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* FIX: AI helper not working correctly with mixtral This PR introduces a new function on the generic llm called #generate This will replace the implementation of completion! #generate introduces a new way to pass temperature, max_tokens and stop_sequences Then LLM implementers need to implement #normalize_model_params to ensure the generic names match the LLM specific endpoint This also adds temperature and stop_sequences to completion_prompts this allows for much more robust completion prompts * port everything over to #generate * Fix translation - On anthropic this no longer throws random "This is your translation:" - On mixtral this actually works * fix markdown table generation as well
221 lines
6.1 KiB
Ruby
221 lines
6.1 KiB
Ruby
# frozen_string_literal: true
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require_relative "endpoint_examples"
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RSpec.describe DiscourseAi::Completions::Endpoints::OpenAi do
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subject(:model) { described_class.new(model_name, DiscourseAi::Tokenizer::OpenAiTokenizer) }
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let(:model_name) { "gpt-3.5-turbo" }
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let(:generic_prompt) { { insts: "You are a helpful bot.", input: "write 3 words" } }
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let(:dialect) { DiscourseAi::Completions::Dialects::ChatGpt.new(generic_prompt, model_name) }
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let(:prompt) { dialect.translate }
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let(:tool_id) { "eujbuebfe" }
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let(:tool_deltas) do
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[
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{ id: tool_id, function: {} },
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{ id: tool_id, function: { name: "get_weather", arguments: "" } },
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{ id: tool_id, function: { name: "get_weather", arguments: "" } },
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{ id: tool_id, function: { name: "get_weather", arguments: "{" } },
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{ id: tool_id, function: { name: "get_weather", arguments: " \"location\": \"Sydney\"" } },
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{ id: tool_id, function: { name: "get_weather", arguments: " ,\"unit\": \"c\" }" } },
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]
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end
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let(:tool_call) do
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{
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id: tool_id,
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function: {
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name: "get_weather",
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arguments: { location: "Sydney", unit: "c" }.to_json,
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},
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}
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end
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let(:request_body) do
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model
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.default_options
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.merge(messages: prompt)
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.tap { |b| b[:tools] = dialect.tools if generic_prompt[:tools] }
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.to_json
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end
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let(:stream_request_body) do
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model
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.default_options
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.merge(messages: prompt, stream: true)
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.tap { |b| b[:tools] = dialect.tools if generic_prompt[:tools] }
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.to_json
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end
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def response(content, tool_call: false)
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message_content =
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if tool_call
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{ tool_calls: [content] }
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else
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{ content: content }
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end
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{
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id: "chatcmpl-6sZfAb30Rnv9Q7ufzFwvQsMpjZh8S",
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object: "chat.completion",
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created: 1_678_464_820,
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model: "gpt-3.5-turbo-0301",
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usage: {
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prompt_tokens: 337,
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completion_tokens: 162,
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total_tokens: 499,
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},
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choices: [
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{ message: { role: "assistant" }.merge(message_content), finish_reason: "stop", index: 0 },
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],
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}
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end
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def stub_response(prompt, response_text, tool_call: false)
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WebMock
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.stub_request(:post, "https://api.openai.com/v1/chat/completions")
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.with(body: request_body)
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.to_return(status: 200, body: JSON.dump(response(response_text, tool_call: tool_call)))
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end
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def stream_line(delta, finish_reason: nil, tool_call: false)
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message_content =
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if tool_call
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{ tool_calls: [delta] }
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else
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{ content: delta }
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end
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+"data: " << {
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id: "chatcmpl-#{SecureRandom.hex}",
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object: "chat.completion.chunk",
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created: 1_681_283_881,
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model: "gpt-3.5-turbo-0301",
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choices: [{ delta: message_content }],
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finish_reason: finish_reason,
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index: 0,
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}.to_json
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end
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def stub_streamed_response(prompt, deltas, tool_call: false)
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chunks =
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deltas.each_with_index.map do |_, index|
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if index == (deltas.length - 1)
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stream_line(deltas[index], finish_reason: "stop_sequence", tool_call: tool_call)
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else
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stream_line(deltas[index], tool_call: tool_call)
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end
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end
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chunks = (chunks.join("\n\n") << "data: [DONE]").split("")
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WebMock
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.stub_request(:post, "https://api.openai.com/v1/chat/completions")
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.with(body: stream_request_body)
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.to_return(status: 200, body: chunks)
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end
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it_behaves_like "an endpoint that can communicate with a completion service"
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context "when chunked encoding returns partial chunks" do
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# See: https://github.com/bblimke/webmock/issues/629
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let(:mock_net_http) do
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Class.new(Net::HTTP) do
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def request(*)
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super do |response|
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response.instance_eval do
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def read_body(*, &)
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@body.each(&)
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end
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end
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yield response if block_given?
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response
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end
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end
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end
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end
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let(:remove_original_net_http) { Net.send(:remove_const, :HTTP) }
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let(:original_http) { remove_original_net_http }
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let(:stub_net_http) { Net.send(:const_set, :HTTP, mock_net_http) }
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let(:remove_stubbed_net_http) { Net.send(:remove_const, :HTTP) }
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let(:restore_net_http) { Net.send(:const_set, :HTTP, original_http) }
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before do
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mock_net_http
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remove_original_net_http
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stub_net_http
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end
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after do
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remove_stubbed_net_http
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restore_net_http
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end
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it "will automatically recover from a bad payload" do
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# this should not happen, but lets ensure nothing bad happens
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# the row with test1 is invalid json
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raw_data = <<~TEXT
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d|a|t|a|:| |{|"choices":[{"delta":{"content":"test,"}}]}
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data: {"choices":[{"delta":{"content":"test1,"}}]
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data: {"choices":[{"delta":|{"content":"test2,"}}]}
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data: {"choices":[{"delta":{"content":"test3,"}}]|}
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data: {"choices":[{|"|d|elta":{"content":"test4"}}]|}
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data: [D|ONE]
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TEXT
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chunks = raw_data.split("|")
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stub_request(:post, "https://api.openai.com/v1/chat/completions").to_return(
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status: 200,
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body: chunks,
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)
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partials = []
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llm = DiscourseAi::Completions::Llm.proxy("gpt-3.5-turbo")
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llm.generate({ insts: "test" }, user: Discourse.system_user) { |partial| partials << partial }
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expect(partials.join).to eq("test,test2,test3,test4")
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end
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it "supports chunked encoding properly" do
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raw_data = <<~TEXT
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da|ta: {"choices":[{"delta":{"content":"test,"}}]}
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data: {"choices":[{"delta":{"content":"test1,"}}]}
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data: {"choices":[{"delta":|{"content":"test2,"}}]}
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data: {"choices":[{"delta":{"content":"test3,"}}]|}
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data: {"choices":[{|"|d|elta":{"content":"test4"}}]|}
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data: [D|ONE]
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TEXT
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chunks = raw_data.split("|")
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stub_request(:post, "https://api.openai.com/v1/chat/completions").to_return(
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status: 200,
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body: chunks,
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)
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partials = []
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llm = DiscourseAi::Completions::Llm.proxy("gpt-3.5-turbo")
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llm.generate({ insts: "test" }, user: Discourse.system_user) { |partial| partials << partial }
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expect(partials.join).to eq("test,test1,test2,test3,test4")
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end
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end
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end
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