maintainer edit: reject arbitrary custom provider keys that normalize to built-in provider names so runtime and WebUI settings cannot disagree about whether a provider is dynamic or built in.
maintainer edit: preserve provider-prefix CLI routing for named custom providers by stripping only the matched dynamic route prefix before sending the model id to OpenAI-compatible endpoints. This keeps ordinary namespaced model ids intact when the provider is selected explicitly.
maintainer edit: spell out that arbitrary named custom providers use the OpenAI-compatible request format only, and point Anthropic-compatible proxies to the built-in anthropic provider with apiBase.
maintainer edit: explain how to configure arbitrary OpenAI-compatible provider names, including multiple endpoints, model presets, and troubleshooting guidance.
maintainer edit: keep the WebUI dynamic-provider behavior unchanged while reducing repeated test setup and tightening the small dynamic-provider helper.
maintainer edit: WebUI settings still treated non-registry custom providers as unknown, so users could not select them in model configurations or fetch their model list. Reuse dynamic provider specs for settings payloads, model-list requests, and provider updates.
maintainer edit: treat arbitrary custom provider names as direct OpenAI-compatible providers, validate their api_type consistently, and avoid Pydantic instance-field warnings in fallback routing.
Slack's groupPolicy could either restrict to specific channels
("allowlist") or require an @mention ("mention"), but not both: in
allowlist mode the bot replied to every message in approved channels.
Add a groupRequireMention flag so that, when groupPolicy is "allowlist",
the bot only responds in channels listed in groupAllowFrom AND only when
@mentioned. Mirrors Signal's group.requireMention. No effect for the
"mention"/"open" policies, so existing configs are unchanged.
Extract the mention check into _is_mention and reuse it from both the
mention and allowlist branches.
Co-authored-by: Cursor <cursoragent@cursor.com>
Maintainer edit: split final streamed Telegram markdown before rendering to HTML so long fenced code blocks do not produce unbalanced <pre><code> chunks while still respecting Telegram's rendered HTML limit.
Move the fenced-code-block-aware splitting logic out of the shared
split_message helper (used by Signal, Slack, Discord, Weixin, etc.)
and into a Telegram-specific _split_telegram_markdown function.
The shared split_message remains a plain-text chunker. The Telegram
channel now uses _split_telegram_markdown for its raw Markdown paths
that feed _markdown_to_telegram_html, preventing broken HTML rendering
when splits fall inside fenced code blocks.
Also fixes a regression where content beginning with whitespace before
a fence could emit a whitespace-only chunk.
Addresses review feedback on #4257.
When split_message splits a long message, it now checks whether the
split point falls inside a fenced code block. If so, it either moves
the split to before the opening fence or closes/reopens the fence
across chunks, preventing broken HTML rendering.
Addresses #4250
- Register SiliconFlow in transcription registry with default model
FunAudioLLM/SenseVoiceSmall and alias 'silicon'
- Reuse existing OpenAITranscriptionProvider adapter (Whisper-compatible)
- Add generic key/base resolution: fallback to registry env_key and
default_api_base when provider config is absent
- Add tests for registry entry, alias, adapter, default model, and
config resolution with env var fallback
maintainer edit: streamed timeout recovery was returning the retried response internally while the channel still treated the final outbound as already streamed. End the current stream segment before retry/fallback recovery so subsequent deltas are delivered in a new segment.
When a stream stalls mid-response, both the retry layer and
FallbackProvider blocked recovery because content had already been
emitted via on_content_delta. This left users with truncated replies
and no automatic recovery.
For error_kind="timeout" specifically:
- _run_with_retry now suppresses delta callbacks and retries the same
model instead of returning immediately
- FallbackProvider now allows failover to a different model with
delta callbacks suppressed
Non-timeout errors retain the original "skip retry/failover after
streamed content" behavior to avoid duplicate output.
The channel manager coalesces consecutive _stream_delta messages and
forwards a single merged message with _stream_end=True. In that path
no individual delta events ever reach the WebUI client, so the
stream_end frame is the only carrier of the text. The previous guard
only attached text when media-URL rewriting changed the string, which
silently dropped entire turns of plain-text output whenever the
agent generated tokens faster than the queue drained.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add StepFunTranscriptionProvider class in nanobot/providers/transcription.py
- New _post_stepfun_asr_with_retry() function handling SSE stream parsing
(transcript.text.delta → transcript.text.done event sequence)
- Register 'stepfun' in transcription_registry.py with default model stepaudio-2.5-asr
- Reuse existing stepfun provider config (apiBase can point to Plan endpoint)
- Add 17 tests covering SSE parsing, retry contract, empty-text edge case, and registry integration
- Update docs/configuration.md with stepfun ASR documentation
StepFun ASR uses a dedicated SSE endpoint (/v1/audio/asr/sse) rather
than the chat-completions or Whisper multipart formats used by other
providers. Users on Step Plan can set apiBase to the Plan endpoint.
Maintainer edit: keep the GPT-5/o-series fallback on slug-boundary matching so unrelated model names are not caught by substring checks, and include o1 alongside o3/o4 because it is also an o-series chat model.
