llmgw keys cache --similar 0.95 also gives a chat question the answer to
one like it: the last user message embedded by router.cache.embedding_model
(asked on the gateway's account, as a judge is, within 2 s), compared by
cosine with the questions answered in the same conversation — system
prompt, earlier turns, tools and sampling the same. The exact cache is
asked first; an exact hit embeds nothing. Said as x-llmgw-cache: similar
with x-llmgw-cache-similarity, charged as a hit.
The vectors live with their answers in the gateway's memory, counted in
max_bytes and evicted with them, not in a pgvector table as BACKLOG I1
planned: the cache keeps answer text out of the store on purpose. A
question over 2,000 characters is reused only when the same. Migration
0051 holds the setting. BACKLOG I1.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Conflicts: server.go imports (both kept); agent.pb.go regenerated with buf
from the merged proto.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A bundle carries what a cluster would download — llmgw's release with its
signed checksums, every llama.cpp build per architecture with GitHub's
checksums, model files pinned and checked against Hugging Face's — into a
directory or a tar whose manifest sha256 is carried apart. Import checks
everything, the release against the signing key, before anything is put in
the mirror. With offline.mirror set, nodes install llama.cpp from the
mirror against checksums the control plane holds and sends over the agent
stream; releases default to the mirror for node add, ctl upgrade and agent
upgrades. server init and the setup form install from a bundle.
The Helm chart runs one control plane with SQLite on a claim or an external
PostgreSQL, Secrets for the agent certificate and admin token, Services,
Ingress and ServiceMonitor; deploy/container/Dockerfile builds its image.
GPU nodes stay outside Kubernetes, as docs/kubernetes.md explains.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
llmgw keys cache --project P --ttl 1h [--any] | --off, and Cache answers… on
the console's project menu. The same request — project, endpoint, canonical
model and the body without stream, stream_options, user, metadata, store and
service_tier, keys sorted — is answered from the gateway's memory for the
TTL. Only reproducible answers (temperature 0 or a seed; embeddings, rerank,
score) unless --any; only a whole 200 from the model asked for, not a
fallback's, a canary's, or one a guardrail changed.
Looked up after the limits and the input guardrails; the output policy
judges a reused answer; no_store and stored conversations bypass it.
Streamed answers are kept put back together and streamed back as chunks.
x-llmgw-cache: hit|miss|bypass, Age on a hit, Cache-Control: no-cache or
x-llmgw-cache: no to bypass. A hit is recorded with no tokens and no cost,
marked cache_hit (migration 0027) and shown by ctl request. An LRU bounded
by router.cache.max_bytes and max_entry_bytes; llmgw_cache_requests_total,
llmgw_cache_bytes, llmgw_cache_entries.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Neither had a page. "We have GPUs, now what" was left to the reader, and what
llmgw stores was scattered across four documents and a changelog — which is
the same as not being written down when somebody is doing a security review.
docs/data.md describes the behaviour and leaves the legal mapping to whoever
holds the obligation. Every claim in it was checked against the source, and it
names where to look for each: usage rows carry no text field, keys are stored
hashed, conversations are off until a named project turns them on and cannot be
turned on without a retention window.
It also states the egress exactly, which took reading every outbound call to
say honestly: nothing on the serving path leaves the cluster, and the two
things that can reach outside — fetching weights, and ctl plan reading a model's
architecture — are both started by an operator. Weight fetching points wherever
you tell it, including an internal mirror.
docs/choosing-models.md is the opinion llmgw itself does not have. Three jobs
rather than one model, with the third — embeddings — being the one that gets
forgotten and the one that decides whether the deployment can answer questions
about your company or only questions in general.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
