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Raising GLOBAL_LOG_LEVEL to WARNING buys quieter output but not less work: 241 INFO call sites interpolate their payload into an f-string before the logging call gets to drop it. The heaviest is get_doc, which logs every chunk id and metadata dict in a collection, so on the full-context retrieval path that is the entire knowledge base, once per chat request.
That one line at WARNING, CPython 3.12:
| knowledge base | payload | before | after |
| -------------- | ------- | -------- | ------- |
| top-k of 3 | 1.2 kB | 3.8 us | 0.07 us |
| 500 chunks | 201 kB | 583.6 us | 0.08 us |
| 5000 chunks | 2.0 MB | 5.8 ms | 0.15 us |
The lazy form log.info('query_doc:result %s %s', result.ids, result.metadatas) hands the payload to record.getMessage(), which the InterceptHandler only reaches once a record has passed the level check. Output at INFO is byte-identical. Two sites that already built their message eagerly, one str concat and one % operator, move to the same lazy form.
GLOBAL_LOG_LEVEL defaults to INFO, so every log.debug(...) in the backend is discarded, but the message is built first: 187 call sites interpolate their payload into an f-string before the logging call runs, so the work happens on every request and the result is thrown away. The worst one sits in process_chat_payload and stringifies the whole request body, full conversation history included, once per chat completion.
That one line with DEBUG disabled, CPython 3.12:
| conversation | payload | before | after |
| ------------ | ------- | -------- | ------- |
| 4 messages | 1.2 kB | 3.4 us | 0.07 us |
| 20 messages | 17 kB | 24.8 us | 0.07 us |
| 60 messages | 123 kB | 216.6 us | 0.07 us |
The lazy form log.debug('form_data: %s', form_data) hands the payload to record.getMessage(), which the InterceptHandler only reaches once a record has passed the level check. With DEBUG enabled the emitted lines are byte-identical, f'{x=}' sites included: those map to %r. MistralLoader._debug_log callers get the same treatment, since that wrapper already forwards *args.
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When an audio file is split into multiple chunks for transcription, transcribe() collected the per-chunk results with asyncio.as_completed(), which yields results in completion order rather than submission order. Whenever a later chunk finished transcribing before an earlier one, the assembled transcript was scrambled, for example the second half of a recording appearing before the first, and the stored file content plus everything downstream (file preview, full-context retrieval) read out of chronological order. This change awaits the chunk tasks with asyncio.gather() instead, which runs them just as concurrently but returns the results in the order the tasks were created, i.e. chunk_paths order. The existing error handling and chunk cleanup are unchanged: an HTTPException from a chunk is re-raised as is and any other error is wrapped in a 500. Fixes#27143
Audit of asyncio.sleep vs time.sleep and event-loop-blocking calls:
- utils/plugin.py: run pip `install_frontmatter_requirements`
(subprocess.check_call) via asyncio.to_thread in load_tool_module_by_id,
load_function_module_by_id, and install_tool_and_function_dependencies.
- retrieval/utils.py: move the synchronous SSRF-guarded requests probe and
loader.load() in get_content_from_url into a sync helper run via
asyncio.to_thread.
- routers/audio.py: write uploaded audio to disk off the event loop in
transcription().
- routers/pipelines.py: write uploaded pipeline file off the event loop in
upload_pipeline().
The existing time.sleep call sites are all in genuinely synchronous
functions (sync requests/DB drivers/daemon threads) with async
counterparts that already use asyncio.sleep, so no time.sleep -> asyncio.sleep
changes were needed.
Claude-Session: https://claude.ai/code/session_01LXR5bYfsfSS42RGHQZu2Ta
Co-authored-by: Claude <noreply@anthropic.com>
The Azure TTS handler (_tts_azure) interpolated the user-supplied voice,
and the locale derived from it, into the SSML xml:lang and <voice name>
attributes without XML-escaping, while the text body was already escaped
(2e75c6dbd). Escape both attributes too, so every user-derived value in
the SSML document is consistently encoded.
Co-authored-by: alanturing881 <alanturing881@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
pydub imports the stdlib `audioop`, removed in Python 3.13, so audio
preprocessing would break there. requires-python is already capped at
< 3.13; this one-line pointer flags what to handle (audioop-lts, or drop
pydub) before raising that cap.
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The transcription endpoint was async but called the synchronous transcribe() function directly, blocking the single-threaded uvicorn event loop for the entire duration of inference. This caused all HTTP and WebSocket connections to stall for every user on the instance during STT processing.
- Add asyncio import
- Use async UploadFile.read() instead of synchronous file.file.read()
- Offload the blocking transcribe() call via asyncio.to_thread()
Closes#24169
The locale for Azure TTS SSML was being extracted with `split("-")[:1]`,
which only takes the first segment (e.g., "en" from "en-US"). The
xml:lang attribute in SSML requires a full locale like "en-US", not just
a language code. This caused Azure TTS to either fail or use incorrect
pronunciation rules.
Changed `[:1]` to `[:2]` to properly extract the locale (e.g., "en-US").
Co-authored-by: gambletan <ethanchang32@gmail.com>
- Use os.path.basename() for filename sanitization instead of fragile blocklist
- Replace ERROR_MESSAGES.DEFAULT(e) with generic error message in both except blocks to prevent CWE-209 information disclosure
- Server-side logging via log.exception(e) is preserved for debugging
