49 lines
1.5 KiB
Ruby
49 lines
1.5 KiB
Ruby
# frozen_string_literal: true
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RSpec.describe Jobs::GenerateEmbeddings do
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subject(:job) { described_class.new }
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describe "#execute" do
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before do
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SiteSetting.ai_embeddings_discourse_service_api_endpoint = "http://test.com"
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SiteSetting.ai_embeddings_enabled = true
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end
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fab!(:topic)
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fab!(:post) { Fabricate(:post, post_number: 1, topic: topic) }
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let(:truncation) { DiscourseAi::Embeddings::Strategies::Truncation.new }
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let(:vector_rep) do
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(truncation)
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end
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it "works for topics" do
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expected_embedding = [0.0038493] * vector_rep.dimensions
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text =
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truncation.prepare_text_from(
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topic,
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vector_rep.tokenizer,
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vector_rep.max_sequence_length - 2,
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)
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EmbeddingsGenerationStubs.discourse_service(vector_rep.class.name, text, expected_embedding)
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job.execute(target_id: topic.id, target_type: "Topic")
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expect(vector_rep.topic_id_from_representation(expected_embedding)).to eq(topic.id)
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end
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it "works for posts" do
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expected_embedding = [0.0038493] * vector_rep.dimensions
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text =
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truncation.prepare_text_from(post, vector_rep.tokenizer, vector_rep.max_sequence_length - 2)
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EmbeddingsGenerationStubs.discourse_service(vector_rep.class.name, text, expected_embedding)
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job.execute(target_id: post.id, target_type: "Post")
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expect(vector_rep.post_id_from_representation(expected_embedding)).to eq(post.id)
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end
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end
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end
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