56 lines
1.7 KiB
Ruby
56 lines
1.7 KiB
Ruby
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# frozen_string_literal: true
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RSpec.describe Jobs::EmbeddingsBackfill do
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fab!(:second_topic) do
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topic = Fabricate(:topic, created_at: 1.year.ago, bumped_at: 2.day.ago)
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Fabricate(:post, topic: topic)
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topic
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end
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fab!(:first_topic) do
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topic = Fabricate(:topic, created_at: 1.year.ago, bumped_at: 1.day.ago)
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Fabricate(:post, topic: topic)
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topic
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end
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fab!(:third_topic) do
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topic = Fabricate(:topic, created_at: 1.year.ago, bumped_at: 3.day.ago)
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Fabricate(:post, topic: topic)
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topic
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end
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let(:vector_rep) do
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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end
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it "backfills topics based on bumped_at date" do
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SiteSetting.ai_embeddings_enabled = true
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SiteSetting.ai_embeddings_discourse_service_api_endpoint = "http://test.com"
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SiteSetting.ai_embeddings_backfill_batch_size = 1
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Jobs.run_immediately!
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embedding = Array.new(1024) { 1 }
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WebMock.stub_request(
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:post,
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"#{SiteSetting.ai_embeddings_discourse_service_api_endpoint}/api/v1/classify",
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).to_return(status: 200, body: JSON.dump(embedding))
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Jobs::EmbeddingsBackfill.new.execute({})
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topic_ids = DB.query_single("SELECT topic_id from #{vector_rep.topic_table_name}")
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expect(topic_ids).to eq([first_topic.id])
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# pulse again for the rest (and cover code)
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SiteSetting.ai_embeddings_backfill_batch_size = 100
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Jobs::EmbeddingsBackfill.new.execute({})
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topic_ids = DB.query_single("SELECT topic_id from #{vector_rep.topic_table_name}")
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expect(topic_ids).to contain_exactly(first_topic.id, second_topic.id, third_topic.id)
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end
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end
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