Arming the bouncer on proxy-2 (#766) merged and deployed nothing. The switch
lives in ansible/inventory/host_vars/proxy-2.renner.uno.yml, and the trigger
list covered ansible/roles/** and the play but no inventory path at all. The
change sat in git looking applied while the host kept running dry-run, and
nothing reported a problem — there was simply no pipeline run to fail.
The bouncer's LAPI key has the same shape: it lives in
group_vars/netbird_proxy/vault.yml, so a rotation would never have reached the
hosts either.
Both paths are now listed. pve_hosts.yml deliberately is not: it is the single
inventory file for the whole fleet, so listing it would redeploy the proxies
for any unrelated host edit. The cost is that a change to [netbird_proxy] group
membership needs a manual deployment run.
Merging this triggers the run itself, since .woodpecker/deploy-netbird-proxy.yml
is in its own trigger list — so the arming from #766 applies with it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
First host goes from dry-run to enforcing. proxy-2 deliberately: it is the
dual-stack member, while proxy-1 is IPv6-only and the one where every
network-shaped problem has surfaced first — the dhcpcd/resolv.conf breakage,
and a GitHub download that could not work at all. host_vars keeps the change
to one host; proxy-1 stays on the role default.
Verified before committing: ansible-inventory resolves nftables for proxy-2
and nothing for proxy-1.
Adds netbird.renner.uno to capi_whitelists first, and that is not decoration.
The bouncer's chain sits on the INPUT hook, the proxy hosts run no other
firewall, and there is therefore no conntrack rule accepting established flows
ahead of it. A ban on the management address would drop the return traffic of
the proxies' own outbound control connection and take them off the mesh, with
no inbound attack involved at all. The same reasoning already covers the proxy
addresses, which were listed from the start.
Checked at the time of writing: none of these addresses appear in the current
decision set — searched all ~15k entries. That is the state today, not a
guarantee; they are rented addresses on a hoster whose ranges do end up on
community blocklists, which is the whole reason for listing them.
The delivery path was tested end to end beforehand with a decision on
192.0.2.1 (TEST-NET-1, so no real traffic could be affected). Both bouncers
received the ban within one poll cycle — a 206-byte delta against the usual
26 — and the revocation the same way, 203 bytes, ~5s after deletion. What that
did not test is whether a packet is actually dropped, which dry-run cannot
show. This commit is what makes that testable.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The Home Assistant conversation agent is configured and refuses every
request:
API error: request (18910 tokens) exceeds the available
context size (16384 tokens), try increasing it
18910 breaks down as ~12k for the exposed-entity overview and ~7k for
the base instructions and tool schemas, whose parameters enumerate every
exposed name, area and floor. Trimming the exposed set from 845 entities
to 666 already took the overview from 16045 tokens to 12048; the
remainder is not the sort of thing curation reaches.
--ctx-size is per slot rather than the total, which is what makes this
affordable: /props reports n_ctx_slot equal to the flag while running
four slots, where a total would have divided down to 4096. Doubling the
flag alone would double the KV cache, so the slot count halves in step
and the allocation lands where it already is -- 24979 MiB against the
32Gi limit, most of it the 19.5 GiB of weights.
Two slots matches the actual callers: one holds Home Assistant's prompt,
one holds Paperless's, and prompt caching keeps each warm. A cold prefill
of this prompt costs about two minutes, so a third caller queueing is a
better outcome than it evicting one of them.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The LLM index rebuild ran for 79 minutes and then died:
input (1069 tokens) is too large to process.
increase the physical batch size (current batch size: 1024)
Setting the batch to Paperless's chunk size looked like an exact match
and was not. Paperless counts its 1024 tokens with llama_index's
tokenizer; this server measures the batch in bge-m3's, which is
XLM-RoBERTa and splits German into more pieces. A chunk Paperless
considered full arrived here 4.4% over the limit, and the request failed
rather than truncating.
1536 absorbs a tokenizer ratio rather than a rounding error. 2048 stays
out of reach: that is where the chat model hit ErrorDeviceLost on this
Vulkan stack, under a 20 GB model's memory pressure that a 600 MB one may
well not reproduce — but there is no reason to go looking.
The index is left partial by the failed run and has to be rebuilt with
`document_llmindex rebuild` once this is live.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
HA 2026.8.3 ships a native `llama_cpp` integration, so an Assist
conversation agent backed by the cluster's own model needs no custom
component — only a path from the HA pod to llama-server, which two
default-deny policies currently block in both directions. Verified from
inside the pod: the call to llama-server.ai.svc.cluster.local:8080 times
out today.
Both grants are scoped to the single pod and port, mirroring the
paperless grant next to them, so neither widens if something else is
placed in the other namespace. llama-embed is deliberately excluded: the
conversation agent uses chat completions only.
Tool calling was verified against the running model before adding this —
the Assist API requires it, and llama.cpp only exposes it for some
builds. b10731 returns finish_reason=tool_calls and parses German
commands into the right arguments without --jinja.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 14:18:30 +02:00
7 changed files with 108 additions and 12 deletions
# The cluster's own chat model, for the llama.cpp conversation agent that
# backs Assist. Scoped to the one pod and port rather than the ai namespace
# as a whole, so this grant cannot widen if something else is placed there —
# the embedding endpoint next to it is deliberately not included.
- toEndpoints:
- matchLabels:
"k8s:io.kubernetes.pod.namespace": ai
app.kubernetes.io/name:llama-server
toPorts:
- ports:
- port:"8080"
protocol:TCP
# traefik for east-west OIDC roundtrips that go through the
# public hostname via k8s_gateway.
- toEndpoints:
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