Custom metadata attached to a file upload now reaches the vector DB, but the model still never sees it. Both prompt-assembly paths build their output from a fixed field set: the classic RAG <source> tag carries only id, name and resource type, and the retrieval tools return only content, source and file id per chunk. A scraper that records where each document came from therefore cannot get that origin in front of the model, so answers cannot state it.
RAG_SOURCE_METADATA_KEYS names the chunk metadata keys allowed through to the model. Configured keys are emitted as extra attributes on the <source> tag and as extra fields on tool result chunks, covering both retrieval paths. It is empty by default, so nothing changes for existing deployments.
An allowlist instead of passing everything through, because chunk metadata also carries file hashes, collection names, embedding config and relevance scores, which would then be added to every retrieved chunk of every request. Values are attacker-controllable through an uploaded file, so they are escaped before they go into the tag, and a configured key can never displace a field the tag or the chunk already defines.
Reported in open-webui/open-webui#29486.
The ask_user card badges the first option of every question as "Recommended", but nothing ever told the model that. The model picks whatever order it likes, so the badge really means "listed first" and users act on a recommendation the model never made.
The ask_user tool description now states that the first option is labelled Recommended and that the model should list the option it recommends first, so the badge reflects an actual choice.
Kept the badge and instructed the model instead of adding a per-option "recommended" flag: the flag would need a schema change, validation in the request normalizer and a frontend change, for the same result in the common case. Dropping the badge was the other option, but it removes a useful affordance rather than fixing it.
Verified that the added line reaches the model by running the docstring through the tool-spec builder and checking the generated OpenAI function schema.
Fixes#30195
Asked to add a section or change a few lines, models answer with a whole-note
replace_note_content call and the rest of the note is gone, with nothing to
undo because the editor only records versions on chat inserts. The tool
already supports replace_range operations with an expected guard, but its
description never said when to use them or how the offsets work, so models
defaulted to sending the whole note back.
The docstring, which is the description every model receives for this tool
in note chats and normal chats alike, now states the preference for range
edits and the offset, overlap and expected rules the handler enforces.
Verified the text lands in the generated tool spec unchanged and that range
edits, the expected mismatch rejection and whole-note replace behave as
described against a sqlite data dir.
The built-in chat and knowledge grep tools awaited their matching helper directly, so the search ran on the event loop and held it for as long as the match took. Both call sites now hand the helper to a worker thread.
Output and error handling are unchanged: 17 cases (literal, regex, case-insensitive, count-only, no-match, invalid pattern, rejected quantifiers, missing file data, result truncation) compare byte-for-byte against the previous behaviour. The matcher's time budget lives in a contextvar, which asyncio.to_thread copies into the worker, so budget scoping still behaves as before.
* fix: preserve user info in agentic RAG tools
* fix: preserve user info in file access checks
---------
Co-authored-by: Damien SPINELLI <damien.spinelli@external.list.lu>
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The `create_automation` and `update_automation` builtin tools wrote straight to `Automations.insert` / `Automations.update_by_id`, skipping the limit checks that `/api/v1/automations/create` and `/api/v1/automations/{id}/update` run through `check_automation_limits`. A non-admin user could therefore ask the model to create automations indefinitely, ignoring `AUTOMATION_MAX_COUNT`, and could schedule them below `AUTOMATION_MIN_INTERVAL`, on both create and update.
Both tools now call the same `check_automation_limits` helper the routers use, so the limits and the admin bypass cannot drift between the chat path and the HTTP path. A rejection is returned to the model as a plain error message instead of raising. `update_automation` also gained the missing user lookup guard, since the helper needs the user's role.
The `automations.enable` toggle and the `features.automations` user permission were already enforced when the tool set is assembled, so they are unaffected.
Fixes#27121
