Every streamed token appended to the response text through a dict slot, which allocates a new string and copies everything received so far. A long answer pays that copy thousands of times on the event loop, so every other request on the worker waits behind it.
The appends now clear the slot before growing the string, which leaves a single reference and lets CPython resize it in place. This covers the six accumulation points in the Chat Completions streaming path. The Responses API transport copies its items before appending, so it holds two references and is unchanged.
Measured on CPython 3.12 with 10-character deltas:
| accumulated text | before | after |
|---|---|---|
| 100 KB | 5.97 ms | 0.98 ms |
| 250 KB | 29.53 ms | 2.83 ms |
| 1 MB | 659 ms | 15.5 ms |
Results match the replaced expressions for every input where the key exists and holds a string, key order and exception type included. Buffering into a list and joining at the end was the alternative, but each delta is read back immediately by the stream save, and joining per read measured 666 ms at 250 KB.
75 entries in the French locale had an empty value. i18next falls back to the
key when a value is empty, so French users were shown raw English strings
across permission settings, empty states, form placeholders and error toasts.
- fill those 75 entries; no key is added, removed or reordered
- follow the conventions already present in the file: "Entrez ..." for input
hints, "Échec de ..." for failures, Chat -> Conversation, Token left as is
- interpolation placeholders preserved; no existing translation modified
`x in d` and `x in d.keys()` are identical for a plain dict, so the `.keys()` call builds a throwaway view and reads as if it were doing something. Both sites operate on a plain dict: `combined` in `merge_and_sort_query_results` is a local `dict()`, and `ui_settings` comes from `UserSettings.model_dump()` where `ui` is annotated `dict | None` and is already guarded against None on the preceding line.
No behaviour change, and no measurable speedup either, so this is a readability cleanup rather than a performance one.
Sites where `.keys()` is load-bearing are left alone: the `list(d.keys())` snapshots taken before mutating during iteration, and the places where `.keys()` is the iteration or comprehension source rather than a membership test.
A stream filter function, or a provider that puts something other than a string in a delta, makes the streaming handler concatenate a string with a non-string. That raises TypeError, and the broad handler wrapped around the whole per-chunk block swallows it at debug level and moves on. The chunk's text never reaches the message the user sees, and nothing above debug level says why.
The content and reasoning fields are now coerced to text once, where they are read off the delta, ahead of every consumer. The coercion is guarded on truthiness, so falsy values such as an empty list still skip the block exactly as before, and the accumulated content receives byte for byte what it received previously.
Checked against 14 delta shapes covering strings, empty values, numbers, booleans, None, lists, dicts and a content array: the truthiness gate and the accumulated content are identical before and after.
The ComfyUI workflow.json Edit drawer (CodeEditorModal -> Drawer) used
z-999, while the admin Settings dialog (Modal) uses z-9999. Since both
are appended to <body>, the drawer rendered behind the settings dialog
and appeared to open 'in the background' (#27647).
Add an optional zIndexClass prop to Drawer (default z-999, preserving
existing behaviour for all other callers) and pass z-99999 from
CodeEditorModal so the editor surfaces above any enclosing modal.
Task lists in Notes serialized to markdown as `- [ ] [ ]` with the item text pushed onto a separate line after a blank line, so previewing or downloading a note produced a broken checklist, and checking an item left the second `[ ]` behind as plain text.
TipTap renders each task item as a checkbox inside a label plus a block-wrapped body. The GFM turndown plugin matches that checkbox and emits its own `[ ]`, which landed next to the marker the task item rule already writes, and the block wrapper left blank lines around the text that the old leading-whitespace strip could not remove.
Register a rule that drops the checkbox so the task item rule is the only source of the marker, and trim the block wrapper while indenting continuation lines so nested lists and code fences stay inside the item.
Fixes#26067
The web search error message was a lambda with a passthrough branch that returned whatever it was handed. Since #28942 both call sites pass no arguments, so that branch is unreachable, and it is the trap that let a caller drop a raw exception object into an HTTP response body and turn an intended 400 into an unserialisable 500.
A plain string constant removes the trap and lines the message up with every other fixed message in that file. Behaviour is unchanged: the response detail comes out byte for byte identical, because the enum already overrides __str__ to render members as their value. Verified on Python 3.11 and 3.12, both producing the same string and the same JSON body.
Any failure during a web search comes back to the client as a bare HTTP 500 with nothing in it. The handler tries to build a 400 whose detail is the caught exception object itself, FastAPI cannot serialise that into a response body, so rendering the error response fails and the request falls through to the generic 500 handler. In chat this surfaces as a web search that fails with no explanation at all, and the most common trigger is simply selecting a search engine without configuring its API key.
This routes the failure through the standard error formatter, which is what the sibling handler for content loading failures in the same function already does. Web search failures now return 400 with a readable message, and the exception itself keeps going to the server log exactly as before.
Passing str(e) into the response was the other option and was rejected: the rest of the backend deliberately keeps provider exception text out of client responses and in the log, and provider exceptions here can carry request details that should not be echoed back.
The DuckDuckGo search path catches RatelimitException from the ddgs library. That exception is defined by the library but never raised anywhere in it, checked against the pinned 9.14.4 and against 9.11.3, so the handler could never run. The two fallbacks around it were dead for the same reason: ddgs.text() returns a non-empty list or raises, so None and an empty list are not outcomes it can produce.
Removing all three leaves one call and changes nothing observable. A refused or rate limited search already came out as a failed search, with the error shown to the user and the traceback in the log, and it still does.
The backend argument is now passed as backend or 'auto' rather than conditionally omitted, because 'auto' is the library's own default for that parameter, so every configured value including unset and empty resolves exactly as before. Verified by running the old and the new function side by side against a stubbed library covering normal results, the domain filter, all four backend settings and a failing search, with identical results in every case.
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Two components under the admin settings area are not imported by any route or component. The admin settings shell went dead when the admin settings route became a redirect into the settings modal, which imports every admin tab directly and carries its own tab list and search. The model selector beside it lost its last importer in a separate models refactor. Every child component the shell used is still imported by the settings modal, so nothing goes with them.
This removes around 590 lines that still turn up in every search across the admin area.
The Mistral OCR loader has a full async pipeline beside its synchronous one: an async load, its own upload, signed URL, OCR, delete and retry helpers, a pooled session and a batch loader on top. The only way in was the batch loader, which nothing calls, so the entire async half was unreachable. Everything that loads documents goes through the synchronous path, and the shared loader entry point runs it in a worker thread. The Datalab loader carries a public request status poller with no caller either, since its own load inlines the polling it needs.
With the async half gone, the retry classifier's two aiohttp branches can no longer be reached, since the only retried calls are synchronous, so those go with it along with the aiohttp import that existed solely to feed them, and a timeout attribute that nothing reads any more. The class docstring loses the three bullets that only described the removed pipeline, and four docstrings stop calling themselves the sync version of something that no longer has an async counterpart.
This removes around 350 lines and leaves one code path per loader instead of one live path and one that cannot be entered.
With "show emoji in call" enabled, voice mode stayed completely silent and no request ever reached the configured TTS server. Reasoning models served with a reasoning parser return `message.content` as null and put the text in `reasoning_content`, and the emoji helper called `.replace()` on that null value and threw.
The call overlay ran the emoji request first, inside the same `try` block as speech synthesis, so that error skipped the entire TTS section. The audio cache was never filled, and the playback loop kept re-queueing the same content every 200 ms without ever playing it. Read aloud was unaffected because it synthesizes speech directly, which is why the failure looked specific to voice mode.
Fixed on both sides: the optional chain in `generateEmoji` now covers `content`, and the emoji request in the call overlay gets its own catch, matching the speech synthesis call directly below it. An emoji failure now costs the emoji instead of the whole reply.
With WEBSOCKET_MANAGER=redis on a multi-node deployment, the usage pool cleanup task could stop permanently for the whole cluster. Nodes that lost the startup lock race gave up for good after three attempts, and the winner died on a single failed renew or on any Redis connection error, releasing the lock with nobody left to take it over. From then on expired entries accumulated in the usage pool until a node restarted, so /api/usage over-reported models in use and every disconnect handler walked an ever-growing pool.
The task now retries lock acquisition forever like the session pool cleanup does, and any error is logged and answered by releasing the lock and returning to acquisition, so a transient failure costs one cleanup cycle and every node stays a takeover candidate. The delete of an emptied model entry is KeyError-guarded because a disconnect handler on another node can remove the same key between the sweep's snapshot and its delete; unguarded, that race was a permanent task killer that needed nothing rarer than a chat finishing while its tab closed.
Saving a streaming response serialized the payload with orjson, decoded it to
str, scanned it for the three Unicode line separators and let redis-py encode
it straight back to UTF-8: on an 8 MB non-ASCII chat that is 6.9 ms and ~22 MB
of transient buffers per write, synchronously on the event loop.
json_codec now exposes dumps_bytes, which returns the serialized payload as
UTF-8 bytes without the line-separator escaping, and the two Redis writes in
tasks.py use it. That escaping only protects line-framed protocols such as
SSE; every reader of these Redis values re-parses them before anything is
served, and the escaped and raw forms parse identically, so mixed versions
during a rolling deploy interoperate both ways. The same write drops to
0.9 ms and one 8 MB buffer (7.5x), with 31-66% saved on KB-sized writes.
With ENABLE_ORJSON off, dumps_bytes wraps stdlib json, behaviour unchanged.
The str path keeps the escaping but applies it with chained str.replace
instead of a translate table, cutting a separator-containing 8 MB payload
from 312 ms to 5.7 ms with byte-identical output.
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Exporting workspace models loaded every model row, built a full response object with its owner for each, and only then dropped the ones the caller may not see. On a large model table that made the export endpoint slow in proportion to models the user cannot even access.
The owner-or-grant check now happens in the query itself, reusing the permission filter this file already applies to the paginated list endpoint, so only visible rows are ever hydrated. The by-user wrapper had one caller left and is gone with it.
Measured with 500 workspace models of which 3 are visible to the caller: 5 queries and ~12.7 ms before, 4 queries and ~2.8 ms after. The resulting set is unchanged for owner, public, direct-user, group and multi-grant entries, and base model entries stay excluded as before.
Every streamed event that persists to a chat (status updates, citations,
file attachments, message content) serialized the entire conversation JSON
three times: a null-byte check of the stored row, a second sanitize of the
whole blob after merging in the event payload and the flush of the UPDATE
itself. The middle pass rescans megabytes of already-clean history for null
bytes that can only come from the small incoming payload, so long chats pay
for their full history on every single event.
The write paths now sanitize just the incoming message, message id and
status dict and keep the row-level sanitize, so legacy rows with null bytes
still self-heal as before. Median per-event write time (sqlite, orjson):
1 MB chat 15.0 ms to 11.1 ms, 4 MB 65.2 ms to 52.4 ms, 10 MB 159.9 ms to
124.5 ms, roughly 20 percent less per event. As a side effect the
chat_message dual write now receives the sanitized message; previously null
bytes in non-content fields were cleaned in the blob but written raw to
chat_message, which failed that insert on PostgreSQL. Verified byte-identical
rows against the previous implementation across nine scenarios covering null
bytes in every input, legacy dirty rows, a missing title and a NULL chat
column.
