In a previous refactor, we moved the responsibility of querying and storing embeddings into the `Schema` class. Now, it's time for embedding generation.
The motivation behind these changes is to isolate vector characteristics in simple objects to later replace them with a DB-backed version, similar to what we did with LLM configs.
* 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>
Roman Rizzi
Loïc Guitaut
Rafael dos Santos Silva