## 🔍 Overview
We want to unhide `ai_spam_detection_enabled` setting so that we can retain staff action log features. However, we also want to ensure users cannot enable spam detection without having `AiModerationSetting.spam` present in the database.
In this update we unhide the setting, but also add a validator to ensure the necessary configuration is in place before allowing the setting to be enabled.
* Small fix, reasoning is now available on Claude 4 models
* fix invalid filters should raise, topic filter not working
* fix spec so we are consistent
* FIX: Improve MessageBus efficiency and correctly stop streaming
This commit enhances the message bus implementation for AI helper streaming by:
- Adding client_id targeting for message bus publications to ensure only the requesting client receives streaming updates
- Limiting MessageBus backlog size (2) and age (60 seconds) to prevent Redis bloat
- Replacing clearTimeout with Ember's cancel method for proper runloop management, we were leaking a stop
- Adding tests for client-specific message delivery
These changes improve memory usage and make streaming more reliable by ensuring messages are properly directed to the requesting client.
* composer suggestion needed a fix as well.
* backlog size of 2 is risky here cause same channel name is reused between clients
The structured output JSON comes embedded inside the API response, which is also a JSON. Since we have to parse the response to process it, any control characters inside the structured output are unescaped into regular characters, leading to invalid JSON and breaking during parsing. This change adds a retry mechanism that escapes
the string again if parsing fails, preventing the parser from breaking on malformed input and working around this issue.
For example:
```
original = '{ "a": "{\\"key\\":\\"value with \\n newline\\"}" }'
JSON.parse(original) => { "a" => "{\"key\":\"value with \n newline\"}" }
# At this point, the inner JSON string contains an actual newline.
```
This update adds a safety checker which scans the streamed updates. It ensures that incomplete segments of text are not sent yet over message bus as this will cause breakage with the diff streamer. It also updates the diff streamer to handle a thinking state for when we are waiting for message bus updates.
This change fixes two bugs and adds a safeguard.
The first issue is that the schema Gemini expected differed from the one sent, resulting in 400 errors when performing completions.
The second issue was that creating a new persona won't define a method
for `response_format`. This has to be explicitly defined when we wrap it inside the Persona class. Also, There was a mismatch between the default value and what we stored in the DB. Some parts of the code expected symbols as keys and others as strings.
Finally, we add a safeguard when, even if asked to, the model refuses to reply with a valid JSON. In this case, we are making a best-effort to recover and stream the raw response.
* FEATURE: allow researcher to also research specific topics
Also improve UI around research with more accurate info
* this ensures that under no conditions PMs will be included
* DEV: use a proper object for tool definition
This moves away from using a loose hash to define tools, which
is error prone.
Instead given a proper object we will also be able to coerce the
return values to match tool definition correctly
* fix xml tools
* fix anthropic tools
* fix specs... a few more to go
* specs are passing
* FIX: coerce values for XML tool calls
* Update spec/lib/completions/tool_definition_spec.rb
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit introduces a new Forum Researcher persona specialized in deep forum content analysis along with comprehensive improvements to our AI infrastructure.
Key additions:
New Forum Researcher persona with advanced filtering and analysis capabilities
Robust filtering system supporting tags, categories, dates, users, and keywords
LLM formatter to efficiently process and chunk research results
Infrastructure improvements:
Implemented CancelManager class to centrally manage AI completion cancellations
Replaced callback-based cancellation with a more robust pattern
Added systematic cancellation monitoring with callbacks
Other improvements:
Added configurable default_enabled flag to control which personas are enabled by default
Updated translation strings for the new researcher functionality
Added comprehensive specs for the new components
Renames Researcher -> Web Researcher
This change makes our AI platform more stable while adding powerful research capabilities that can analyze forum trends and surface relevant content.
Examples simulate previous interactions with an LLM and come
right after the system prompt. This helps grounding the model and
producing better responses.
We were logging persona triage as "bot" in logs, causing some
confusions around real world usage
This amends it so we log usage to "automation - AUTOMATION NAME"
* DEV: Use structured responses for summaries
* Fix system specs
* Make response_format a first class citizen and update endpoints to support it
* Response format can be specified in the persona
* lint
* switch to jsonb and make column nullable
* Reify structured output chunks. Move JSON parsing to the depths of Completion
* Switch to JsonStreamingTracker for partial JSON parsing
This ensures webp / gif are converted to png prior to sending
to LLMs given webp and gif are not evenly supported.
png/jpg is universally supported and are the only supported format.
longer term we need to add support for audio/video/pdf which is supported by some models.
* more specs
System personas leaned on reused classes, this was a problem
in a multisite environement cause state, such as "enabled"
ended up being reused between sites.
New implementation ensures state is pristine between sites in
a multisite
* more handling for new superclass story
* small oversight, display name should be used for display
AI bots come in 2 flavors
1. An LLM and LLM user, in this case we should decorate posts with persona name
2. A Persona user, in this case, in PMs we decorate with LLM name
(2) is a significant improvement, cause previously when creating a conversation
you could not tell which LLM you were talking to by simply looking at the post, you would
have to scroll to the top of the page.
* lint
* translation missing
This commit enhances the AI image generation functionality by adding support for:
1. OpenAI's GPT-based image generation model (gpt-image-1)
2. Image editing capabilities through the OpenAI API
3. A new "Designer" persona specialized in image generation and editing
4. Two new AI tools: CreateImage and EditImage
Technical changes include:
- Renaming `ai_openai_dall_e_3_url` to `ai_openai_image_generation_url` with a migration
- Adding `ai_openai_image_edit_url` setting for the image edit API endpoint
- Refactoring image generation code to handle both DALL-E and the newer GPT models
- Supporting multipart/form-data for image editing requests
* wild guess but maybe quantization is breaking the test sometimes
this increases distance
* Update lib/personas/designer.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* simplify and de-flake code
* fix, in chat we need enough context so we know exactly what uploads a user uploaded.
* Update lib/personas/tools/edit_image.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* cleanup downloaded files right away
* fix implementation
---------
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* FEATURE: display more places where AI is used
- Usage was not showing automation or image caption in llm list.
- Also: FIX - reasoning models would time out incorrectly after 60 seconds (raised to 10 minutes)
* correct enum not to enumerate non configured models
* FEATURE: implement chat streamer
This implements a basic chat streamer, it provides 2 things:
1. Gives feedback to the user when LLM is generating
2. Streams stuff much more efficiently to client (given it may take 100ms or so per call to update chat)
This update adds metrics for estimated spending in AI usage. To make use of it, admins must add cost details to the LLM config page (input, output, and cached input costs per 1M tokens). After doing so, the metrics will appear in the AI usage dashboard as the AI plugin is used.
* FEATURE: Update model names and specs
- not a bug, but made it explicit that tools and thinking are not a chat thing
- updated all models to latest in presets (Gemini and OpenAI)
* allow larger context windows
1. Add age of post to topic context (1 month ago, 1 year ago, etc)
2. Refactor code for simplicity
3. Fix handling of post context in DMs which was not using new handling of uploads
Previous to this fix we assumed the name field contained usernames
when in fact it was stored in the id field.
This fixes the context contruction and also adds some basic user
information to the context to assist responders in understanding
the cast of chars
In this feature update, we add the UI for the ability to easily configure persona backed AI-features. The feature will still be hidden until structured responses are complete.
Add API methods to AI tools for reading and updating personas, enabling
more flexible AI workflows. This allows custom tools to:
- Fetch persona information through discourse.getPersona()
- Update personas with modified settings via discourse.updatePersona()
- Also update using persona.update()
These APIs enable new use cases like "trainable" moderation bots, where
users with appropriate permissions can set and refine moderation rules
through direct chat interactions, without needing admin panel access.
Also adds a special API scope which allows people to lean on API
for similar actions
Additionally adds a rather powerful hidden feature can allow custom tools
to inject content into the context unconditionally it can be used for memory and similar features
Previously, allowing "everyone" to access gists meant anons would see them too.
With the move to Personas, we used "[]" to reflect that.
With discourse/discourse#32199 adding the "everyone" option to the personas-allowed
groups, we are switching back to the original behavior.
Leaving allowed groups empty should always mean nobody can use the feature.
