72 lines
2.4 KiB
Ruby
72 lines
2.4 KiB
Ruby
# frozen_string_literal: true
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RSpec.describe DiscourseAi::Completions::Llm do
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subject(:llm) do
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described_class.new(
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DiscourseAi::Completions::Dialects::OrcaStyle.new,
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canned_response,
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"Upstage-Llama-2-*-instruct-v2",
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)
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end
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fab!(:user) { Fabricate(:user) }
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describe ".proxy" do
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it "raises an exception when we can't proxy the model" do
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fake_model = "unknown_v2"
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expect { described_class.proxy(fake_model) }.to(
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raise_error(DiscourseAi::Completions::Llm::UNKNOWN_MODEL),
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)
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end
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end
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describe "#completion!" do
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let(:prompt) do
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{
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insts: <<~TEXT,
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I want you to act as a title generator for written pieces. I will provide you with a text,
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and you will generate five attention-grabbing titles. Please keep the title concise and under 20 words,
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and ensure that the meaning is maintained. Replies will utilize the language type of the topic.
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TEXT
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input: <<~TEXT,
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Here is the text, inside <input></input> XML tags:
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<input>
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To perfect his horror, Caesar, surrounded at the base of the statue by the impatient daggers of his friends,
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discovers among the faces and blades that of Marcus Brutus, his protege, perhaps his son, and he no longer
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defends himself, but instead exclaims: 'You too, my son!' Shakespeare and Quevedo capture the pathetic cry.
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</input>
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TEXT
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post_insts:
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"Please put the translation between <ai></ai> tags and separate each title with a comma.",
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}
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end
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let(:canned_response) do
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DiscourseAi::Completions::Endpoints::CannedResponse.new(
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[
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"<ai>The solitary horse.,The horse etched in gold.,A horse's infinite journey.,A horse lost in time.,A horse's last ride.</ai>",
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],
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)
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end
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context "when getting the full response" do
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it "processes the prompt and return the response" do
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llm_response = llm.completion!(prompt, user)
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expect(llm_response).to eq(canned_response.responses[0])
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end
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end
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context "when getting a streamed response" do
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it "processes the prompt and call the given block with the partial response" do
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llm_response = +""
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llm.completion!(prompt, user) { |partial, cancel_fn| llm_response << partial }
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expect(llm_response).to eq(canned_response.responses[0])
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end
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end
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end
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end
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