## Summary
- add an opt-in local `smoke/` pytest suite for API, auth, providers,
CLI, IDE-shaped requests, messaging, voice, tools, and thinking stream
contracts
- keep smoke tests out of normal CI collection with `testpaths =
["tests"]`
- write sanitized smoke artifacts under `.smoke-results/`
## Verification
- `uv run ruff format`
- `uv run ruff check`
- `uv run ty check`
- `uv run ty check smoke`
- `FCC_LIVE_SMOKE=1 FCC_SMOKE_TARGETS=all FCC_SMOKE_RUN_VOICE=1 uv run
pytest smoke -n 0 -m live -s --tb=short` -> 17 passed, 9 skipped
- `uv run pytest` -> 904 passed
## Notes
- Skipped live checks require local credentials/tools/services, such as
provider models, Telegram/Discord targets, voice backend, or Claude CLI.
- `claude-pick` smoke was intentionally removed.
Add proxy support for providers based on
[doc](https://www.python-httpx.org/advanced/proxies/):
- Add per-provider proxy support (HTTP and SOCKS5) for all 4 providers:
nvidia_nim, open_router, lmstudio, llamacpp
- Each provider gets its own env var (NVIDIA_NIM_PROXY,
OPENROUTER_PROXY, LMSTUDIO_PROXY, LLAMACPP_PROXY) for independent proxy
configuration
---------
Co-authored-by: Alishahryar1 <alishahryar2@gmail.com>
## Summary
Added NVIDIA NIM as a second transcription option ( alongside local
Whisper). This lets you transcribe voice notes using NVIDIA's cloud API
instead of running Whisper locally.
## What changed
- **Transcription**: Now supports the two backends
- Local Whisper: Free, runs on your GPU/CPU (existing)
- NVIDIA NIM: Cloud API via Riva gRPC (new)
- **Supported models**: 8 NVIDIA NIM models added (Parakeet variants for
different languages, Whisper Large V3)
---------
Co-authored-by: Alishahryar1 <alishahryar2@gmail.com>
- Updated the pyproject.toml to include allowed unresolved imports for optional voice dependencies: torch, transformers, and librosa.
- This change facilitates smoother integration in CI environments where these packages may not be installed.
- Updated transcription logic to utilize Hugging Face's Whisper models instead of faster-whisper.
- Introduced new model mapping and pipeline loading functions.
- Adjusted tests to reflect changes in the transcription process.
- Updated documentation in README, .env.example, and settings to align with the new implementation.
- Ensured compatibility with CUDA 13 and removed unnecessary dependencies.
- Introduced voice note handling for Discord and Telegram platforms.
- Added configuration options for voice note functionality in settings.py and .env.example.
- Updated README to include voice note instructions and configuration details.
- Implemented audio attachment processing and transcription using faster-whisper.
- Enabled voice note support through message handlers in both platforms.
