249 lines
7.4 KiB
Ruby
249 lines
7.4 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module AiHelper
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class Assistant
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AI_HELPER_PROMPTS_CACHE_KEY = "ai_helper_prompts"
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def self.clear_prompt_cache!
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Discourse.cache.delete(AI_HELPER_PROMPTS_CACHE_KEY)
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end
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def available_prompts
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Discourse
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.cache
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.fetch(AI_HELPER_PROMPTS_CACHE_KEY, expires_in: 30.minutes) do
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prompts = CompletionPrompt.where(enabled: true)
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# Hide illustrate_post if disabled
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prompts =
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prompts.where.not(
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name: "illustrate_post",
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) if SiteSetting.ai_helper_illustrate_post_model == "disabled"
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prompts =
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prompts.map do |prompt|
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translation =
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I18n.t("discourse_ai.ai_helper.prompts.#{prompt.name}", default: nil) ||
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prompt.translated_name || prompt.name
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{
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id: prompt.id,
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name: prompt.name,
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translated_name: translation,
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prompt_type: prompt.prompt_type,
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icon: icon_map(prompt.name),
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location: location_map(prompt.name),
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}
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end
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prompts
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end
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end
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def custom_locale_instructions(user = nil)
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locale = SiteSetting.default_locale
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locale = user.locale || SiteSetting.default_locale if SiteSetting.allow_user_locale && user
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locale_hash = LocaleSiteSetting.language_names[locale]
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if locale != "en" && locale_hash
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locale_description = "#{locale_hash["name"]} (#{locale_hash["nativeName"]})"
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"It is imperative that you write your answer in #{locale_description}, you are interacting with a #{locale_description} speaking user. Leave tag names in English."
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else
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nil
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end
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end
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def localize_prompt!(prompt, user = nil)
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locale_instructions = custom_locale_instructions(user)
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if locale_instructions
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prompt.messages[0][:content] = prompt.messages[0][:content] + locale_instructions
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end
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if prompt.messages[0][:content].include?("%LANGUAGE%")
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locale = SiteSetting.default_locale
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locale = user.locale || SiteSetting.default_locale if SiteSetting.allow_user_locale &&
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user
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locale_hash = LocaleSiteSetting.language_names[locale]
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prompt.messages[0][:content] = prompt.messages[0][:content].gsub(
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"%LANGUAGE%",
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"#{locale_hash["name"]}",
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)
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end
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end
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def generate_prompt(completion_prompt, input, user, &block)
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llm = DiscourseAi::Completions::Llm.proxy(SiteSetting.ai_helper_model)
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prompt = completion_prompt.messages_with_input(input)
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localize_prompt!(prompt, user)
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llm.generate(
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prompt,
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user: user,
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temperature: completion_prompt.temperature,
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stop_sequences: completion_prompt.stop_sequences,
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&block
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)
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end
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def generate_and_send_prompt(completion_prompt, input, user)
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completion_result = generate_prompt(completion_prompt, input, user)
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result = { type: completion_prompt.prompt_type }
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result[:suggestions] = (
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if completion_prompt.list?
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parse_list(completion_result).map { |suggestion| sanitize_result(suggestion) }
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else
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sanitized = sanitize_result(completion_result)
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result[:diff] = parse_diff(input, sanitized) if completion_prompt.diff?
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[sanitized]
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end
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)
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result
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end
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def stream_prompt(completion_prompt, input, user, channel)
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streamed_result = +""
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start = Time.now
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generate_prompt(completion_prompt, input, user) do |partial_response, cancel_function|
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streamed_result << partial_response
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# Throttle the updates
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if (Time.now - start > 0.5) || Rails.env.test?
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payload = { result: sanitize_result(streamed_result), done: false }
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publish_update(channel, payload, user)
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start = Time.now
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end
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end
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sanitized_result = sanitize_result(streamed_result)
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if sanitized_result.present?
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publish_update(channel, { result: sanitized_result, done: true }, user)
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end
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end
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def generate_image_caption(image_url, user)
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if SiteSetting.ai_helper_image_caption_model == "llava"
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parameters = {
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input: {
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image: image_url,
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top_p: 1,
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max_tokens: 1024,
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temperature: 0.2,
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prompt: "Please describe this image in a single sentence",
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},
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}
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::DiscourseAi::Inference::Llava.perform!(parameters).dig(:output).join
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else
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prompt =
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DiscourseAi::Completions::Prompt.new(
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messages: [
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{
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type: :user,
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content: [
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{
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type: "text",
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text:
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"Describe this image in a single sentence#{custom_locale_instructions(user)}",
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},
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{ type: "image_url", image_url: image_url },
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],
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},
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],
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skip_validations: true,
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)
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DiscourseAi::Completions::Llm.proxy(SiteSetting.ai_helper_image_caption_model).generate(
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prompt,
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user: Discourse.system_user,
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max_tokens: 1024,
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)
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end
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end
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private
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SANITIZE_REGEX_STR =
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%w[term context topic replyTo input output result]
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.map { |tag| "<#{tag}>\\n?|\\n?</#{tag}>" }
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.join("|")
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SANITIZE_REGEX = Regexp.new(SANITIZE_REGEX_STR, Regexp::IGNORECASE | Regexp::MULTILINE)
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def sanitize_result(result)
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result.gsub(SANITIZE_REGEX, "")
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end
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def publish_update(channel, payload, user)
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MessageBus.publish(channel, payload, user_ids: [user.id])
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end
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def icon_map(name)
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case name
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when "translate"
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"language"
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when "generate_titles"
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"heading"
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when "proofread"
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"spell-check"
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when "markdown_table"
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"table"
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when "tone"
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"microphone"
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when "custom_prompt"
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"comment"
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when "rewrite"
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"pen"
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when "explain"
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"question"
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when "illustrate_post"
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"images"
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else
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nil
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end
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end
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def location_map(name)
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case name
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when "translate"
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%w[composer post]
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when "generate_titles"
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%w[composer]
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when "proofread"
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%w[composer post]
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when "markdown_table"
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%w[composer]
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when "tone"
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%w[composer]
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when "custom_prompt"
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%w[composer post]
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when "rewrite"
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%w[composer]
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when "explain"
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%w[post]
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when "summarize"
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%w[post]
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when "illustrate_post"
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%w[composer]
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else
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%w[composer post]
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end
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end
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def parse_diff(text, suggestion)
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cooked_text = PrettyText.cook(text)
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cooked_suggestion = PrettyText.cook(suggestion)
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DiscourseDiff.new(cooked_text, cooked_suggestion).inline_html
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end
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def parse_list(list)
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Nokogiri::HTML5.fragment(list).css("item").map(&:text)
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end
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end
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end
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end
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