config.loader.load_config() intentionally returns the raw config with ${VAR}
references intact — env interpolation is a separate, explicit step
(resolve_config_env_vars) so that settings read/edit/save paths never
materialize secrets to disk or to the UI.
The transcription config path does not apply that step: both
channels/base.py (channel voice notes) and webui/transcription_ws.py (WebUI
recording) build their effective config via
resolve_transcription_config(load_config()). As a result a configured
api_key of "${GROQ_API_KEY}" (the documented way to reference secrets) is
passed to the provider verbatim, which fails with 401 Invalid API Key. No
amount of rotating the real key helps, because the literal placeholder
string is what gets sent.
Resolve the reference at the single choke point both callers share —
_resolve_transcription_api_key / _resolve_transcription_api_base — using a
new lenient loader.resolve_env_refs() helper (unset var -> empty string, so
a missing variable degrades to "not configured" rather than raising or
leaking). This fixes both entry points at once and cannot drift the way a
per-call-site fix does. Resolving inside load_config() was rejected: the
~20 settings-UI callers depend on it returning raw ${VAR} placeholders.
Literal keys are unaffected; the settings API only reads the derived
`configured` flag (never the key), which now reflects the resolved value.
Claude-Session: https://claude.ai/code/session_01Q3HuVaJAAQJA3kgVQVJ2Zt
_get_copilot_access_token had a check-then-act race: concurrent chat()
calls after token expiry both fetched new tokens and clobbered each other.
Add asyncio.Lock with double-checked locking so only one fetch happens
per expiry window.
Closes#4677
Maintainer edit: add mocked coverage for the enterprise endpoint and client ID override paths, and document the environment variables users must set before OAuth login.
Widen thinking_style from Literal to str | None and add a
@field_validator that produces a helpful error message listing
valid options when an invalid value is provided.
Addresses the review feedback on #4482.
ProviderConfig.thinking_style defaults to None (Optional field), but
create_dynamic_spec expects a string. Coalesce None to "" at all call
sites (factory.py, settings_api.py) and fix the test assertion to
expect None from the config default.
Maintainer edit: document OpenCode Zen and Go configuration, keep their registry entries with gateway providers, and add focused provider registration tests.
maintainer edit: move duplicate tool_use history repair out of AgentRunner and into Anthropic message conversion, reusing the OpenAI-compatible queue-mapping approach locally without broadening the shared runner path.
maintainer edit: remap duplicate tool_use/tool_call ids instead of dropping later calls, so Anthropic-compatible providers that reuse ids for distinct parallel tool calls keep all requested work while still sending unique ids.
The _strip_image_content methods now use a fixed non-descriptive
placeholder instead of path-derived text. Update the 3 existing
test_provider_retry assertions to match the new placeholder format.
The image-strip fallback (triggered when a model errors on image input)
replaced image_url blocks with [image: <path>] or [image omitted]. Both
read like a live, available image to the LLM, causing it to:
1. hallucinate about image contents it never received
2. attempt read_file on the leaked server path
3. expose internal file paths to the model
Replace with an explicit '[Image not delivered to model — do not describe
or reference it]' placeholder that tells the LLM the image was stripped.
Fixes#4345
Mistral's API constrains reasoning_effort to "high"/"none", rejects the
kwarg entirely for Magistral (reasoning is implicit), returns assistant
content as a mixed array of {type:"thinking",...}/{type:"text",...}
blocks, and 400s on the reasoning_content key in history.
- Remap user-supplied reasoning_effort (low/medium/minimal) onto Mistral's
two-tier vocabulary; strip the kwarg for Magistral models
- Lift thinking blocks into reasoning_content for both batch and streaming
responses; pass only text through on_content_delta callbacks
- Drop reasoning_content from outbound history when the spec asks for it
- Expose per-preset reasoning_effort_values so the UI can render the
provider-specific option set
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
chengyongru reviewed #4367 and identified that the cloud branch
created a bare httpx.AsyncClient that lacked the SDK's default settings
(follow_redirects, connection pool limits). Since the SDK's
DefaultAsyncHttpxClient already has trust_env=True and proper defaults,
the simplest fix is to let http_client stay None for cloud endpoints.
Also updated the test to match the new behavior (http_client is None).
When the host has HTTP_PROXY / HTTPS_PROXY / ALL_PROXY set, httpx routes
all traffic through the proxy — including requests to localhost or LAN
addresses that the proxy typically cannot reach. This breaks local model
servers (Ollama, llama.cpp, vLLM) silently.
- Local endpoints: pass transport=httpx.AsyncHTTPTransport(proxy=None)
so proxy env vars are ignored for local traffic.
- Cloud endpoints: pass trust_env=True so corporate/VPN proxies work
without explicit configuration.
Fixes#4366
The temperature suppression was hardcoded to only match opus-4-7. Newer
Anthropic models (opus-4-8, fable) also reject the parameter with a 400.
Normalize model_name to lowercase before matching so mixed-case configs
do not fall through. Add tests for opus-4-8 and fable across adaptive,
enabled, and no-thinking paths, plus a negative test confirming ordinary
models still send temperature.
Fixes#4333
- 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.
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