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534b0df391
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.
26 lines
923 B
Ruby
26 lines
923 B
Ruby
# frozen_string_literal: true
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module ::Jobs
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class GenerateRagEmbeddings < ::Jobs::Base
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sidekiq_options queue: "ultra_low"
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# we could also restrict concurrency but this takes so long if it is not concurrent
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def execute(args)
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return if (fragments = RagDocumentFragment.where(id: args[:fragment_ids].to_a)).empty?
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vector = DiscourseAi::Embeddings::Vector.instance
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# generate_representation_from checks compares the digest value to make sure
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# the embedding is only generated once per fragment unless something changes.
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fragments.map { |fragment| vector.generate_representation_from(fragment) }
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last_fragment = fragments.last
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target = last_fragment.target
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upload = last_fragment.upload
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indexing_status = RagDocumentFragment.indexing_status(target, [upload])[upload.id]
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RagDocumentFragment.publish_status(upload, indexing_status)
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end
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end
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end
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