token chunks that never reach dense search's top-k are unaffected.
That's a follow-up (smaller chunks + BM25/RRF hybrid).
Lift the hardcoded 'nomic-embed-text:v1.5' string out of both
RagService and OllamaService into a shared EMBEDDING_MODEL_NAME
constant in constants/ollama.ts. The duplicate in OllamaService
existed only to dodge a circular import with RagService; the
constants module has no service imports, so a shared constant
eliminates both the duplication and the drift risk called out
in the inline "keep in sync" comment.
* feat(KB): per-file ingest state machine (Phase 1 of RFC #883)
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
* feat(KB): Always/Manual ingest policy toggle (RFC #883 §1/§4)
Activates the `rag.defaultIngestPolicy` KV registered in Phase 1
(#888) so users on a fresh install (or anyone who picks Manual mode)
no longer get every new ZIM auto-dispatched to the embed pipeline.
## Stacks on #888
This PR's base is `feat/kb-ingest-state-machine` (#888). The state
machine has to be in place for the decision function to be policy-aware;
GitHub will fast-forward the base to `rc` once #888 merges.
## Backend changes
- `decideScanAction` now takes a `policy: 'Always' | 'Manual'` argument
(defaults to `Always` for backward compatibility).
- New `ScanAction` kind: `create_pending`. Manual mode records that the
scanner has seen a new file (so the UI can surface a per-card Index
affordance later) without dispatching an EmbedFileJob.
- `scanAndSyncStorage` reads the KV and passes it through. The scan-result
log line now includes the active policy and a `waiting on user` count
for Manual-mode hits.
- `rag.defaultIngestPolicy` added to `SETTINGS_KEYS` so it's reachable
through the existing `GET/PATCH /api/system/settings` surface — no new
endpoint.
## Frontend changes
- New section in the KB panel between "Why upload" and "Processing Queue":
"Auto-index new content for AI? [Always | Manual]" — segmented radio
with copy explaining the 5-10× disk multiplier. Default Always.
- `useQuery('ingestPolicy')` reads the current value; clicking the
inactive option mutates and shows a notification confirming the new
behavior.
## Tests
- 14 unit tests on `decideScanAction` (was 9) — split into Always-mode
cases (preserves Phase 1's contract) and Manual-mode cases
(`create_pending`, `pending_decision → skip`, etc.).
- Type-check clean.
- Hot-patch + browser verification deferred until #888 lands; the state
machine smoke-tested cleanly on NOMAD3 in #888's PR, and this PR's
decision-tree changes are exhaustively unit-tested.
## RFC open question §3 — policy-change re-trigger
Switching Manual → Always doesn't auto-dispatch existing `pending_decision`
rows immediately. The next scan re-evaluates and dispatches them under
the new policy. This matches the RFC's "treat the switch as I've-
thought-about-it" instinct for the guardrail; full guardrail
implementation lands in Phase 3 task 14.
---------
Co-authored-by: Jake Turner <52841588+jakeaturner@users.noreply.github.com>
* feat(maps): add regional map downloads via go-pmtiles extract
* address Copilot review feedback on PR #780
- auto-refresh preflight on selection/maxzoom change with 400ms debounce and
requestId stale-safety so the confirm button no longer requires a two-step
"Estimate Size" -> "Start Download" dance
- safeUpdateProgress helper replaces fire-and-forget updateProgress().catch()
pattern so cancelled-job errors (code -1) can't surface as unhandled rejections
- gate world basemap source on worldBasemapReady - when ensureWorldBasemap()
fails we already delete world.pmtiles, so emitting the source was producing
404s on every tile request
- verify go-pmtiles binary SHA256 at image build time; upstream doesn't ship a
checksums file so per-arch hashes are pinned as build args with a regenerate
note when bumping PMTILES_VERSION
New defaults:
OLLAMA_NO_CLOUD=1 - "Ollama can run in local only mode by disabling
Ollama’s cloud features. By turning off Ollama’s cloud features, you
will lose the ability to use Ollama’s cloud models and web search."
https://ollama.com/blog/web-searchhttps://docs.ollama.com/faq#how-do-i-disable-ollama%E2%80%99s-cloud-features
example output:
```
ollama run minimax-m2.7:cloud
Error: ollama cloud is disabled: remote model details are unavailable
```
This setting can be safely disabled as you have to click on a link to
login to ollama cloud and theres no real way to do that in nomad outside
of looking at the nomad_ollama logs.
This one can be disabled in settings in case theres a model out there
that doesn't play nice. but that doesnt seem necessary so far.
OLLAMA_FLASH_ATTENTION=1 - "Flash Attention is a feature of most modern
models that can significantly reduce memory usage as the context size
grows. "
Tested with llama3.2:
```
docker logs nomad_ollama --tail 1000 2>&1 |grep --color -i flash_attn
llama_context: flash_attn = enabled
```
And with second_constantine/deepseek-coder-v2 with is based on
https://huggingface.co/lmstudio-community/DeepSeek-Coder-V2-Lite-Instruct-GGUF
which is a model that specifically calls out that you should disable
flash attention, but during testing it seems ollama can do this for you
automatically:
```
docker logs nomad_ollama --tail 1000 2>&1 |grep --color -i flash_attn
llama_context: flash_attn = disabled
```
Add a warm charcoal dark mode ("Night Ops") using CSS variable swapping
under [data-theme="dark"]. All 23 desert palette variables are overridden
with dark-mode counterparts, and ~313 generic Tailwind classes (bg-white,
text-gray-*, border-gray-*) are replaced with semantic tokens.
Infrastructure:
- CSS variable overrides in app.css for both themes
- ThemeProvider + useTheme hook (localStorage + KV store sync)
- ThemeToggle component (moon/sun icons, "Night Ops"/"Day Ops" labels)
- FOUC prevention script in inertia_layout.edge
- Toggle placed in StyledSidebar and Footer for access on every page
Color replacements across 50 files:
- bg-white → bg-surface-primary
- bg-gray-50/100 → bg-surface-secondary
- text-gray-900/800 → text-text-primary
- text-gray-600/500 → text-text-secondary/text-text-muted
- border-gray-200/300 → border-border-subtle/border-border-default
- text-desert-white → text-white (fixes invisible text on colored bg)
- Button hover/active states use dedicated btn-green-hover/active vars
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
