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.
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Uploading an .svg to a chat attached it as a vision image input, so the model received a data URI it could not decode. PIL-backed servers answered "cannot identify image file" and OpenAI answered "The image data you provided does not represent a valid image". No setting made it work.
SVG now takes the ordinary file upload path, so its XML source is extracted and indexed and the model can answer questions about it. Rasterizing was the alternative and it would have discarded the part of an SVG a model reads best, the source itself. Raster formats are untouched and still go up as image inputs.
A shared helper replaces the ad hoc image/ prefix checks at the points that decide image input versus document, on both ends. It normalises the content type first, because a stored "image/SVG+xml" or a trailing charset parameter slipped past a plain comparison.
One behaviour change worth knowing: an SVG now needs the model to have the file upload capability, where before it rode in as an image.
Fixes#30100
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`has_access_to_file` now derives knowledge-base access purely from the knowledge-file association, instead of also consulting the `collection_name` value stored on the file record.
Sending an image or file with no typed text to a model with skills
bound replaced the (empty) message text with the list of skill names,
so the model answered with its skill catalog instead of handling the
attachment. The empty-message guard added for #24929 fired on any
empty last user text without checking for attachments.
The guard now skips the fallback when the current message carries
files, read from the user_message object the frontend sends with the
request. Attachment-only messages then go out exactly as they do on
models without skills. A bare skill selection with no attachment still
gets the fallback, so the provider 400 from #24929 stays fixed.
Request-level files were not usable as the signal: that list carries
the model's knowledge and folder files, so a bare skill selection on a
knowledge model would have gone out empty again.
Fixes#30040
The comments on the opt-in in-place append made it sound like the fast path
can lose streamed text under normal operation. The only way the field can be
emptied is an allocation failure, which means the host is already out of
memory, and the default path (which copies the whole accumulated string on
every chunk) raises in that situation as well. Reword both comments to state
that condition so the flag is not read as unsafe.
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A slow Redis freezes the whole worker during sign-in, not just the user signing in. RateLimiter held a synchronous redis-py client and signin called is_limited inline from a coroutine, so every attempt did blocking round trips on the event-loop thread, with REDIS_SOCKET_TIMEOUT defaulting to None so nothing bounded the wait. Its Redis methods are now async and take the handle as their first argument, and both handlers pass request.app.state.redis, the async client the lifespan already creates. Building one in the limiter instead would pin its pooled connection to the first event loop that used it.
Without Redis, which is the default single-instance setup, the fallback store leaked. It was keyed by the rate-limit key and pruned a key's expired buckets only when that same key was checked again, so a login email never seen again was never reclaimed, and that email comes straight from an unauthenticated request body. It is now keyed by bucket, so one prune drops every key an expired bucket held, and it lives on the instance: pruning uses the per-instance num_buckets, so a shared store would let a limiter with a short window delete buckets a longer-windowed one still needs.
With a Redis costing a second per call, the widest event-loop tick gap drops from 2.010s to 0.010s and a concurrent request is answered at 0.05s instead of 2.05s, at no cost to the caller's own latency. Across 20,000 distinct keys the store goes from 40,000 entries and 6.4 MB, growing linearly, to a flat 1,004 entries and 100 KB. Rate-limiting decisions are unchanged across 700,000 randomised calls over 14 window, bucket and limit combinations, against a real Redis and the in-memory fallback alike, and sign-in still returns its first 429 on attempt 16.
Two behaviour changes worth naming. Pruning is now global rather than per key, so a wall clock that jumps forward past a full window and back forgets a hit it previously kept. The two limiters also stop sharing a store, which previously let a sign-in attempt with an IP-shaped email touch the token-exchange limiter's counters.
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Pressing stop saved the assistant message as finished while its output items were still marked as running, so the affected block kept showing "Thinking...", "Analyzing..." or "Executing...". That state was written to the database, so it came back after every reload. A tool call cancelled mid-execution was affected as well: it is marked completed as soon as its arguments finish streaming, well before the tool returns, and the client reads that as still executing until the result item arrives.
Cancelling now closes those items before the message is saved, marking them incomplete, which is the status the client already treats as finished without flagging an error. Tool calls waiting for approval are left untouched so their approval prompt survives a reload. The corrected output is sent on the chat:tasks:cancel event that was already being emitted, so an open tab settles immediately instead of only after a reload.
Emitting a chat:completion event instead would have fired response auto-copy, text to speech playback and the chat finished listeners on a response the user had just cancelled.
Verified on a running instance against a mock upstream: cancelling mid-response leaves no item marked as running in the saved message, and the identical run on unpatched code leaves one.
Fixes#29281
When a tool returns a base64 image, Open WebUI only moves it out of the model's context if the entire result is that image, or if the tool is MCP. An image sitting inside a returned object or a list was serialised into the tool message instead, so a single screenshot could cost hundreds of thousands of tokens, push out the rest of the conversation and leave the model answering from garbage.
Any string in a tool result that is exactly one image data URI is now moved into the result's files wherever it sits in the structure, and replaced with a short marker. The model gets the image as an attachment rather than as text, and it renders for the user instead of being dropped.
Detection is deliberately limited to values that are entirely a data URI. Scanning inside longer strings was tried and abandoned: a payload wrapped over several lines, or one followed by text, gets cut short, and shipping a truncated image is worse than the bloat because the provider rejects the whole request.
This also fixes the OpenAPI branch removing entries from the list it was iterating over, which skipped every second data URI and left it in the model's context.
Fixes#29208
* fix: drop thinking blocks when converting Anthropic Messages requests to Chat Completions
Claude Code and other Anthropic SDK clients pointed at /api/v1/messages send the assistant's earlier thinking blocks back with every follow-up request. Since 0.11.0 those blocks were copied into the OpenAI assistant message as content parts of type thinking, a part type Chat Completions does not define. Strict OpenAI-compatible servers such as NVIDIA Dynamo reject the whole request with 400 "data did not match any variant of untagged enum ChatCompletionRequestAssistantMessageContent", so a conversation with a reasoning model died on its second turn. The error reports its position at the very end of the body, which made the request look cut off; it was complete.
Thinking and redacted_thinking blocks are now skipped in the conversion, which is what happened before 0.11.0. An assistant turn that held only thinking blocks is kept as an empty assistant message so the turn order survives. Native Anthropic and LiteLLM connections are unaffected because they receive the request untouched, and thinking blocks in responses are still produced.
Fixes#29799
* fix: keep signed thinking blocks when converting Anthropic Messages requests
Dropping every thinking block also removed the signed ones. Those are the blocks a gateway such as LiteLLM forwards to Anthropic, which needs the signed thinking block of the previous assistant turn when a tool-use turn continues with extended thinking. Only the unsigned blocks are the problem: Open WebUI creates them itself from reasoning_content, and strict Chat Completions backends reject them because thinking is not a content part type they know.
Unsigned thinking blocks are now dropped while signed thinking and redacted_thinking blocks are kept, the same rule LiteLLM applies before forwarding to Anthropic and the rule the native chat path already uses for Anthropic reasoning details. Backends that never emit a signature, such as NVIDIA Dynamo and vLLM, keep receiving requests without thinking parts, so the original failure stays fixed.
Tool calls to an OpenAPI tool server put every argument the model returned into the JSON request body, including the parameters that were already substituted into the URL. Servers that validate their input strictly (additionalProperties: false) answered 422 "unexpected property", so reads worked and every write through an endpoint with a path or query parameter failed.
The body is now built from the model's arguments minus the operation's declared parameters, keeping any name the requestBody schema declares as a property of its own, so an endpoint that wants the resource id in the body as well as in the path still gets it.
The filter only runs when the resolved body schema lists its properties. A free-form, composed or non-JSON body offers nothing to check a name against, so those requests go out exactly as before.
src/lib/apis/index.ts carries the same request builder for direct tool server connections and had the same bug, so it gets the same fix.
Fixes#29716
Some endpoints stream a tool call whose function name is JSON null instead of a string. Nothing normalized it, so the null stayed on the tool call, was written into the stored message, and was sent back to the endpoint in the assistant message on the next turn, where a null is not a valid function name.
The delta accumulator now replaces a null name with an empty string, at the same point it already normalizes the arguments field. The call still fails as an unknown tool, which is the right outcome for a call that has no name, so the result is one failed tool call instead of a follow-up request the endpoint has to reject.
This is done where the delta enters the accumulator rather than at the consumers, because the name is emitted to the client and persisted while the response is still streaming, before anything downstream could clean it up.
Checked against 1261 streaming delta sequences: behaviour is unchanged except where the delta that creates the tool call carries a null name.
Seen in #29686 with a custom sglang build.
Two OAuth failure paths interpolated the raw token object into their log message. On the callback path that object is a live credential set, so a provider returning no user data wrote an access token, and usually a refresh token, straight into the application log.
Both messages now log without the payload. The token-exchange failure keeps its error level and its client_id binding and reports the provider's error description instead of the raw response body, which by that branch's own condition never contained an access token anyway. The callback failure keeps its warning level and identifies the provider, matching the other failure logs in that handler.
Sending a message to an arena model failed with "'JSONResponse' object has no attribute 'body_iterator'" whenever the backing provider answered with an HTTP error, so the real error never reached the user. Non-streaming requests on an arena model, such as title and tag generation, broke the same way with "'JSONResponse' object is not a mapping".
The arena wrapper assumed the sub-model call always returns a stream for a streaming request and a dict for everything else, but the OpenAI-compatible router returns a plain response object as soon as the provider answers 4xx or 5xx. Both arms now hand that response straight back, which is exactly what the non-arena path already does, so the existing error handling turns it into the usual error message in the chat.
Verified against a matrix of streaming and non-streaming requests with the sub-model returning a stream, a dict, a JSONResponse and a PlainTextResponse: both crashes are gone and the two success paths are unchanged, including the selected_model_id prelude on the stream.
Fixes#29658
A web search is not something that can be cited. The search engine
returns a title, a link and a one line snippet for each hit, and the
model never opens any of those pages. Emitting them as citation sources
produced one <source> tag per result, all named search_web with empty
bodies, and the citation template then instructed the model to cite them
by id. Models either hesitated visibly or attached an id to content from
a different result, which the citations panel then resolved to a title
that looked plausible, so the misattribution read as correct.
Web search results now stay in the tool output the model reads, and stop
being offered as things to cite. Where the model needs to cite a page it
calls fetch_url, whose citation names the URL and already works.
Web search results no longer appear in the citations panel. That is the
point of the change: the panel was offering pages that nothing had read.
Scoped to the native tool-calling path. The legacy handler cites every
tool result as one opaque source and does not single out web search, so
it is left alone rather than special-cased.
