Intent judging, tool routing and arg extraction are classification/JSON calls,
not the spoken answer, yet the stack aliased OLLAMA_INTENT_MODEL back to the big
chat model — so each command paid the big model's cost 2-3 times for routing
before the reply even ran. With the GPU on, that round-trip stacking is the main
remaining per-turn latency. Default OLLAMA_INTENT_MODEL to qwen2.5:3b (the
project's reference small model, clean Korean on classification) and have
ollama-init pull it. The reply engine already routes these calls through
intent_judge_model, so answer quality is untouched; set OLLAMA_INTENT_MODEL =
OLLAMA_CHAT_MODEL to fold back onto one resident model.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
agents/llm.md promises "play the Nth video from the top", but browseAndPlay
only ever clicked the first result. Add an optional 1-based index argument
(default 1, backward-compatible) threaded to the Node helper, which now clicks
the Nth a#video-title and clamps to the number of results returned so asking
beyond the list plays the last available video instead of failing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
docker/jarvis-config.template.json hardcoded "tts_engine": "piper". entrypoint
renders it into /app/config/jarvis.json, and bridge _tts_engine_setting() reads
that JSON BEFORE the env — so TTS_ENGINE=melo in .env was ignored and the bot
synthesised Korean with the English Piper voice (the "foreign accent" the user
heard); the warm melo-worker sat unused.
Template now carries ${TTS_ENGINE}; compose sets TTS_ENGINE=${TTS_ENGINE:-melo}
so envsubst renders the real engine. Verified: envsubst with TTS_ENGINE=melo
yields "tts_engine": "melo", and `docker compose config` passes TTS_ENGINE=melo.
Added a regression test that the template stays env-driven and renders the
configured engine.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
MeloTTS's single Korean speaker sounded non-native ("foreign accent"). Swap it
for Coqui XTTS-v2 with the built-in female studio speaker "Ana Florence"
(language ko), the natural voice used in earlier local runs.
- bridge/xtts_worker.py: new warm HTTP worker (own /opt/xtts venv), same
/synth + /health contract and PCM16 output as the old melo worker
- docker/setup-xtts.sh: builds the venv with cu128 torch (Blackwell) + Coqui
TTS and bakes the XTTS-v2 model offline. Pins transformers>=4.57,<5 (5.x
removed isin_mps_friendly, breaking XTTS) and installs the [codec] extra
(torch>=2.9 needs torchcodec) — both verified by a real host synth
- Dockerfile: replace the melo build layer with the xtts layer
- supervisord.conf: melo-worker -> xtts-worker, env passthrough for
XTTS_DEVICE/SPEAKER/LANGUAGE (always set via compose defaults)
- bridge/server.py: default TTS_ENGINE=xtts, route to the xtts worker, generic
worker-synth helper, neural-only fallback flag (XTTS_FALLBACK_PIPER)
- settings UI: engine dropdown xtts/piper, drop the dead melo_speed field, fix
the supervisorctl restart target to xtts-worker
- compose/.env.example/README: XTTS_* vars, speaker/language knobs, remove melo
- remove bridge/melo_worker.py and docker/setup-melo.sh
- tests: xtts treated as multilingual (not English-only)
Verified on host: coqui-tts loads XTTS-v2 and synthesises Korean as
"Ana Florence" to a 16-bit mono 24kHz WAV.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drop Markdown files into an agents/ folder and their contents are appended to
the main reply LLM's system prompt, so an operator can extend the assistant's
rules/tone without code changes. Files are concatenated in filename order
(use 00-, 10- prefixes to control ordering) and re-read once per turn, so edits
apply on the next reply with no rebuild/restart. Fail-open: a missing, empty,
or unreadable folder yields no instructions and never breaks a reply.
- load_agent_instructions() in system_prompt.py (AGENTS_DIR env, default
/app/agents); reads *.md only, skips blanks, ignores non-dir paths
- engine.py appends it alongside the existing settings-UI llm_instructions,
under the same "Additional instructions from the operator:" framing
- docker-compose.yml bind-mounts ./agents:/app/agents:ro and sets AGENTS_DIR
- agents/example.md.sample starter template (.sample is not loaded)
- tests cover ordering, md-only filtering, blank-skip, env/arg resolution,
and fail-open paths
- README, .env.example, docs/llm_contexts.md updated
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
OAuth cannot be done interactively in the headless container, so the login
must be seeded into the mounted ~/.gemini. Three problems are fixed:
- Mount fragility on the Windows Docker Desktop target: the creds mount
defaulted to ${HOME}/.config/javis/gemini, but ${HOME} is often unset when
compose runs outside a WSL shell, silently mounting the wrong dir. Default is
now the project-local ./docker/gemini-oauth (cross-platform), GEMINI_OAUTH_DIR
still overrides.
- No visibility: when oauth is selected but no login is seeded, the path
silently degraded to DDG/Brave. Added gemini_oauth_ready() + a one-time debug
hint and a startup entrypoint warning (skipped on the browser role, fail-open).
- Seeding guidance: oauth_creds.json is the essential credential (refresh token;
GOOGLE_GENAI_USE_GCA=true forces OAuth), which is what the readiness check and
warning verify; docs recommend copying the whole ~/.gemini for convenience.
Adds docker/gemini-oauth/ seed dir (.gitkeep) with the login files gitignored,
GEMINI_OAUTH_DIR in .env.example, and updates DEPLOY.md, stream_browser_modes.md
and llm_contexts.md. Covered by 3 new tests (10 passed total).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Integration test driving the real bridge _save() and engine
_resolve_output_language(): a language chosen in the settings UI is written to
both the persistent volume and the runtime config, applies immediately (config
wins over the OUTPUT_LANGUAGE env), and survives a simulated container recreate
(entrypoint re-renders the config then merges the persistent override). Also
asserts the persona and reply directive both follow the persisted language.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The persona prompt was built from the raw OUTPUT_LANGUAGE env while the
reply-language directive read the settings-web UI value (config JSON), so
changing the language in the settings page was honoured by the directive
but ignored by the persona, leaving them contradicting each other.
Add _resolve_output_language() as the single source of truth (config wins
over env) and feed the same resolved value to both build_system_prompt()
and reply_language_directive(). Update docs/llm_contexts.md to match.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Address review of the noise/broadcast fixes:
- STT now refuses to run Whisper on non-speech. transcribe() runs the Silero
VAD (bundled with faster-whisper, no new dep) BEFORE the model, so noise or a
brief loud blip with no real speech never reaches STT and can't be
hallucinated into a transcript. The no_speech_prob/avg_logprob post-filter
stays as a second line of defence (a clap the VAD lets through is still killed
by Whisper's own no_speech_prob). VAD is env-tunable (VAD_THRESHOLD,
VAD_MIN_SPEECH_MS, VAD_ENABLED) and fail-open so a VAD error never swallows a
real utterance. Validated on real audio: synthesised Korean speech passes;
silence, a 50ms blip and white noise are rejected.
- Broadcast auto-start no longer blocks the voice join and no longer silently
swallows failures: wiring is synchronous, the Go-Live start runs in the
background with a bounded retry and a loud final-failure log.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The bridge transcribe path joined every Whisper segment unconditionally, so a
brief loud sound or background noise that momentarily opened the mic gate (no
real speech) still produced a transcript, and Whisper's noise hallucinations
("감사합니다", "MBC 뉴스", ...) made the bot reply to nothing.
Add bridge/stt_filter.py mirroring the desktop listener's _filter_noisy_segments
policy: a hard no_speech_prob cutoff (whisper_no_speech_threshold) plus an
avg_logprob confidence floor (whisper_min_confidence), both config-driven. Apply
it in transcribe() so only segments that look like human speech survive; a
noise-only turn yields an empty transcript and the existing empty-transcript
guard drops it with no reply. Add unit tests for the gate.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The streaming splitter treated every "." as a sentence boundary, so the
operational reply "17.5°C" was read as "17." / "5°C" and "1.8 km/h" as
"1." / "8 km/h" - numbers spoken digit-by-digit plus extra TTS calls.
An ASCII terminator (. ! ?) now only ends a sentence when it is followed by
whitespace, a closing quote/bracket, or end of text. In-token dots (decimals
"17.5", versions "v2.0", hosts "example.com") are followed by a digit/letter,
so they no longer split. CJK fullwidth terminators stay unconditional since
those scripts use no trailing space. Language-agnostic, punctuation only.
- bridge: lookahead-gated boundary regex + finditer-based chunking
- tests: regression cases for decimals (17.5/1.8), versions, URLs, and an
integer that genuinely ends a sentence
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The /converse turn synthesised the entire reply before any audio played, so
time-to-first-audio grew with reply length. Add a streaming /converse_stream
endpoint that emits the transcript/reply first, then one audio clip per
sentence as each finishes synthesising. The Discord voice layer enqueues each
clip on arrival via the existing FIFO playQueue, so the first sentence starts
speaking while the rest are still being synthesised.
STT and the reply engine still run to completion before the first clip; only
TTS is pipelined. The non-streaming /converse and /text endpoints are
unchanged.
- bridge: language-agnostic sentence splitter (bridge/text_utils.py) + NDJSON
streaming route
- bot: ndjson() reader + converseStream() client; voice.ts plays clips
progressively
- tests: splitter unit tests + bot ndjson/converseStream tests
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Empirical A/B/C measurement against the live RTX 5050 Ollama stack
(qwen2.5:3b + nomic-embed-text) showed keep_alive=0 unloads the embed
model ~2s after every call, so each turn after a brief idle gap pays a
cold reload. VRAM is not the constraint (~4.4-4.7 GB free with both
models resident) and keep_alive=0 never evicted the chat model, so CPU
embedding (num_gpu=0) gave no benefit. A short positive keep_alive is
the fastest of the three: it keeps the ~0.3 GB embed model resident
across consecutive turns at negligible VRAM cost.
Add tests/test_embeddings.py covering the warm-across-turns behaviour.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Open-Meteo's geocoder only matches Latin/English spellings, so a Korean city
name like "서울" returns zero results even though the place exists. With
OUTPUT_LANGUAGE locked to Korean the tool-calling model naturally fills
`location` with the Korean name, which dead-ended on every weather request
("could not find location").
When the first geocode is empty and the name is non-ASCII, ask the warm small
model for the common English exonym ("서울" -> "Seoul") and retry once. ASCII
names skip the round-trip entirely.
Live-verified: "서울 날씨 알려줘" now returns real Seoul weather. Tests cover the
romanise-and-retry path and the ASCII short-circuit.
qwen2.5:3b emits tool calls in text shapes the parser dropped, breaking
two reviewer-reported behaviours:
- `getWeather: {"location": "Seoul"}` (a JSON object after the colon) was
dumped wholesale into {"query": "{...}"}, so `location` never reached the
tool. getWeather then ran with empty args, returned the auto-detected
location's weather, the model noticed the mismatch and retried — looping up
to 8 times before giving up with an English error. Now the JSON object after
the colon is parsed directly as the argument dict.
- `call_stop: {"id":..., "function": {"name": "setBroadcast",
"arguments": "{\"action\": \"stop\"}"}}` — a single tool_call object without
the `tool_calls: [...]` array wrapper, behind a `call_xxx:` label — matched
no form, so the raw JSON leaked to the user AND setBroadcast never ran
("방송 꺼줘" did nothing). Now name + arguments are pulled from the embedded
`function` object when the name is in the allow-list.
Field-captured from the live qwen2.5:3b brain (2026-06-12). Tests cover both
shapes, non-ASCII args, dict/string arguments, and unknown-tool rejection.
Harden the reply-language lock so qwen2.5:3b reliably stays in the locked
language instead of leaking the query language back in:
- reply_language_directive(): single resolver with clear precedence —
explicit OUTPUT_LANGUAGE lock wins over the Piper/Chatterbox English-only
fallback (this deployment's actual TTS is Korean MeloTTS, so the legacy
English lock was both wrong and contradicting the Korean lock).
- Stronger, override-explicit directive wording, inserted near the FRONT of
the system prompt so a small model gives it primacy over the persona.
- build_system_prompt(output_language=...): rewrite the persona's "in the
user's language" clause to the locked language so the persona stops
fighting the lock.
- docs/llm_contexts.md: document the resolver, precedence, and placement.
Live-verified on the running brain (qwen2.5:3b): Korean voice-style input
and a cold English query both return fully Korean replies with no CJK/Hanja
leak. Tests cover unset/set/agnostic/whitespace + precedence + persona rewrite.
Add an optional OUTPUT_LANGUAGE env var that forces every reply into a
single language. When set, output_language_directive() injects a "respond
only in <language>" instruction (also forbidding other scripts) into the
chat loop's system prompt, next to the existing TTS English-only lock.
Empty (default) keeps the multilingual "reply in the user's language"
behaviour, so upstream is unaffected.
For the Korean-only deployment this also suppresses the occasional trailing
CJK/Hanja fragment qwen2.5:3b leaks on free-form chit-chat.
- system_prompt.py: language-agnostic output_language_directive() helper
- engine.py: read OUTPUT_LANGUAGE, append directive in _build_initial_system_message
- docker-compose.yml + .env.example: document/pass the new var
- docs/llm_contexts.md: note the new gating on the main reply context
- tests: cover unset/set/agnostic/whitespace cases
Completes the STREAM_BROWSER=true behaviour:
- handleJoin auto-starts the broadcast on voice join and wires the session to
the guild streamer; each turn reports the live state to the brain so search
routes Chrome (live) vs Gemini (off).
- New setBroadcast tool lets the user toggle the broadcast by voice ("방송
켜줘/꺼줘") via the LLM (no hardcoded phrases); it refuses when
STREAM_BROWSER=false. The directive flows brain -> bridge (broadcast_action)
-> bot streamer.start/stop, guarded by isActive() so it's idempotent.
- Per-turn IPC uses a thread-local (reply/turn_state.py) instead of threading
params through the whole engine chain: bridge sets broadcasting in, tool
records the directive out; Tool.execute exposes broadcasting on ToolContext.
Bot typecheck clean; brain covered by tests/test_set_broadcast.py (+ existing
routing tests). Specs + docs updated.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
STREAM_BROWSER becomes the broadcast *capability* master flag; the live
screen-share state (new ToolContext.broadcasting, passed per turn by the bot)
decides the backend:
- master off -> broadcast disabled, always Gemini
- master on + live on -> on-screen Chrome (visible on the stream)
- master on + live off -> Gemini
context.broadcasting is None outside the voice path (evals, text entry, older
bot) and falls back to the master flag, so current behaviour is unchanged.
This is the brain-side foundation; bot-side wiring (bridge passes broadcast
state, auto-broadcast on voice join, voice on/off toggle) follows.
Specs + docs/llm_contexts.md updated. Covered by
tests/test_web_search_broadcast_routing.py.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
yolo auto-approves every tool call, so a real-time search query could in
principle trigger write/shell tools. Default approval mode still auto-runs the
CLI's read-only web search in headless mode but never silently approves
destructive tools. Verified end-to-end: a grounded query returns a current
answer in ~23s with the account OAuth login. Test asserts yolo is absent;
specs and docs/llm_contexts.md updated.
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>
