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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> |
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| __init__.py | ||