ExternalSecret only provides vllm-api-key from 1Password.
Using same key for both purposes is acceptable for now.
Avoids need for manual secret patching.
The open-webui item doesn't exist in 1Password yet.
webui-secret-key is manually managed via Kubernetes secret patch.
Template now only pulls vllm-api-key from 1Password.
- Change emptyDir to persistent volume (open-webui-data PVC)
Fixes data loss on pod restart (admin user disappears)
- Add ENABLE_OLLAMA_API=false
Disable broken Ollama endpoint probing
vLLM uses OpenAI-compatible API, not Ollama
- Create pvc.yaml for persistent data storage
10Gi NFS-backed storage via nfs-emporium StorageClass
The 'open-webui' item doesn't exist in 1Password yet.
webui-secret-key is manually patched in the secret for now.
Re-enable ExternalSecret pull once the 1Password item is created.
- Add ExternalName Service for astro-orbiter (10.1.71.130:8000)
- Remove Ollama-specific configuration (OLLAMA_BASE_URLS)
- Keep only OpenAI-compatible configuration (OPENAI_BASE_URL)
Fixes: Open WebUI pod was attempting to use Ollama endpoints (/api/tags)
against vLLM which only supports OpenAI-compatible API (/v1/models).
Pod now reaches astro-orbiter via Kubernetes Service DNS name.
Resolves: HTTP 404 errors from Upstream ollama request failed
- ExternalSecret: use key/property format (not path) for 1Password reference
- Deployment: remove admin password env var (use single vllm-api-key)
- Syncs vLLM API key from op://mk-labs/vllm/api-key
astro-orbiter's vLLM primary model changed 2026-09-01 from
DeepSeek-R1-Distill-Qwen-32B-AWQ to Gemma-4-26B-A4B-it-AWQ (Google,
Apache 2.0, US-origin). Same endpoint/API key — only the served model
name changed. max_model_len also bumped to 65536 (was 32768).
Gemma 4 does not emit an always-on <think> reasoning trace like
DeepSeek-R1 did, so HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=4096
should have more effective headroom for real extraction output than
before, not less.
astro-orbiter's vLLM primary model changed 2026-09-01 from
Qwen2.5-32B-Instruct-AWQ to DeepSeek-R1-Distill-Qwen-32B-AWQ
(single-model deployment, see ansible/roles/deploy-vllm/README.md).
Same endpoint (http://astro-orbiter:8000/v1) and API key — only the
served model name changed.
Flagged for follow-up, not fixed here: DeepSeek-R1 emits a
<think>...</think> reasoning trace before its final answer; Hindsight's
fact-extraction prompt/parsing has not been specifically verified
against this reasoning-model output shape. Watch extraction quality
after this rolls out; HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS left
at 4096 pending confirmation that's enough headroom for a real retain
to complete past the reasoning trace.
Companion commit to 6bfcc76 (vllm permanent residency cutover). Now that
llama-swap is stopped+disabled and vLLM is the permanent serving layer:
- HINDSIGHT_API_LLM_BASE_URL: llama-swap :8001 -> vLLM :8000
- HINDSIGHT_API_LLM_MODEL: Qwen3.8-27B-Q4_K_M -> Qwen2.5-32B-Instruct-AWQ
- Added HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=4096 (vLLM's
max_model_len=8192 is far below the 64000 Hindsight retain default;
uncapped this 400s on long retains -- found in t_e6facb19's validation)
- ExternalSecret HINDSIGHT_API_LLM_API_KEY source: 1Password 'nous' item
(unused fallback) -> 1Password 'vllm' item's api-key (vLLM validates
its bearer token, unlike llama-swap which accepted anything)
Embeddings intentionally UNCHANGED -- Hindsight stays on its bundled
local 384-dim embedder (HINDSIGHT_API_EMBEDDINGS_PROVIDER still unset).
Switching to vLLM's nomic-embed (768-dim) is a separate, destructive,
explicitly-approved-only migration (~1300 rows across ~20 banks) --
out of scope here, not part of the dashboard's 'stop llama-swap /
start vLLM' instruction.
Critical finding: flipping vllm_service_enabled/state=true/started and
restarting llama-swap alongside it broke llama-swap's ability to load
ANY of its own generative models -- every /v1/chat/completions request
against Qwen3.8-27B-Q4_K_M or Qwen3-8B aux models failed with
'upstream command exited prematurely' (llama-server OOM at spawn,
~1.8GB free on this 24GB card once vLLM's ~22.8GB was claimed).
Confirmed by direct A/B: same request 500s with vLLM running, 200s
seconds after stopping it.
This breaks 21 Hermes agent profiles' aux-model tasks (skills_hub,
approval, mcp, title_generation, profile_describer, compression) plus
OpenViking's VLM -- a far larger blast radius than Hindsight's single
LLM endpoint. Reverted:
- vllm_service_enabled/state back to role defaults (false/stopped) --
vLLM stays staged, startable for a brief validated shadow window,
NOT safe to leave resident in production.
- Hindsight's HINDSIGHT_API_LLM_BASE_URL back to llama-swap
(astro-orbiter:8001, Qwen3.8-27B-Q4_K_M) and the API key secret
source back to the Nous fallback item (pre-task state) --
the vLLM cutover, while functionally validated in isolation
(health, /v1/chat/completions, and a live hindsight_retain+recall
round-trip all succeeded), requires continuous vLLM availability
which is now known to be unsafe on this card.
Comment posted on t_6dff1ecc: teardown remains correctly blocked --
full cutover is not achievable within this card's VRAM budget as
currently scoped. Needs a human decision on aux-model migration
strategy (see roles/deploy-vllm README's 'Critical architectural
finding' section) before any further progress.
Default 64000 exceeded vLLM Qwen2.5-32B-Instruct-AWQ's --max-model-len
8192, causing every retain call to 500 with
'max_tokens=64000 cannot be greater than max_model_len=8192'.
llama-swap's Qwen3.8-27B ran at ctx=65536 so this never surfaced
before the vLLM cutover. Lowered to 4096.
HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai pointed at vLLM's
nomic-embed-text-v1.5 (768-dim) crash-looped hindsight-api:
'Cannot change embedding dimension from 384 to 768: memory_units
table contains 1289 rows with embeddings.' Hindsight was never
actually using astro-orbiter for embeddings (defaults to a bundled
local 384-dim sentence-transformers model when the env var is unset)
-- this was a genuine architecture assumption error, not a config
typo. Re-embedding all existing memory data across ~20 agent banks is
a destructive, irreversible operation requiring its own explicit,
approved task -- not something to trigger as a side effect of an
infra migration. Keeps the LLM cutover (astro-orbiter:8000, vLLM
Qwen2.5-32B-Instruct-AWQ) which is safe and already validated.
- vllm.service.j2: branch on role==embedding for --runner pooling
--convert embed, --no-enable-prefix-caching, per-model
trust_remote_code toggle (needed for nomic-embed-text-v1.5's custom
NomicBertModel code), and enforce_eager toggle (needed to avoid CUDA
graph capture OOM when co-resident with another vLLM process on this
24GB card).
- tasks/verify.yml: split completions vs embedding smoke tests --
embedding-mode instances don't serve /v1/completions. Assert a
non-empty embedding vector, not just HTTP 200.
- host_vars/astro-orbiter: enable nomic-embed-text-v1.5 (port 8020),
lower primary model's gpu_memory_utilization 0.95->0.90 + add
enforce_eager after finding 0.95 crash-looped 6-7x before stabilizing
with co-resident nomic-embed (real fix, confirmed via NRestarts=0
after clean stop/start, not luck).
- Hindsight (values.yaml + externalsecret.yaml): cut LLM + embeddings
over to vLLM (:8000, :8020), wire the previously-unset
HINDSIGHT_API_EMBEDDINGS_* env vars for the first time, and swap the
API key secret source from the Nous fallback item to vllm/api-key
(vLLM enforces real auth, llama-swap did not).
- README: document the embedding-mode branch, VRAM findings, and a
genuine architecture gap -- vLLM's one-model-per-process design
cannot replace llama-swap's 5-model LRU roster on this 24GB card, so
21 Hermes profiles' aux-model consumers (Qwen3-8B-no_think,
Phi-3.5-mini, Meta-Llama-3.1-8B, Qwen2.5-Coder-14B) and OpenViking's
VLM stay on llama-swap. Full teardown (t_6dff1ecc) needs a human
decision on the aux-model strategy before it can proceed.
t_d7f8cd65 — astro-orbiter is a single serial llama-swap process; Hindsight's
default LLM_MAX_CONCURRENT=32 burst N parallel retains -> node rejects/times
out extras -> 502. Cap global + retain concurrency to 1 (native semaphore
config, no code change). Raise client timeout 120->600s and ingress
proxy-read/send 300->600s so a single long retain (~90s under load) survives.
stepfun rejects Hindsight tagged fact-extraction (400 'missing tags');
solar-pro4:free is the previously-verified-good Nous model for retain/recall
(t_e3375410 / t_d0dffc3d). base_url + provider unchanged.
Scoped to the hindsight ingress (cosmic-rewind.local.mk-labs.cloud) only.
A slow agentic reflect (~132s on solar-pro4) exceeded the 60s default
proxy-read-timeout, causing nginx to return 504 before the app delivered 200.
Raising read+send to 300s lets the response land. No global configmap change.
Refs t_e3375410.
astro-orbiter llama-server-router retired :8002 on 2026-08-18
(llama-swap now serves the OpenAI-compatible API on :8001).
OpenViking still pointed at the dead :8002, so every search/find
call failed: dense embedding -> Connection error -> 500 INTERNAL
server error on /api/v1/search/search and /api/v1/search/find.
Verified from the pod: :8002 = connection refused,
:8001/v1/embeddings (nomic-embed-text-v1.5) = 200 with vectors.
Repoints embedding.dense.api_base and vlm.api_base to :8001.
t_abc05db5
- vlm.model was 'llama3.1-8b' which doesn't exist on astro-orbiter's
/v1/models, causing every summarization call to 400 and endless
circuit-breaker retries. Correct id: Meta-Llama-3.1-8B-Instruct-Q4_K_M.
- embedding.max_input_tokens=1536 still let chunks through that actually
tokenized to 2000-2860 real tokens (estimator undercounts vs llama.cpp's
tokenizer by 1.35x-1.86x on this corpus). Lowered to 1024 for real margin
under the 2048 n_ctx ceiling.
astro-orbiter's llama.cpp router hard-caps nomic-embed-text-v1.5 effective
context at 2048 tokens regardless of ctx-size (known nomic-bert/RoPE limitation
in llama.cpp, not fixable server-side). OpenViking chunks observed at
2000-3400 tokens were tripping 400 exceed_context_size_error and endless
circuit-breaker re-enqueue for viking://temp/default/08140552_5f1c9e/homelab.tar/*.
Set embedding.max_input_tokens: 1536 (well under 2048) since OpenViking's
chunk-time token estimator uses a different tokenizer than llama.cpp's context
counter, so token counts won't match 1:1 - 1536 leaves ~25% headroom.
Approved by Ryan as lowest-risk mitigation (does not touch astro-orbiter/
llama.cpp serving config, which is War Machine's domain and already fixed
separately for the ubatch-size issue).
- Build/push image: the-seas.local.mk-labs.cloud/library/maelstrom-ui:v0.3.17-1
(upstream volcengine/openviking web-studio/, pinned to commit 3cd1d4e9)
- Deployment + Service serving the static SPA via nginx (reverse-proxies
/api, /health, /ready to openviking backend; /bot deliberately NOT proxied)
- Ingress at maelstrom.local.mk-labs.cloud (TLS via letsencrypt-internal)
- ExternalSecret wiring scoped maelstrom-ui-key from
op://mk-labs/openviking/maelstrom-ui-key into the pod env (MAELSTROM_UI_KEY)
Per approved plan: inbox/ryan/2026-08-14-maelstrom-ui-deployment-plan.md
Key mint + approval: system/inbox/agents/nick-fury/2026-08-14-maelstrom-ui-key-mint-complete.md
Ryan approval: inbox/ryan/2026-08-14-maelstrom-key-approval.md
The letsencrypt-internal ClusterIssuer does not exist on the cluster.
Only letsencrypt-prod and letsencrypt-staging are available.
Both use DNS-01 via Cloudflare for the mk-labs.cloud zone,
so they work for internal-only hosts with no public HTTP reachability.
Fixes: https://github.com/volcengine/openviking/issues/...
Closes: kanban task t_1c2cc2db
Bug 1: ExternalSecret referenced three separate 1Password items
(openviking-root-api-key, openviking-embedding-api-key, openviking-vlm-api-key)
but Ryan created ONE item 'openviking' with three fields inside.
Changed all remoteRef.key values to 'openviking' and corrected field property names.
Bug 2: values.yaml had two invalid embedding config fields:
- encoding_format: 'float' (not in upstream schema, removed)
- max_concurrent under embedding.dense (wrong nesting, moved to embedding level)
Verified against upstream chart schema at github.com/volcengine/openviking
Remove resource-level sync-wave annotations that caused ArgoCD deadlock.
The wave 8 annotation was meant for platform-level ordering (apps-of-apps)
but was incorrectly applied to individual resources within the Application.
This caused ArgoCD to apply Deployment (wave 0) before ExternalSecret (wave 8),
resulting in CreateContainerConfigError since the pod needed the secret first.
Changes:
- namespace.yaml: Remove sync-wave annotation, add clarifying comment
- externalsecret.yaml: Change sync-wave from 8 to -1 (must sync before Deployment at wave 0)
- application.yaml: Remove sync-wave annotation, document as platform-level only in comments
This ensures:
1. ExternalSecret syncs first (wave -1)
2. Deployment uses it immediately (implicit wave 0)
3. No deadlock
Task: t_3906c41a
- Changed source 1 from 'chart: deploy/helm/openviking' to 'path: deploy/helm/openviking'
- ArgoCD multi-source now correctly resolves the Helm chart from the git repo
- targetRevision: main now correctly refers to a git branch, not a chart version
- Fixes: invalid revision 'main': improper constraint error
Platform Knowledge Infrastructure pilot - context database for large
file trees, shared skills, and long-term logs to reduce agent token
consumption. Pilot scope: two corpora (hermes/ skills library,
personal/homelab/), two consumer profiles (Wong, Shuri) for before/after
token comparison.
- namespace.yaml: openviking namespace, sync-wave 8 (after Harbor wave 7)
- externalsecret.yaml: credentials from 1Password via onepassword-connect
ClusterSecretStore (Wong, t_32766900)
- values.yaml: Helm overrides - px-fa-direct-access storage (30Gi),
embedding (nomic-embed-text-v1.5) + VLM (Llama-3.1-8B) via astro-orbiter
router (:8002), internal-only ingress
- application.yaml: multi-source ArgoCD Application, Harbor pattern
(Peter Parker, t_eefdcc17 + reconciled in t_3e54efa8)
Prerequisites verified complete before this commit:
- nomic-embed-text-v1.5-Q4_K_M live on astro-orbiter router (War Machine,
t_34b96e83, commit ad70b34)
- All 3 1Password items provisioned (root/embedding/vlm api keys)
- Storage class corrected to px-fa-direct-access after live PV audit
showed pure-block/pure-file have zero provisioned volumes (t_77b3ff79)
- Dry-run validated against live cluster prior to commit
Constraint: vault (~/friday) remains canonical source of truth; OpenViking
index is a derived cache, rebuilt from vault source files.
Honcho/lincoln explicitly out of scope for this work.
/metrics?model=Qwen3.6 forces the router to attempt loading Qwen3.6 every
90s scrape cycle, triggering a CUDA OOM error since VRAM is already consumed
by the resident Llama3+Phi3.5 models. This produces real GPU power spikes
(~110W load-attempt), not a benign counter read like the Llama3/Phi3.5 jobs.
nvidia_gpu_exporter (:9835) already provides GPU power/VRAM/utilization at
zero wake cost. No Grafana dashboard panel references Qwen3.6 model-specific
llama-server metrics. Ryan approved full removal.
Job is commented out (not deleted) for easy revert if Qwen3.6 is ever
re-added as a resident model. Llama3/Phi3.5 scrape jobs untouched.
Each /metrics?model=<id> request on the llama.cpp router wakes the GPU
sub-server to P2 (~110W). At 15s with two active jobs (llama3 + phi35),
combined scrape frequency (~7-8s effective) keeps the GPU continuously at
P2 despite zero real inference requests.
At 90s: each scrape wakes GPU for ~5-10s then it drops to P8 (~20W) for
~80s. Verified via iptables block test on 2026-08-13 (t_e7d547ea):
Before block: 110-115W P2 continuously
After block: 19-21W P8 consistently
After unblock: returned to 110W P2 within seconds
qwen3 interval also set to 90s (model not loaded so moot, but consistent).
Ref: t_e7d547ea
Port 8000 (gemma-2-27b-it-GGUF) is dead after the day2 router cutover on
2026-08-12. Production inference now runs through llama-server-router on
port 8002.
The router exposes per-model Prometheus metrics via /metrics?model=<id>.
Since a single /metrics request without ?model returns HTTP 400, replaced
the single stale job with three per-model jobs — one per model registered
in the router per /v1/models:
- Qwen3.6-35B-A3B-UD-Q4_K_S (currently unloaded but registered)
- Meta-Llama-3.1-8B-Instruct-Q4_K_M (loaded)
- Phi-3.5-mini-instruct-Q8_0 (loaded)
Static 'model' label carries the canonical llama.cpp model id (not alias).
Added 'endpoint: astro-orbiter-router' to identify the scrape origin.
Removed dead :8000 target entirely.
- Prometheus scrape configs for node/gpu/llama-server exporters on
astro-orbiter now declared in cluster/applications/monitoring/values.yaml
(additionalScrapeConfigs), applied via ArgoCD sync instead of an
imperative kubectl secret patch from the Ansible role.
- Grafana dashboard for astro-orbiter LLM inference added as a ConfigMap
in cluster/applications/monitoring/dashboards.yaml (grafana_dashboard=1
sidecar label), replacing the role's ad-hoc kubectl apply of a rendered
Jinja template.
- ansible/roles/llm-inference/tasks/monitoring.yml: removed the kubectl
get/patch/apply tasks and orphaned grafana-llm-dashboard.json.j2
template; role now only stands up node_exporter + nvidia_gpu_exporter
and verifies they're reachable — cluster-facing config lives in Git.
- host_vars/vars.yml + inventory.yml: finalize astro-orbiter as the
llama.cpp/RTX 3090 host (jarvis user, ssh key), drop stale Ollama/AMD
vars and ollama_server inventory group superseded by the ATX rebuild.
- Stable udev device symlinks (/dev/jmri/nce, /dev/jmri/loconet, /dev/jmri/lcc)
- jmri-monitor: polls Leviton Decora Smart switch to start/stop JMRI automatically
- Quiet hours 1-10 AM (no polling)
- 30s off-delay before shutdown
- LCRR config cloned from Gitea (ssh://gitea.mk-labs.cloud:2221/rblundon/LCRR.git)
- ~/.jmri symlinked to LCRR repo for GitOps config management
- jmri-gui: X11 remote GUI access (PanelPro/DecoderPro) via ssh -X as jmri user
- Stops daemon, launches GUI, restarts daemon on exit if layout still on
- jmri user gets login shell + SSH key for GUI sessions
- Full JRE installed (openjdk-21-jre) for AWT/X11 support
