112 lines
3.8 KiB
Ruby
112 lines
3.8 KiB
Ruby
# frozen_string_literal: true
|
|
|
|
module DiscourseAi
|
|
module InferredConcepts
|
|
class Applier
|
|
# Associates the provided concepts with a topic
|
|
# topic: a Topic instance
|
|
# concepts: an array of InferredConcept instances
|
|
def self.apply_to_topic(topic, concepts)
|
|
return if topic.blank? || concepts.blank?
|
|
|
|
concepts.each do |concept|
|
|
# Use the join table to associate the concept with the topic
|
|
# Avoid duplicates by using find_or_create_by
|
|
ActiveRecord::Base.connection.execute(<<~SQL)
|
|
INSERT INTO topics_inferred_concepts (topic_id, inferred_concept_id, created_at, updated_at)
|
|
VALUES (#{topic.id}, #{concept.id}, NOW(), NOW())
|
|
ON CONFLICT (topic_id, inferred_concept_id) DO NOTHING
|
|
SQL
|
|
end
|
|
end
|
|
|
|
# Extracts content from a topic for concept analysis
|
|
# Returns a string with the topic title and first few posts
|
|
def self.topic_content_for_analysis(topic)
|
|
return "" if topic.blank?
|
|
|
|
# Combine title and first few posts for analysis
|
|
posts = Post.where(topic_id: topic.id).order(:post_number).limit(10)
|
|
|
|
content = "Title: #{topic.title}\n\n"
|
|
content += posts.map do |p|
|
|
"#{p.post_number}) #{p.user.username}: #{p.raw}"
|
|
end.join("\n\n")
|
|
|
|
content
|
|
end
|
|
|
|
# Comprehensive method to analyze a topic and apply concepts
|
|
def self.analyze_and_apply(topic)
|
|
return if topic.blank?
|
|
|
|
# Get content to analyze
|
|
content = topic_content_for_analysis(topic)
|
|
|
|
# Identify concepts
|
|
concept_names = Finder.identify_concepts(content)
|
|
|
|
# Create or find concepts in the database
|
|
concepts = Finder.create_or_find_concepts(concept_names)
|
|
|
|
# Apply concepts to the topic
|
|
apply_to_topic(topic, concepts)
|
|
|
|
concepts
|
|
end
|
|
|
|
# Match a topic with existing concepts
|
|
def self.match_existing_concepts(topic)
|
|
return [] if topic.blank?
|
|
|
|
# Get content to analyze
|
|
content = topic_content_for_analysis(topic)
|
|
|
|
# Get all existing concepts
|
|
existing_concepts = InferredConcept.all.pluck(:name)
|
|
return [] if existing_concepts.empty?
|
|
|
|
# Use the ConceptMatcher persona to match concepts
|
|
matched_concept_names = match_concepts_to_content(content, existing_concepts)
|
|
|
|
# Find concepts in the database
|
|
matched_concepts = InferredConcept.where(name: matched_concept_names)
|
|
|
|
# Apply concepts to the topic
|
|
apply_to_topic(topic, matched_concepts)
|
|
|
|
matched_concepts
|
|
end
|
|
|
|
# Use ConceptMatcher persona to match content against provided concepts
|
|
def self.match_concepts_to_content(content, concept_list)
|
|
return [] if content.blank? || concept_list.blank?
|
|
|
|
# Prepare user message with content and concept list
|
|
user_message = <<~MESSAGE
|
|
Content to analyze:
|
|
#{content}
|
|
|
|
Available concepts to match:
|
|
#{concept_list.join(", ")}
|
|
MESSAGE
|
|
|
|
# Use the ConceptMatcher persona to match concepts
|
|
llm = DiscourseAi::Completions::Llm.default_llm
|
|
persona = DiscourseAi::Personas::ConceptMatcher.new
|
|
context = DiscourseAi::Personas::BotContext.new(
|
|
messages: [{ type: :user, content: user_message }],
|
|
user: Discourse.system_user
|
|
)
|
|
|
|
prompt = persona.craft_prompt(context)
|
|
response = llm.completion(prompt, extract_json: true)
|
|
|
|
return [] unless response.success?
|
|
|
|
matching_concepts = response.parsed_output["matching_concepts"]
|
|
matching_concepts || []
|
|
end
|
|
end
|
|
end
|
|
end |