122 lines
3.8 KiB
Ruby
122 lines
3.8 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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class SemanticSearch
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def self.clear_cache_for(query)
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digest = OpenSSL::Digest::SHA1.hexdigest(query)
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hyde_key =
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"semantic-search-#{digest}-#{SiteSetting.ai_embeddings_semantic_search_hyde_model}"
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Discourse.cache.delete(hyde_key)
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Discourse.cache.delete("#{hyde_key}-#{SiteSetting.ai_embeddings_model}")
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end
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def initialize(guardian)
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@guardian = guardian
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end
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def cached_query?(query)
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digest = OpenSSL::Digest::SHA1.hexdigest(query)
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embedding_key =
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build_embedding_key(
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digest,
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SiteSetting.ai_embeddings_semantic_search_hyde_model,
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SiteSetting.ai_embeddings_model,
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)
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Discourse.cache.read(embedding_key).present?
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end
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def search_for_topics(query, page = 1)
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max_results_per_page = 100
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limit = [Search.per_filter, max_results_per_page].min + 1
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offset = (page - 1) * limit
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search = Search.new(query, { guardian: guardian })
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search_term = search.term
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return [] if search_term.nil? || search_term.length < SiteSetting.min_search_term_length
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep =
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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digest = OpenSSL::Digest::SHA1.hexdigest(search_term)
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hyde_key = build_hyde_key(digest, SiteSetting.ai_embeddings_semantic_search_hyde_model)
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embedding_key =
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build_embedding_key(
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digest,
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SiteSetting.ai_embeddings_semantic_search_hyde_model,
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SiteSetting.ai_embeddings_model,
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)
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hypothetical_post =
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Discourse
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.cache
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.fetch(hyde_key, expires_in: 1.week) { hypothetical_post_from(search_term) }
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hypothetical_post_embedding =
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Discourse
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.cache
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.fetch(embedding_key, expires_in: 1.week) { vector_rep.vector_from(hypothetical_post) }
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candidate_topic_ids =
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vector_rep.asymmetric_topics_similarity_search(
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hypothetical_post_embedding,
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limit: limit,
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offset: offset,
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)
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semantic_results =
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::Post
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.where(post_type: ::Topic.visible_post_types(guardian.user))
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.public_posts
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.where("topics.visible")
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.where(topic_id: candidate_topic_ids, post_number: 1)
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.order("array_position(ARRAY#{candidate_topic_ids}, topic_id)")
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query_filter_results = search.apply_filters(semantic_results)
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guardian.filter_allowed_categories(query_filter_results)
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end
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private
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attr_reader :guardian
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def build_hyde_key(digest, hyde_model)
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"semantic-search-#{digest}-#{hyde_model}"
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end
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def build_embedding_key(digest, hyde_model, embedding_model)
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"#{build_hyde_key(digest, hyde_model)}-#{embedding_model}"
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end
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def hypothetical_post_from(search_term)
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prompt = DiscourseAi::Completions::Prompt.new(<<~TEXT.strip)
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You are a content creator for a forum. The forum description is as follows:
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#{SiteSetting.title}
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#{SiteSetting.site_description}
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Put the forum post between <ai></ai> tags.
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TEXT
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prompt.push(type: :user, content: <<~TEXT.strip)
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Using this description, write a forum post about the subject inside the <input></input> XML tags:
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<input>#{search_term}</input>
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TEXT
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llm_response =
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DiscourseAi::Completions::Llm.proxy(
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SiteSetting.ai_embeddings_semantic_search_hyde_model,
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).generate(prompt, user: @guardian.user)
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Nokogiri::HTML5.fragment(llm_response).at("ai")&.text&.presence || llm_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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