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This commit introduces a new feature for AI Personas called the "Question Consolidator LLM". The purpose of the Question Consolidator is to consolidate a user's latest question into a self-contained, context-rich question before querying the vector database for relevant fragments. This helps improve the quality and relevance of the retrieved fragments. Previous to this change we used the last 10 interactions, this is not ideal cause the RAG would "lock on" to an answer. EG: - User: how many cars are there in europe - Model: detailed answer about cars in europe including the term car and vehicle many times - User: Nice, what about trains are there in the US In the above example "trains" and "US" becomes very low signal given there are pages and pages talking about cars and europe. This mean retrieval is sub optimal. Instead, we pass the history to the "question consolidator", it would simply consolidate the question to "How many trains are there in the United States", which would make it fare easier for the vector db to find relevant content. The llm used for question consolidator can often be less powerful than the model you are talking to, we recommend using lighter weight and fast models cause the task is very simple. This is configurable from the persona ui. This PR also removes support for {uploads} placeholder, this is too complicated to get right and we want freedom to shift RAG implementation. Key changes: 1. Added a new `question_consolidator_llm` column to the `ai_personas` table to store the LLM model used for question consolidation. 2. Implemented the `QuestionConsolidator` module which handles the logic for consolidating the user's latest question. It extracts the relevant user and model messages from the conversation history, truncates them if needed to fit within the token limit, and generates a consolidated question prompt. 3. Updated the `Persona` class to use the Question Consolidator LLM (if configured) when crafting the RAG fragments prompt. It passes the conversation context to the consolidator to generate a self-contained question. 4. Added UI elements in the AI Persona editor to allow selecting the Question Consolidator LLM. Also made some UI tweaks to conditionally show/hide certain options based on persona configuration. 5. Wrote unit tests for the QuestionConsolidator module and updated existing persona tests to cover the new functionality. This feature enables AI Personas to better understand the context and intent behind a user's question by consolidating the conversation history into a single, focused question. This can lead to more relevant and accurate responses from the AI assistant.
395 lines
11 KiB
Ruby
395 lines
11 KiB
Ruby
# frozen_string_literal: true
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class AiPersona < ActiveRecord::Base
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# places a hard limit, so per site we cache a maximum of 500 classes
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MAX_PERSONAS_PER_SITE = 500
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validates :name, presence: true, uniqueness: true, length: { maximum: 100 }
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validates :description, presence: true, length: { maximum: 2000 }
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validates :system_prompt, presence: true, length: { maximum: 10_000_000 }
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validate :system_persona_unchangeable, on: :update, if: :system
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validates :max_context_posts, numericality: { greater_than: 0 }, allow_nil: true
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# leaves some room for growth but sets a maximum to avoid memory issues
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# we may want to revisit this in the future
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validates :vision_max_pixels, numericality: { greater_than: 0, maximum: 4_000_000 }
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validates :rag_chunk_tokens, numericality: { greater_than: 0, maximum: 50_000 }
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validates :rag_chunk_overlap_tokens, numericality: { greater_than: -1, maximum: 200 }
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validates :rag_conversation_chunks, numericality: { greater_than: 0, maximum: 1000 }
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belongs_to :created_by, class_name: "User"
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belongs_to :user
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has_many :upload_references, as: :target, dependent: :destroy
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has_many :uploads, through: :upload_references
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has_many :rag_document_fragment, dependent: :destroy
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has_many :rag_document_fragments, through: :ai_persona_rag_document_fragments
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before_destroy :ensure_not_system
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before_update :regenerate_rag_fragments
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def self.persona_cache
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@persona_cache ||= ::DiscourseAi::MultisiteHash.new("persona_cache")
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end
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scope :ordered, -> { order("priority DESC, lower(name) ASC") }
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def self.all_personas
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persona_cache[:value] ||= AiPersona
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.ordered
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.where(enabled: true)
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.all
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.limit(MAX_PERSONAS_PER_SITE)
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.map(&:class_instance)
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end
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def self.persona_users(user: nil)
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persona_users =
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persona_cache[:persona_users] ||= AiPersona
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.where(enabled: true)
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.joins(:user)
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.pluck(
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"ai_personas.id, users.id, users.username_lower, allowed_group_ids, default_llm, mentionable",
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)
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.map do |id, user_id, username, allowed_group_ids, default_llm, mentionable|
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{
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id: id,
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user_id: user_id,
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username: username,
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allowed_group_ids: allowed_group_ids,
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default_llm: default_llm,
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mentionable: mentionable,
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}
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end
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if user
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persona_users.select { |mentionable| user.in_any_groups?(mentionable[:allowed_group_ids]) }
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else
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persona_users
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end
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end
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def self.mentionables(user: nil)
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all_mentionables =
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persona_cache[:mentionable_usernames] ||= AiPersona
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.where(mentionable: true)
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.where(enabled: true)
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.joins(:user)
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.pluck("ai_personas.id, users.id, users.username_lower, allowed_group_ids, default_llm")
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.map do |id, user_id, username, allowed_group_ids, default_llm|
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{
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id: id,
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user_id: user_id,
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username: username,
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allowed_group_ids: allowed_group_ids,
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default_llm: default_llm,
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}
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end
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if user
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all_mentionables.select { |mentionable| user.in_any_groups?(mentionable[:allowed_group_ids]) }
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else
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all_mentionables
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end
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end
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after_commit :bump_cache
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def bump_cache
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self.class.persona_cache.flush!
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end
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def class_instance
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allowed_group_ids = self.allowed_group_ids
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id = self.id
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system = self.system
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user_id = self.user_id
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mentionable = self.mentionable
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default_llm = self.default_llm
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max_context_posts = self.max_context_posts
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vision_enabled = self.vision_enabled
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vision_max_pixels = self.vision_max_pixels
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rag_conversation_chunks = self.rag_conversation_chunks
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question_consolidator_llm = self.question_consolidator_llm
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persona_class = DiscourseAi::AiBot::Personas::Persona.system_personas_by_id[self.id]
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if persona_class
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persona_class.define_singleton_method :allowed_group_ids do
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allowed_group_ids
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end
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persona_class.define_singleton_method :id do
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id
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end
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persona_class.define_singleton_method :system do
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system
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end
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persona_class.define_singleton_method :user_id do
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user_id
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end
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persona_class.define_singleton_method :mentionable do
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mentionable
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end
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persona_class.define_singleton_method :default_llm do
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default_llm
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end
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persona_class.define_singleton_method :max_context_posts do
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max_context_posts
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end
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persona_class.define_singleton_method :vision_enabled do
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vision_enabled
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end
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persona_class.define_singleton_method :vision_max_pixels do
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vision_max_pixels
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end
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persona_class.define_singleton_method :question_consolidator_llm do
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question_consolidator_llm
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end
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persona_class.define_singleton_method :rag_conversation_chunks do
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rag_conversation_chunks
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end
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return persona_class
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end
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name = self.name
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description = self.description
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ai_persona_id = self.id
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options = {}
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tools = self.respond_to?(:commands) ? self.commands : self.tools
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tools =
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tools.filter_map do |element|
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inner_name = element
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current_options = nil
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if element.is_a?(Array)
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inner_name = element[0]
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current_options = element[1]
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end
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# Won't migrate data yet. Let's rewrite to the tool name.
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inner_name = inner_name.gsub("Command", "")
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inner_name = "List#{inner_name}" if %w[Categories Tags].include?(inner_name)
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begin
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klass = ("DiscourseAi::AiBot::Tools::#{inner_name}").constantize
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options[klass] = current_options if current_options
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klass
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rescue StandardError
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nil
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end
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end
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Class.new(DiscourseAi::AiBot::Personas::Persona) do
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define_singleton_method :id do
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id
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end
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define_singleton_method :name do
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name
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end
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define_singleton_method :user_id do
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user_id
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end
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define_singleton_method :description do
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description
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end
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define_singleton_method :system do
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system
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end
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define_singleton_method :allowed_group_ids do
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allowed_group_ids
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end
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define_singleton_method :user_id do
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user_id
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end
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define_singleton_method :mentionable do
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mentionable
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end
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define_singleton_method :default_llm do
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default_llm
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end
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define_singleton_method :max_context_posts do
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max_context_posts
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end
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define_singleton_method :vision_enabled do
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vision_enabled
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end
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define_singleton_method :vision_max_pixels do
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vision_max_pixels
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end
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define_singleton_method :rag_conversation_chunks do
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rag_conversation_chunks
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end
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define_singleton_method :question_consolidator_llm do
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question_consolidator_llm
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end
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define_singleton_method :to_s do
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"#<DiscourseAi::AiBot::Personas::Persona::Custom @name=#{self.name} @allowed_group_ids=#{self.allowed_group_ids.join(",")}>"
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end
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define_singleton_method :inspect do
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"#<DiscourseAi::AiBot::Personas::Persona::Custom @name=#{self.name} @allowed_group_ids=#{self.allowed_group_ids.join(",")}>"
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end
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define_method :initialize do |*args, **kwargs|
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@ai_persona = AiPersona.find_by(id: ai_persona_id)
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super(*args, **kwargs)
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end
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define_method :persona_id do
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@ai_persona&.id
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end
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define_method :tools do
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tools
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end
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define_method :options do
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options
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end
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define_method :temperature do
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@ai_persona&.temperature
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end
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define_method :top_p do
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@ai_persona&.top_p
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end
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define_method :system_prompt do
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@ai_persona&.system_prompt || "You are a helpful bot."
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end
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define_method :uploads do
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@ai_persona&.uploads
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end
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end
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end
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FIRST_PERSONA_USER_ID = -1200
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def create_user!
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raise "User already exists" if user_id && User.exists?(user_id)
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# find the first id smaller than FIRST_USER_ID that is not taken
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id = nil
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id = DB.query_single(<<~SQL, FIRST_PERSONA_USER_ID, FIRST_PERSONA_USER_ID - 200).first
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WITH seq AS (
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SELECT generate_series(?, ?, -1) AS id
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)
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SELECT seq.id FROM seq
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LEFT JOIN users ON users.id = seq.id
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WHERE users.id IS NULL
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ORDER BY seq.id DESC
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SQL
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id = DB.query_single(<<~SQL).first if id.nil?
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SELECT min(id) - 1 FROM users
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SQL
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# note .invalid is a reserved TLD which will route nowhere
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user =
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User.new(
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email: "#{SecureRandom.hex}@does-not-exist.invalid",
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name: name.titleize,
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username: UserNameSuggester.suggest(name + "_bot"),
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active: true,
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approved: true,
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trust_level: TrustLevel[4],
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id: id,
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)
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user.save!(validate: false)
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update!(user_id: user.id)
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user
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end
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def regenerate_rag_fragments
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if rag_chunk_tokens_changed? || rag_chunk_overlap_tokens_changed?
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RagDocumentFragment.where(ai_persona: self).delete_all
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end
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end
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private
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def system_persona_unchangeable
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if top_p_changed? || temperature_changed? || system_prompt_changed? || commands_changed? ||
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name_changed? || description_changed?
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errors.add(:base, I18n.t("discourse_ai.ai_bot.personas.cannot_edit_system_persona"))
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end
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end
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def ensure_not_system
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if system
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errors.add(:base, I18n.t("discourse_ai.ai_bot.personas.cannot_delete_system_persona"))
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throw :abort
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end
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end
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end
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# == Schema Information
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#
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# Table name: ai_personas
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#
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# id :bigint not null, primary key
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# name :string(100) not null
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# description :string(2000) not null
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# commands :json not null
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# system_prompt :string(10000000) not null
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# allowed_group_ids :integer default([]), not null, is an Array
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# created_by_id :integer
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# enabled :boolean default(TRUE), not null
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# created_at :datetime not null
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# updated_at :datetime not null
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# system :boolean default(FALSE), not null
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# priority :boolean default(FALSE), not null
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# temperature :float
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# top_p :float
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# user_id :integer
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# mentionable :boolean default(FALSE), not null
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# default_llm :text
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# max_context_posts :integer
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# max_post_context_tokens :integer
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# max_context_tokens :integer
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# vision_enabled :boolean default(FALSE), not null
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# vision_max_pixels :integer default(1048576), not null
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# rag_chunk_tokens :integer default(374), not null
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# rag_chunk_overlap_tokens :integer default(10), not null
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# rag_conversation_chunks :integer default(10), not null
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# question_consolidator_llm :text
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#
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# Indexes
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#
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# index_ai_personas_on_name (name) UNIQUE
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#
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