124 lines
4.1 KiB
Ruby
124 lines
4.1 KiB
Ruby
# frozen_string_literal: true
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require_relative "../../../../support/openai_completions_inference_stubs"
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RSpec.describe DiscourseAi::Summarization::Models::OpenAi do
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subject(:model) { described_class.new(model_name, max_tokens: max_tokens) }
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let(:model_name) { "gpt-3.5-turbo" }
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let(:max_tokens) { 720 }
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let(:content) do
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{
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resource_path: "/t/1/POST_NUMBER",
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content_title: "This is a title",
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contents: [{ poster: "asd", id: 1, text: "This is a text" }],
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}
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end
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def as_chunk(item)
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{ ids: [item[:id]], summary: "(#{item[:id]} #{item[:poster]} said: #{item[:text]} " }
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end
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def expected_messages(contents, opts)
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base_prompt = <<~TEXT
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You are a summarization bot.
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You effectively summarise any text and reply ONLY with ONLY the summarized text.
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You condense it into a shorter version.
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You understand and generate Discourse forum Markdown.
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You format the response, including links, using markdown.
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Try generating links as well the format is #{opts[:resource_path]}. eg: [ref](#{opts[:resource_path]}/77)
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The discussion title is: #{opts[:content_title]}.
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TEXT
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messages = [{ role: "system", content: base_prompt }]
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text =
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contents.reduce("") do |memo, item|
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memo += "(#{item[:id]} #{item[:poster]} said: #{item[:text]} "
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end
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messages << {
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role: "user",
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content:
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"Summarize the following in 400 words. Keep the summary in the same language used in the text below.\n#{text}",
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}
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end
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describe "#summarize_in_chunks" do
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context "when the content fits in a single chunk" do
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it "performs a request to summarize" do
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opts = content.except(:contents)
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OpenAiCompletionsInferenceStubs.stub_response(
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expected_messages(content[:contents], opts),
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"This is summary 1",
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)
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chunks = content[:contents].map { |c| as_chunk(c) }
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summarized_chunks = model.summarize_in_chunks(chunks, opts).map { |c| c[:summary] }
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expect(summarized_chunks).to contain_exactly("This is summary 1")
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end
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end
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context "when the content fits in multiple chunks" do
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it "performs a request for each one to summarize" do
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content[:contents] << {
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poster: "asd2",
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id: 2,
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text: "This is a different text to summarize",
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}
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opts = content.except(:contents)
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content[:contents].each_with_index do |item, idx|
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OpenAiCompletionsInferenceStubs.stub_response(
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expected_messages([item], opts),
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"This is summary #{idx + 1}",
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)
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end
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chunks = content[:contents].map { |c| as_chunk(c) }
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summarized_chunks = model.summarize_in_chunks(chunks, opts).map { |c| c[:summary] }
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expect(summarized_chunks).to contain_exactly("This is summary 1", "This is summary 2")
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end
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end
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end
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describe "#concatenate_summaries" do
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it "combines all the different summaries into a single one" do
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messages = [
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{ role: "system", content: "You are a helpful bot" },
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{
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role: "user",
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content:
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"Concatenate these disjoint summaries, creating a cohesive narrative. Keep the summary in the same language used in the text below.\nsummary 1\nsummary 2",
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},
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]
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OpenAiCompletionsInferenceStubs.stub_response(messages, "concatenated summary")
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expect(model.concatenate_summaries(["summary 1", "summary 2"])).to eq("concatenated summary")
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end
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end
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describe "#summarize_with_truncation" do
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let(:max_tokens) { 709 }
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it "truncates the context to meet the token limit" do
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opts = content.except(:contents)
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truncated_version = expected_messages(content[:contents], opts)
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truncated_version.last[
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:content
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] = "Summarize the following in 400 words. Keep the summary in the same language used in the text below.\n(1 asd said: This is a"
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OpenAiCompletionsInferenceStubs.stub_response(truncated_version, "truncated summary")
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expect(model.summarize_with_truncation(content[:contents], opts)).to eq("truncated summary")
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end
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end
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end
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