In discourse/discourse-translator#249 we introduced splitting content (post.raw) prior to sending to translation as we were using a sync api.
Now that we're streaming thanks to #1424, we'll chunk based on the LlmModel.max_output_tokens.
New implementation uses core concurrent job queue, it is more
robust and predictable than the one shipped in Concurrent.
Additionally:
- Trickles through updates during bulk classification
- Reports errors if we fail during a bulk classification
* push concurrency down to 40. 100 feels quite high.
## 🔍 Overview
This update fixes an issue where message bus streaming related specs
were not working correctly. To do so we pass the `last_id` when
subscribing to `MessageBus` which allows us to unskip those broken
tests.
---------
Co-authored-by: Joffrey JAFFEUX <[email protected]>
The current menu was rendering inside the post text toolbar (on desktop). This is not ideal as the post text toolbar rendering is conditioned on the presence of text selection, when you click a button on the toolbar, by design of the web browsers you will lose your text selection, making all of this super tricky.
This commit makes desktop and mobile behave in the same way by rendering their own menu and capturing the quote state when we render the post text selection toolbar, this allows us to reason a much simpler way about the AI helper.
This commit also removes what appears to be an unused file and corrects which was seemingly copy/paste mistakes.
⚠️ Technical note, this commit is correcting the message bus subscription which amongst other things allows to write specs which are not flaky. However due to the current implementation we have a channel per post, which means we need to serialize on last message bus id per post.
We have two possible solutions here:
- subscribe at the topic level
- refactor the code to be able to use `MessageBus.last_ids` to be able to grab multiple posts at once instead of having to call `MessageBus.last_id` and done one Redis call per post
---------
Co-authored-by: Keegan George <[email protected]>
* FIX: implement max_output tokens (anthropic/openai/bedrock/gemini/open router)
Previously this feature existed but was not implemented
Also updates a bunch of models to in our preset to point to latest
* implementing in base is safer, simpler and easier to manage
* anthropic 3.5 is getting older, lets use 4.0 here and fix spec
We're seeing an aggressive number of translations being enqueued for a single post and locale. Historically, we trigger translation on `cooked` not `raw`, but that has changed a while back.
```
# from AiApiAuditLog, the same post is getting translated to the same locale within a few secs of each other
zh_CN - 2025-06-17 13:02:31 UTC
zh_CN - 2025-06-17 13:02:34 UTC
zh_CN - 2025-06-17 13:02:35 UTC
zh_CN - 2025-06-17 13:02:36 UTC
zh_CN - 2025-06-17 13:02:38 UTC
zh_CN - 2025-06-17 13:02:39 UTC
zh_CN - 2025-06-17 13:02:40 UTC
zh_CN - 2025-06-17 13:02:40 UTC
zh_CN - 2025-06-17 13:02:43 UTC
zh_CN - 2025-06-17 13:02:44 UTC
```
This PR prevents this from happening.
Previously staff and bots would get scanned if TL was low
Additionally if somehow spam scanner user was blocked
(deactivated, silenced, banned) it would stop the feature from working
This adds an override that ensures unconditionally the user is setup correctly prior to scanning
is update adds logging for changes made in the AI admin panel. When making configuration changes to Embeddings, LLMs, Personas, Tools, or Spam that aren't site setting related, changes will now be logged in Admin > Logs & Screening. This will help admins debug issues related to AI. In this update a helper lib is created called `AiStaffActionLogger` which can be easily used in the future to add logging support for any other admin config we need logged for AI.
In hybrid mode ai artifacts can optionally automatically run.
This is useful for cases where you may want to embed a survey and so on.
Additionally, artifacts now allow for better fidelity around display:
<div class="ai-artifact" data-ai-artifact-id="501" data-ai-artifact-height="300px" data-ai-artifact-autorun data-ai-artifact-seamless></div>
User can supply height and seamless mode to be seamlessly rendered with no box shadow and show full screen button.
OpenAI ship a new API for completions called "Responses API"
Certain models (o3-pro) require this API.
Additionally certain features are only made available to the new API.
This allow enabling it per LLM.
see: https://platform.openai.com/docs/api-reference/responses
Introduces a persistent, user-scoped key-value storage system for
AI Artifacts, enabling them to be stateful and interactive. This
transforms artifacts from static content into mini-applications that can
save user input, preferences, and other data.
The core components of this feature are:
1. **Model and API**:
- A new `AiArtifactKeyValue` model and corresponding database table to
store data associated with a user and an artifact.
- A new `ArtifactKeyValuesController` provides a RESTful API for
CRUD operations (`index`, `set`, `destroy`) on the key-value data.
- Permissions are enforced: users can only modify their own data but
can view public data from other users.
2. **Secure JavaScript Bridge**:
- A `postMessage` communication bridge is established between the
sandboxed artifact `iframe` and the parent Discourse window.
- A JavaScript API is exposed to the artifact as `window.discourseArtifact`
with async methods: `get(key)`, `set(key, value, options)`,
`delete(key)`, and `index(filter)`.
- The parent window handles these requests, makes authenticated calls to the
new controller, and returns the results to the iframe. This ensures
security by keeping untrusted JS isolated.
3. **AI Tool Integration**:
- The `create_artifact` tool is updated with a `requires_storage`
boolean parameter.
- If an artifact requires storage, its metadata is flagged, and the
system prompt for the code-generating AI is augmented with detailed
documentation for the new storage API.
4. **Configuration**:
- Adds hidden site settings `ai_artifact_kv_value_max_length` and
`ai_artifact_max_keys_per_user_per_artifact` for throttling.
This also includes a minor fix to use `jsonb_set` when updating
artifact metadata, ensuring other metadata fields are preserved.
Additional changes:
Adds a "#features" method in AiPersona to find which features are using that persona.
Serializes a basic version of a LlmModel in the persona's "#default_llm" serializer attribute.
* FEATURE: Display features that rely on multiple personas.
This change makes the previously hidden feature page visible while displaying features, like the AI helper, which relies on multiple personas.
* Fix system specs
## 🔍 Overview
This update re-introduces the validator used on the `ai_spam_detection_enabled` setting. It was initially added here: https://github.com/discourse/discourse-ai/pull/1374 to prevent Spam from being enabled without creating an `AiModerationSetting` value in the database. However, due to issues with backups/migrations we temporarily removed it here: https://github.com/discourse/discourse-ai/pull/1393. Now with some internal fixes, we can re-introduce it. We also update the validator so that it only validates when trying to turn on rather than when turning off too.
The AiApiAuditLog per translation event doesn't trace back easily to a post or topic.
This commit adds support to that, and also switches the translators to named arguments rather than positional arguments.
Previously I had omitted to add `locale` to the category, as categories tended to be just a single word, and I did not find it would be worth to carry locale information.
Due to certain LLMs that do poorer at translation, category descriptions got pretty messy. We added locale support here - https://github.com/discourse/discourse/pull/32962.
This PR adds the automatic locale detection, and skips translating to the category's locale.
Adds context length controls to researcher (max tokens per post and batch)
Allow picking LLM for researcher
Fix bug where unicode usernames were not working
Fix documentation of OR logic
- add sleep function for tool polling with rate limits
- Support base64 encoding for HTTP requests and uploads
- Enhance forum researcher with cost warnings and comprehensive planning
- Add cancellation support for research operations
- Include feature_name parameter for bot analytics
- richer research support (OR queries)
Natalie Tay
Sam
Keegan GeorgeandJoffrey JAFFEUX
Rafael dos Santos Silva
Roman Rizzi
Roman Rizzi