Non-admin GET /api/v1/prompts/tags went through get_prompts_by_user_id,
which loaded every active prompt with its full content/data/meta plus
owner records and all access grants, then ran one has_access query per
prompt that wasn't owned by the caller - all so the endpoint could
collapse the result to a sorted tag list. With 600 prompts this took
several seconds while the admin path (a single SELECT) returned in <1s.
Add Prompts.get_tags_by_user_id which selects only the tags column and
applies the same EXISTS-based access filter used by /list. Also tighten
the admin get_tags to project just the tags column instead of full rows.
The endpoint is now one DB query (plus one for groups), no row hydration,
no N+1.
Co-authored-by: Claude <noreply@anthropic.com>
get_prompts_by_user_id used to fetch every active prompt (with users +
all access grants), then call AccessGrants.has_access() once per prompt
that the user did not own. With 600+ prompts this issued ~600 extra
round-trips per request and explained the multi-second delay reported in
the GET /api/v1/prompts and /api/v1/prompts/tags endpoints for non-admin
users.
Push the access check into a single SQL query via the existing
AccessGrants.has_permission_filter (EXISTS subquery), so only accessible
rows come back from the DB. Users and access grants for the surviving
rows are still batch-fetched, no N+1 anywhere on this path.
Co-authored-by: Claude <noreply@anthropic.com>
Convert the /chats/all endpoint from loading all user chats into memory
at once to a streaming NDJSON response that fetches chats in batches of
100. This prevents Out-of-Memory crashes for users with large chat
histories.
Backend: Added async generator that paginates through chats with
short-lived DB sessions per batch (critical for SQLite lock release).
Frontend: Updated getAllChats to consume the NDJSON stream via
ReadableStream reader, accumulating results for the export file.
Ref: open-webui#22206
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
