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.
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.
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.
Listing a user's folders re-checks which entries they may still see, and it resolved their group membership again for every folder, then again inside the collection and note branches for every entry. A comment in that helper claims one membership fetch for the whole listing, but the caller invokes it once per folder, so the claim never held.
The listing now resolves membership once, and only when some folder actually carries entries, then threads it through the file, collection and note checks. Callers that do not supply it are unchanged and still resolve for themselves.
Measured with twenty folders holding six files, two knowledge bases and two notes each: 245 queries and ~145 ms before, 186 and ~117 ms after. The folders returned, and the entries the integrity pass writes back, are unchanged. That was checked against entries the caller owns, entries shared through a group, entries shared with nobody, another user's files, and an unrecognised entry type.
The tool callable now takes its connection's cookie jar as a parameter, matching how its headers are already passed and how the terminal tool factory in the same module builds its callables.
A calendar event's `meta` is a free-form dict, so `meta.alert_minutes` can hold any JSON type, while the upcoming-events lookup assumed it was a number and compared it directly. It now ignores a value that is not numeric and falls back to the default alert window for that event.
Handled on the read side rather than on the write path so events already stored with a non-numeric value are covered too. Numeric values are untouched, including the negative "no alert" sentinel.
A chat attached via the "+" menu or dropped from the sidebar references an
existing chat by id and carries no url. add_file_context() filtered on
`file.get('url')`, so the reference was dropped from <attached_files>
entirely and the model was never told it existed.
When the RAG file-context path is enabled the chat content still reaches
the model as <source> context, which masked this. With file_context
disabled that path is skipped, and get_attached_knowledge() only promotes
collection/note items into <attached_knowledge> - so an attached chat was
visible in the UI but invisible to the model, which then reported having
no chat attachments despite having a view_chat tool available.
Keep chat references and emit their id so the model can resolve them with
view_chat. The url attribute is now conditional, since a chat has none;
the id guard it replaces was dead once the filter guarantees a url or a
chat id.
Co-authored-by: Claude <noreply@anthropic.com>
The tool-call continuation re-submits with bypass_system_prompt=True, but only
routers/openai.py and routers/ollama.py checked it, so pipe and manifold models
had the system prompt applied again on every continuation. Since
add_or_update_system_message() prepends rather than replaces, N tool-call rounds
left N+1 copies of the system prompt in the payload.
Checking whether a user may reach a file loaded and validated every workspace model that user can access, then scanned each model's knowledge list in Python for one file id. Folder listings run that check once per file, so opening a folder of twenty files rebuilt the whole accessible-model set twenty times, and the same check sits on every retrieval and download path.
The lookup now runs the other way round: the database returns the models that attach the file, and only those are access-checked. The text match on the metadata column is a prefilter and the knowledge entries still decide, so a file id that merely appears in a description grants nothing; file ids are server-generated uuids, so the match can only be too wide, never too narrow.
Measured with 500 accessible workspace models: a single check drops from 9 queries and ~20 ms to 6 and ~2.6 ms, and a twenty-file folder listing from 180 queries and ~680 ms to 120 and ~56 ms. A 72-case matrix over owner, public, direct-user and group grants, for both read and write, returns exactly what it returned before, and write still requires the model owner to own the file. The check also no longer writes to the database while answering a read-only question.
Listing skills ran one database query per skill in the instance. A non-admin opening the list on a workspace with 500 skills issued over 500 queries, the paginated list re-resolved the caller's group membership once per row, and every chat message carrying a skill loaded every skill the user can read, full body and owner included, to use the two or three it actually referenced.
Skills now arrive already filtered: the owner-or-grant check runs in the query as an EXISTS subquery, the same way prompts and the search endpoints already do it, the per-item write flag uses the existing batch grant lookup, and the chat path asks only for the skill ids the request names.
Measured with 500 skills of which 3 are visible to the caller: 504 queries and ~300 ms before, 4 queries and ~2.6 ms after. The resulting set is unchanged for owner, public, direct-user, group and multi-grant entries, for both read and write.
Timothy Jaeryang Baek
Solaris-starandGitHub
Classic298andGitHub
G30andGitHub
Aleix DorcaandGitHub
