discourse-ai/spec/jobs/regular/generate_rag_embeddings_spe...

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# frozen_string_literal: true
RSpec.describe Jobs::GenerateRagEmbeddings do
describe "#execute" do
let(:vector_rep) { DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation }
let(:expected_embedding) { [0.0038493] * vector_rep.dimensions }
fab!(:ai_persona)
fab!(:rag_document_fragment_1) { Fabricate(:rag_document_fragment, target: ai_persona) }
fab!(:rag_document_fragment_2) { Fabricate(:rag_document_fragment, target: ai_persona) }
before do
SiteSetting.ai_embeddings_enabled = true
SiteSetting.ai_embeddings_discourse_service_api_endpoint = "http://test.com"
WebMock.stub_request(
:post,
"#{SiteSetting.ai_embeddings_discourse_service_api_endpoint}/api/v1/classify",
).to_return(status: 200, body: JSON.dump(expected_embedding))
end
it "generates a new vector for each fragment" do
expected_embeddings = 2
subject.execute(fragment_ids: [rag_document_fragment_1.id, rag_document_fragment_2.id])
embeddings_count =
DB.query_single(
"SELECT COUNT(*) from #{DiscourseAi::Embeddings::Schema::RAG_DOCS_TABLE}",
).first
expect(embeddings_count).to eq(expected_embeddings)
end
describe "Publishing progress updates" do
it "sends an update through mb after a batch finishes" do
updates =
MessageBus.track_publish("/discourse-ai/rag/#{rag_document_fragment_1.upload_id}") do
subject.execute(fragment_ids: [rag_document_fragment_1.id])
end
upload_index_stats = updates.last.data
expect(upload_index_stats[:total]).to eq(1)
expect(upload_index_stats[:indexed]).to eq(1)
expect(upload_index_stats[:left]).to eq(0)
end
end
end
end