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

39 lines
1.3 KiB
Ruby

# frozen_string_literal: true
RSpec.describe Jobs::GenerateRagEmbeddings do
describe "#execute" do
let(:truncation) { DiscourseAi::Embeddings::Strategies::Truncation.new }
let(:vector_rep) do
DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(truncation)
end
let(:expected_embedding) { [0.0038493] * vector_rep.dimensions }
fab!(:ai_persona)
fab!(:rag_document_fragment_1) { Fabricate(:rag_document_fragment, ai_persona: ai_persona) }
fab!(:rag_document_fragment_2) { Fabricate(:rag_document_fragment, ai_persona: 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 #{vector_rep.rag_fragments_table_name}").first
expect(embeddings_count).to eq(expected_embeddings)
end
end
end