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: 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.
- 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.
Add AssemblyAI as a third transcription provider option alongside
OpenAI and Groq. AssemblyAI offers better accuracy for certain
audio types (distant voices, noisy environments) and serves as a
reliable fallback when other providers struggle.
Changes:
- Add AssemblyAITranscriptionProvider class in providers/transcription.py
- Add 'assemblyai' option in base channel's transcribe_audio()
- Per-channel configuration via transcriptionProvider in config
Usage:
Set transcriptionProvider: 'assemblyai' and provide an AssemblyAI
API key via transcriptionApiKey in the channel config.
Add support for Xiaomi MiMo ASR as a third transcription backend alongside
Groq and OpenAI Whisper. Xiaomi ASR uses the /v1/chat/completions endpoint
with base64-encoded audio input, rather than the standard Whisper multipart
upload format.
Co-Authored-By:连 <lian@tangping.homes>
Add a `transcriptionModel` channel setting and an OpenRouter transcription
backend so voice messages can be transcribed through OpenRouter's
speech-to-text endpoint (e.g. nvidia/parakeet-tdt-0.6b-v3, openai/whisper-1),
alongside the existing Groq/OpenAI Whisper providers.
- schema: add channels.transcriptionModel (None = provider default)
- providers/transcription: extract a shared POST/retry skeleton; add a
JSON+base64 OpenRouterTranscriptionProvider; make the STT model a
constructor param on all providers instead of hardcoding it
- channels: route transcriptionProvider="openrouter" and thread the model
through the manager to each channel
- docs + tests
Only dedicated STT models work on OpenRouter's transcription endpoint;
chat LLMs (e.g. google/gemini-3.5-flash) are rejected there.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds ProviderConfig.extra_query, threaded into AsyncOpenAI(default_query)
so that Azure-style gateways requiring query params like api-version can
be configured without URL hacks.
Also updates provider_signature to track extra_query changes so per-turn
refresh rebuilds the provider when the value changes.
Addresses the extra_query portion of #4204. The max_completion_tokens
model-awareness enhancement is intentionally left separate.
Maintainer edit: make the unsafe redirect regression go through connect_mcp_servers so both SSE and streamable HTTP prove that the request hook is attached to the MCP clients before redirects are followed.
maintainer edit: add SDK-object and tool-call history regressions so the empty-string reasoning_content fix is covered across both parse branches and the sanitized request path.
Custom providers (e.g. DeepSeek) may return reasoning_content as an
empty string "" to explicitly indicate no reasoning occurred. The
previous truthiness checks (, ) treated "" as falsy
and converted it to None, which caused the field to be dropped from
the message history entirely. Providers that require reasoning_content
on all assistant messages then rejected subsequent requests.
Replace truthiness checks with identity checks () so that
empty-string reasoning_content is preserved as-is. The streaming path
is unchanged since an empty join genuinely means no chunks received.
Fixes#4105
