Response-quality work, all aimed at giving the model enough context to
answer accurately:
- Every chat's system prompt now carries a live library map (today's
date, counts, and the exact names of channels, domains, zims, and
inventories, capped and cached 60s). Exact spellings are in context
before the first token, and the list_collections round-trip — minutes
on slow hardware — is usually unnecessary.
- The request is budgeted against the model's real context: oldest
turns are trimmed first, the current question is never dropped, and
an over-budget turn gets a clear "start a new conversation" error
instead of a silently truncated prompt. cache_prompt reuses the KV
cache for the stable prefix, which is most of the latency on a Pi.
- context_size plumbing fixed: selecting a model adopts its catalog
default (the 4B was running at 8k instead of 16k), and llama-server
runs --parallel 1 so the single user gets the whole context.
- temperature is configurable in ai.yaml (default 0.3 — small models
call tools far more reliably near 0.2-0.3 than at server defaults).
- One worked tool-use example per mode is prepended to every
conversation; small models imitate far better than they obey.
- scripts/ai_eval.py scores the assistant against a live WROLPi on
mechanical checks (finished, called the right tool first, included a
WROLPi link, no external URLs) so prompt changes stop being validated
by anecdote.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
install_llama_cpp.sh builds llama-server from the SHA-pinned v0.3.0
source tarball (single source of truth: on-device upgrade, Pi image,
Live ISO, and the new docker/llm container all call it; version-marker
makes re-runs no-ops). start_llama_server.sh refuses to start unless
ai.yaml enables AI and the active model file exists — which also covers
unmounted media. wrolpi-llm.service is installed but deliberately
never enabled at boot: the Controller starts it on demand (registered
in managed_services; the llm compose service follows the samba
pattern), and the API's perpetual idle-unload worker asks the
Controller to stop it after the configured idle minutes. llama-server
is localhost-only natively, internal-network-only in docker, and never
proxied by Caddy.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
