- llm.py: log each Ollama call's caller + total/load/prompt/gen durations
so a slow voice turn is attributable to a specific internal call
(router/enrichment/digest/main); a RELOAD marker flags cold reloads.
- voice.ts: track in-flight Opus captures and abort them on session
destroy(); drop any utterance that finishes after the user left, so no
trailing post-leave VAD turns are reported.
- userbot.ts: show the speaker's Discord user ID on each transcript line
(answered and dropped) so it's clear whose audio produced the turn.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Ollama keeps a separate loaded model instance per (model, num_ctx). The
main agentic chat used num_ctx=8192 while the router/enrichment/digest
passes used 4096, so every voice turn forced at least one cold reload
(~3.4s) when switching context sizes — the dominant per-turn latency
(measured: resident chat call 0.27s vs cold 3.4s).
Introduce a single OLLAMA_NUM_CTX (default 8192, env-tunable for tight
VRAM) used by call_llm_direct, chat_with_messages, call_llm_streaming and
the planner, collapsing a turn to one resident instance.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replace the blunt global OLLAMA_KEEP_ALIVE=-1 (which kept every model,
including nomic-embed, resident in VRAM forever) with per-request residency:
- llm.py: all three /api/chat payloads send keep_alive=30m so the actively
used chat model stays resident and voice turns never pay a cold reload.
- embeddings.py: /api/embeddings sends keep_alive=0 so nomic-embed unloads
right after each call instead of squatting in VRAM next to the chat model.
- docker-compose.yml: drop the global OLLAMA_KEEP_ALIVE=-1; document the
per-request scheme on the ollama service.
Switch the default chat model qwen3:8b -> qwen2.5:3b. Verified live on the
RTX 5050 (8GB):
- ollama ps: qwen2.5:3b 2.4GB, 100% GPU (8B was 92% GPU / 8% CPU), UNTIL ~30m
(the 30m pin, not "Forever"); nomic-embed absent after several enriched turns.
- nvidia-smi: ~3.2GB VRAM used total (qwen 2.4GB + whisper 0.7GB) vs ~6.6GB.
- Korean /text turns: warm 1.7-4s (cold first load ~52s), vs ~5-7s on 8B;
time/weather/places tool calls fire and reply in Korean.
Known limitation: qwen2.5:3b can occasionally leak a trailing CJK phrase on
free-form chit-chat (factual/tool replies stay clean).
