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This persona searches Discourse Meta for help with Discourse and points users at relevant posts. It is somewhat similar to using "Forum Helper" on meta, with the notable difference that we can not lean on semantic search so using some prompt engineering we try to keep it simple.
224 lines
7.5 KiB
Ruby
224 lines
7.5 KiB
Ruby
#frozen_string_literal: true
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module DiscourseAi
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module AiBot
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module Tools
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class Search < Tool
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MIN_SEMANTIC_RESULTS = 5
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class << self
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def signature
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{
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name: name,
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description:
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"Will search topics in the current discourse instance, when rendering always prefer to link to the topics you find",
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parameters: [
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{
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name: "search_query",
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description:
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"Specific keywords to search for, space separated (correct bad spelling, remove connector words)",
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type: "string",
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},
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{
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name: "user",
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description:
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"Filter search results to this username (only include if user explicitly asks to filter by user)",
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type: "string",
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},
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{
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name: "order",
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description: "search result order",
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type: "string",
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enum: %w[latest latest_topic oldest views likes],
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},
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{
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name: "limit",
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description:
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"limit number of results returned (generally prefer to just keep to default)",
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type: "integer",
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},
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{
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name: "max_posts",
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description:
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"maximum number of posts on the topics (topics where lots of people posted)",
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type: "integer",
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},
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{
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name: "tags",
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description:
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"list of tags to search for. Use + to join with OR, use , to join with AND",
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type: "string",
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},
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{ name: "category", description: "category name to filter to", type: "string" },
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{
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name: "before",
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description: "only topics created before a specific date YYYY-MM-DD",
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type: "string",
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},
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{
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name: "after",
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description: "only topics created after a specific date YYYY-MM-DD",
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type: "string",
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},
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{
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name: "status",
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description: "search for topics in a particular state",
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type: "string",
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enum: %w[open closed archived noreplies single_user],
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},
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],
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}
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end
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def name
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"search"
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end
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def custom_system_message
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<<~TEXT
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You were trained on OLD data, lean on search to get up to date information about this forum
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When searching try to SIMPLIFY search terms
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Discourse search joins all terms with AND. Reduce and simplify terms to find more results.
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TEXT
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end
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def accepted_options
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[option(:base_query, type: :string), option(:max_results, type: :integer)]
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end
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end
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def search_args
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parameters.slice(:category, :user, :order, :max_posts, :tags, :before, :after, :status)
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end
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def invoke(bot_user, llm)
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search_string =
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search_args.reduce(+parameters[:search_query].to_s) do |memo, (key, value)|
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return memo if value.blank?
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memo << " " << "#{key}:#{value}"
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end
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@last_query = search_string
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yield(I18n.t("discourse_ai.ai_bot.searching", query: search_string))
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if options[:base_query].present?
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search_string = "#{search_string} #{options[:base_query]}"
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end
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results =
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::Search.execute(
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search_string.to_s + " status:public",
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search_type: :full_page,
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guardian: Guardian.new(),
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)
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# let's be frugal with tokens, 50 results is too much and stuff gets cut off
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max_results = calculate_max_results(llm)
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results_limit = parameters[:limit] || max_results
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results_limit = max_results if parameters[:limit].to_i > max_results
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should_try_semantic_search =
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SiteSetting.ai_embeddings_semantic_search_enabled && results_limit == max_results &&
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parameters[:search_query].present?
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max_semantic_results = max_results / 4
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results_limit = results_limit - max_semantic_results if should_try_semantic_search
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posts = results&.posts || []
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posts = posts[0..results_limit.to_i - 1]
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if should_try_semantic_search
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semantic_search = DiscourseAi::Embeddings::SemanticSearch.new(Guardian.new())
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topic_ids = Set.new(posts.map(&:topic_id))
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search = ::Search.new(search_string, guardian: Guardian.new)
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results = nil
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begin
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results = semantic_search.search_for_topics(search.term)
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rescue => e
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Discourse.warn_exception(e, message: "Semantic search failed")
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end
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if results
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results = search.apply_filters(results)
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results.each do |post|
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next if topic_ids.include?(post.topic_id)
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topic_ids << post.topic_id
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posts << post
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break if posts.length >= max_results
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end
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end
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end
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@last_num_results = posts.length
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# this is the general pattern from core
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# if there are millions of hidden tags it may fail
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hidden_tags = nil
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if posts.blank?
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{ args: parameters, rows: [], instruction: "nothing was found, expand your search" }
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else
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format_results(posts, args: parameters) do |post|
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category_names = [
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post.topic.category&.parent_category&.name,
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post.topic.category&.name,
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].compact.join(" > ")
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row = {
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title: post.topic.title,
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url: Discourse.base_path + post.url,
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username: post.user&.username,
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excerpt: post.excerpt,
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created: post.created_at,
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category: category_names,
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likes: post.like_count,
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topic_views: post.topic.views,
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topic_likes: post.topic.like_count,
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topic_replies: post.topic.posts_count - 1,
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}
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if SiteSetting.tagging_enabled
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hidden_tags ||= DiscourseTagging.hidden_tag_names
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# using map over pluck to avoid n+1 (assuming caller preloading)
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tags = post.topic.tags.map(&:name) - hidden_tags
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row[:tags] = tags.join(", ") if tags.present?
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end
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row
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end
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end
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end
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protected
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def description_args
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{
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count: @last_num_results || 0,
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query: @last_query || "",
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url: "#{Discourse.base_path}/search?q=#{CGI.escape(@last_query || "")}",
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}
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end
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private
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def calculate_max_results(llm)
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max_results = options[:max_results].to_i
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return [max_results, 100].min if max_results > 0
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if llm.max_prompt_tokens > 30_000
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60
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elsif llm.max_prompt_tokens > 10_000
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40
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else
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20
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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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end
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