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0634b85a81
* UX: Validations to Llm-backed features (except AI Bot) This change is part of an ongoing effort to prevent enabling a broken feature due to lack of configuration. We also want to explicit which provider we are going to use. For example, Claude models are available through AWS Bedrock and Anthropic, but the configuration differs. Validations are: * You must choose a model before enabling the feature. * You must turn off the feature before setting the model to blank. * You must configure each model settings before being able to select it. * Add provider name to summarization options * vLLM can technically support same models as HF * Check we can talk to the selected model * Check for Bedrock instead of anthropic as a site could have both creds setup
69 lines
2.5 KiB
Ruby
69 lines
2.5 KiB
Ruby
# frozen_string_literal: true
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RSpec.describe DiscourseAi::Summarization::Strategies::FoldContent do
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describe "#summarize" do
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subject(:strategy) { described_class.new(model) }
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let(:summarize_text) { "This is a text" }
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let(:model_tokens) do
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# Make sure each content fits in a single chunk.
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# 700 is the number of tokens reserved for the prompt.
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700 + DiscourseAi::Tokenizer::OpenAiTokenizer.size("(1 asd said: This is a text ") + 3
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end
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let(:model) do
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DiscourseAi::Summarization::Models::OpenAi.new("fake:fake", max_tokens: model_tokens)
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end
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let(:content) { { contents: [{ poster: "asd", id: 1, text: summarize_text }] } }
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let(:single_summary) { "this is a single summary" }
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let(:concatenated_summary) { "this is a concatenated summary" }
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let(:user) { User.new }
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context "when the content to summarize fits in a single call" do
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it "does one call to summarize content" do
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result =
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DiscourseAi::Completions::Llm.with_prepared_responses([single_summary]) do |spy|
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strategy.summarize(content, user).tap { expect(spy.completions).to eq(1) }
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end
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expect(result[:summary]).to eq(single_summary)
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end
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end
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context "when the content to summarize doesn't fit in a single call" do
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it "summarizes each chunk and then concatenates them" do
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content[:contents] << { poster: "asd2", id: 2, text: summarize_text }
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result =
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DiscourseAi::Completions::Llm.with_prepared_responses(
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[single_summary, single_summary, concatenated_summary],
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) { |spy| strategy.summarize(content, user).tap { expect(spy.completions).to eq(3) } }
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expect(result[:summary]).to eq(concatenated_summary)
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end
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it "keeps splitting into chunks until the content fits into a single call to create a cohesive narrative" do
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content[:contents] << { poster: "asd2", id: 2, text: summarize_text }
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max_length_response = "(1 asd said: This is a text "
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chunk_of_chunks = "I'm smol"
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result =
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DiscourseAi::Completions::Llm.with_prepared_responses(
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[
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max_length_response,
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max_length_response,
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chunk_of_chunks,
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chunk_of_chunks,
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concatenated_summary,
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],
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) { |spy| strategy.summarize(content, user).tap { expect(spy.completions).to eq(5) } }
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expect(result[:summary]).to eq(concatenated_summary)
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end
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end
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end
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end
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