* 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
To quickly select backfill candidates without comparing SHAs, we compare the last summarized post to the topic's highest_post_number. However, hiding or deleting a post and adding a small action will update this column, causing the job to stall and re-generate the same summary repeatedly until someone posts a regular reply. On top of this, this is not always true for topics with `best_replies`, as this last reply isn't necessarily included.
Since this is not evident at first glance and each summarization strategy picks its targets differently, I'm opting to simplify the backfill logic and how we track potential candidates.
The first step is dropping `content_range`, which serves no purpose and it's there because summary caching was supposed to work differently at the beginning. So instead, I'm replacing it with a column called `highest_target_number`, which tracks `highest_post_number` for topics and could track other things like channel's `message_count` in the future.
Now that we have this column when selecting every potential backfill candidate, we'll check if the summary is truly outdated by comparing the SHAs, and if it's not, we just update the column and move on
When navigating between topic we were not correctly resetting
internal state for summarization. This leads to a situation where
incorrect summaries can be displayed to users and wrong summaries
can be displayed.
Additionally our controller for grabbing summaries was always
streaming results via message bus, which could be delayed when
sidekiq is overloaded. We now will return the cached summary
right away if it is available direct from REST endpoint.
This allows summary to use the new LLM models and migrates of API key based model selection
Claude 3.5 etc... all work now.
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Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
* DEV: Remove the summarization feature
Instead, we'll register summarization implementations for OpenAI, Anthropic, and Discourse AI using the API defined in discourse/discourse#21813.
Core and chat will implement features on top of these implementations instead of this plugin extending them.
* Register instances that contain the model, requiring less site settings
* FEATURE: Topic summarization
Summarize topics using the TopicView's "summary" filter. The UI is similar to what we do for chat, but we don't allow the user to select a timeframe.
Co-authored-by: Rafael dos Santos Silva <xfalcox@gmail.com>
Roman Rizzi
Sam
Keegan George
Rafael dos Santos Silva