Spoken (TTS) replies are 1-2 sentences, so an unbounded num_predict only
exposes the worst case where the chat model rambles or loops. Add an
ollama_num_predict config (default 512, 0 disables) wired into the reply
loop's chat call on both the native- and text-tool paths. The 512-token
headroom stays well above this app's short tool-call JSON, so capping never
truncates a tool call. This keeps the user's quality model instead of
downgrading it. Configurable in the container via OLLAMA_NUM_PREDICT.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
load_settings() coerced any tts_engine outside {piper, chatterbox} to piper, so
with TTS_ENGINE=edge the reply engine saw "piper" and treated the voice as
English-only in reply_language_directive() (only the OUTPUT_LANGUAGE lock kept
replies Korean). Add "edge" (and "melo") to the accepted set so the engine is
labelled multilingual correctly.
Also: a stale tts_engine in the persistent /data/jarvis-settings.json (melo/xtts
from an earlier voice, no longer built) would override the configured engine via
the entrypoint merge and leave the bot silent. Reset those to the env engine
during the merge.
Verified: load_settings() with tts_engine=edge now returns "edge"; the merge
maps melo/xtts -> edge; reply_language_directive("edge") is multilingual; 27
tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Warm per-turn timing showed STT 0.1s, TTS ~1-3s, but the reply engine (LLM) was
8-17s — even a simple "고마워" took 16.7s — because it makes multiple model calls
per turn. Add a PLANNER_ENABLED env override (config.py) and default it to 0 in
the userbot compose so the pre-loop planner's extra LLM round-trip is dropped on
this latency-sensitive voice deployment. Also pins STT_LANGUAGE=ko in compose.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a GEMINI_AUTH=oauth (default) sub-mode that shells out to the Gemini CLI
using the user's Google-account login instead of an API key. gemini_cli_search()
runs `gemini -p <query> -o json --skip-trust --approval-mode yolo`, strips
GEMINI_API_KEY/GOOGLE_API_KEY and sets GOOGLE_GENAI_USE_GCA=true so the CLI
selects the account OAuth method and fails fast when no login exists. Bounded by
a 30s timeout and fail-open to the DDG/Brave/Wikipedia cascade on any failure
(CLI missing, login expired, quota 429, timeout). GEMINI_AUTH=apikey keeps the
legacy REST path. Specs and docs/llm_contexts.md updated; behaviour covered by
tests/test_realtime_gemini_cli.py.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Completes the two info modes in the Python brain:
- config.py: read STREAM_BROWSER / GEMINI_API_KEY / GEMINI_MODEL from env into
Settings (stream_browser, gemini_api_key, gemini_model). Verified load_settings
reads both modes.
- realtime_search.py: two fail-open backends returning the same fenced
UNTRUSTED-WEB-EXTRACT envelope: browser_search() shells the Node CDP helper to
drive the on-screen Chrome (visible on the broadcast); gemini_search() calls
the Gemini REST API with google_search grounding.
- web_search.run(): routes by mode before the DDG cascade (true->browser,
false->Gemini), falling through to DDG/Brave/Wikipedia on any miss.
- browse_and_play tool: plays a YouTube video on the shared screen (true mode
only); registered in the tool registry.
- specs + docs/llm_contexts.md updated (new Gemini LLM context); CLAUDE.md spec
registry updated.
Verified live against the running Chrome: true-mode webSearch returned real
Google results for "오늘 서울 날씨", browseAndPlay played the IU 밤편지 MV, and
false-mode degrades gracefully on a bad/absent key. A valid GEMINI_API_KEY is
still needed to confirm the real Gemini grounding output.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
