1. on failure we were queuing a job to generate embeddings, it had the wrong params. This is both fixed and covered in a test.
2. backfill embedding in the order of bumped_at, so newest content is embedded first, cover with a test
3. add a safeguard for hidden site setting that only allows batches of 50k in an embedding job run
Previously old embeddings were updated in a random order, this changes it so we update in a consistent order
* FEATURE: HyDE-powered semantic search.
It relies on the new outlet added on discourse/discourse#23390 to display semantic search results in an unobtrusive way.
We'll use a HyDE-backed approach for semantic search, which consists on generating an hypothetical document from a given keywords, which gets transformed into a vector and used in a asymmetric similarity topic search.
This PR also reorganizes the internals to have less moving parts, maintaining one hierarchy of DAOish classes for vector-related operations like transformations and querying.
Completions and vectors created by HyDE will remain cached on Redis for now, but we could later use Postgres instead.
* Missing translation and rate limiting
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Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
TopicQuery already provides a lot of safeguards and options for filtering topic, and enforcing permissions. It makes sense to rely on it as other plugins like discourse-assign do.
As a bonus, we now have access to the current_user while serializing these topics, so users will see things like unread posts count just like we do for the lists.
* FEATURE: Embeddings to main db
This commit moves our embeddings store from an external configurable PostgreSQL
instance back into the main database. This is done to simplify the setup.
There is a migration that will try to import the external embeddings into
the main DB if it is configured and there are rows.
It removes support from embeddings models that aren't all_mpnet_base_v2 or OpenAI
text_embedding_ada_002. However it will now be easier to add new models.
It also now takes into account:
- topic title
- topic category
- topic tags
- replies (as much as the model allows)
We introduce an interface so we can eventually support multiple strategies
for handling long topics.
This PR severely damages the semantic search performance, but this is a
temporary until we can get adapt HyDE to make semantic search use the same
embeddings we have for semantic related with good performance.
Here we also have some ground work to add post level embeddings, but this
will be added in a future PR.
Please note that this PR will also block Discourse from booting / updating if
this plugin is installed and the pgvector extension isn't available on the
PostgreSQL instance Discourse uses.
Depends on discourse/discourse#20915
Hooks to the full-page-search component using an experimental API and performs an assymetric similarity search using our embeddings database.
Also:
- Normalizes behavior between logged in and anon,
we only show related topics in the related topic section
- Renames "suggested" to "related" given this only exists in related section
- Adds a spec section to ensure anon does not regress
- Adds `ai_embeddings_semantic_related_topics` to limit related topics
Renamed settings:
ai_embeddings_semantic_suggested_model -> ai_embeddings_semantic_related_model
ai_embeddings_semantic_suggested_topics_enabled -> ai_embeddings_semantic_related_topics_enabled
Plugins is still in an experimental phase and not much is overidden hence
avoiding adding site setting migrations.
Co-authored-by: Krzysztof Kotlarek <kotlarek.krzysztof@gmail.com>
Roman Rizzi
Sam
Jarek Radosz
Rafael dos Santos Silva