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* 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 --------- Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
32 lines
846 B
Ruby
32 lines
846 B
Ruby
# frozen_string_literal: true
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require "rails_helper"
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describe DiscourseAi::Embeddings::EntryPoint do
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fab!(:user) { Fabricate(:user) }
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describe "registering event callbacks" do
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context "when creating a topic" do
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let(:creator) do
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PostCreator.new(
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user,
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raw: "this is the new content for my topic",
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title: "this is my new topic title",
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)
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end
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it "queues a job on create if embeddings is enabled" do
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SiteSetting.ai_embeddings_enabled = true
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expect { creator.create }.to change(Jobs::GenerateEmbeddings.jobs, :size).by(1)
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end
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it "does nothing if sentiment analysis is disabled" do
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SiteSetting.ai_embeddings_enabled = false
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expect { creator.create }.not_to change(Jobs::GenerateEmbeddings.jobs, :size)
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end
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end
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end
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end
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