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https://github.com/discourse/discourse-ai.git
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03fc94684b
* 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
66 lines
1.6 KiB
Ruby
66 lines
1.6 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Completions
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module Endpoints
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class CannedResponse
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CANNED_RESPONSE_ERROR = Class.new(StandardError)
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def self.can_contact?(_)
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Rails.env.test?
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end
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def initialize(responses)
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@responses = responses
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@completions = 0
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@prompt = nil
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end
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def normalize_model_params(model_params)
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# max_tokens, temperature, stop_sequences are already supported
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model_params
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end
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attr_reader :responses, :completions, :prompt
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def perform_completion!(prompt, _user, _model_params)
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@prompt = prompt
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response = responses[completions]
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if response.nil?
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raise CANNED_RESPONSE_ERROR,
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"The number of completions you requested exceed the number of canned responses"
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end
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@completions += 1
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if block_given?
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cancelled = false
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cancel_fn = lambda { cancelled = true }
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# We buffer and return tool invocations in one go.
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if is_tool?(response)
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yield(response, cancel_fn)
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else
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response.each_char do |char|
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break if cancelled
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yield(char, cancel_fn)
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end
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end
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else
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response
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end
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end
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def tokenizer
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DiscourseAi::Tokenizer::OpenAiTokenizer
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end
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private
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def is_tool?(response)
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Nokogiri::HTML5.fragment(response).at("function_calls").present?
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end
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end
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end
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end
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end
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