discourse-ai/lib/ai_bot/personas/persona.rb

372 lines
11 KiB
Ruby

#frozen_string_literal: true
module DiscourseAi
module AiBot
module Personas
class Persona
class << self
def rag_conversation_chunks
10
end
def vision_enabled
false
end
def vision_max_pixels
1_048_576
end
def question_consolidator_llm
nil
end
def force_default_llm
false
end
def allow_chat_channel_mentions
false
end
def allow_chat_direct_messages
false
end
def system_personas
@system_personas ||= {
Personas::General => -1,
Personas::SqlHelper => -2,
Personas::Artist => -3,
Personas::SettingsExplorer => -4,
Personas::Researcher => -5,
Personas::Creative => -6,
Personas::DallE3 => -7,
Personas::DiscourseHelper => -8,
Personas::GithubHelper => -9,
}
end
def system_personas_by_id
@system_personas_by_id ||= system_personas.invert
end
def all(user:)
# listing tools has to be dynamic cause site settings may change
AiPersona.all_personas.filter do |persona|
next false if !user.in_any_groups?(persona.allowed_group_ids)
if persona.system
instance = persona.new
(
instance.required_tools == [] ||
(instance.required_tools - all_available_tools).empty?
)
else
true
end
end
end
def find_by(id: nil, name: nil, user:)
all(user: user).find { |persona| persona.id == id || persona.name == name }
end
def name
I18n.t("discourse_ai.ai_bot.personas.#{to_s.demodulize.underscore}.name")
end
def description
I18n.t("discourse_ai.ai_bot.personas.#{to_s.demodulize.underscore}.description")
end
def all_available_tools
tools = [
Tools::ListCategories,
Tools::Time,
Tools::Search,
Tools::Read,
Tools::DbSchema,
Tools::SearchSettings,
Tools::SettingContext,
Tools::RandomPicker,
Tools::DiscourseMetaSearch,
Tools::GithubFileContent,
Tools::GithubPullRequestDiff,
Tools::GithubSearchFiles,
Tools::WebBrowser,
Tools::JavascriptEvaluator,
]
tools << Tools::GithubSearchCode if SiteSetting.ai_bot_github_access_token.present?
tools << Tools::ListTags if SiteSetting.tagging_enabled
tools << Tools::Image if SiteSetting.ai_stability_api_key.present?
tools << Tools::DallE if SiteSetting.ai_openai_api_key.present?
if SiteSetting.ai_google_custom_search_api_key.present? &&
SiteSetting.ai_google_custom_search_cx.present?
tools << Tools::Google
end
tools
end
end
def id
@ai_persona&.id || self.class.system_personas[self.class]
end
def tools
[]
end
def force_tool_use
[]
end
def forced_tool_count
-1
end
def required_tools
[]
end
def temperature
nil
end
def top_p
nil
end
def options
{}
end
def available_tools
self
.class
.all_available_tools
.filter { |tool| tools.include?(tool) }
.concat(tools.filter(&:custom?))
end
def craft_prompt(context, llm: nil)
system_insts =
system_prompt.gsub(/\{(\w+)\}/) do |match|
found = context[match[1..-2].to_sym]
found.nil? ? match : found.to_s
end
prompt_insts = <<~TEXT.strip
#{system_insts}
#{available_tools.map(&:custom_system_message).compact_blank.join("\n")}
TEXT
question_consolidator_llm = llm
if self.class.question_consolidator_llm.present?
question_consolidator_llm =
DiscourseAi::Completions::Llm.proxy(self.class.question_consolidator_llm)
end
if context[:custom_instructions].present?
prompt_insts << "\n"
prompt_insts << context[:custom_instructions]
end
fragments_guidance =
rag_fragments_prompt(
context[:conversation_context].to_a,
llm: question_consolidator_llm,
user: context[:user],
)&.strip
prompt_insts << fragments_guidance if fragments_guidance.present?
prompt =
DiscourseAi::Completions::Prompt.new(
prompt_insts,
messages: context[:conversation_context].to_a,
topic_id: context[:topic_id],
post_id: context[:post_id],
)
prompt.max_pixels = self.class.vision_max_pixels if self.class.vision_enabled
prompt.tools = available_tools.map(&:signature) if available_tools
prompt
end
def find_tools(partial, bot_user:, llm:, context:)
return [] if !partial.include?("</invoke>")
parsed_function = Nokogiri::HTML5.fragment(partial)
parsed_function
.css("invoke")
.map do |fragment|
tool_instance(fragment, bot_user: bot_user, llm: llm, context: context)
end
.compact
end
protected
def tool_instance(parsed_function, bot_user:, llm:, context:)
function_id = parsed_function.at("tool_id")&.text
function_name = parsed_function.at("tool_name")&.text
return nil if function_name.nil?
tool_klass = available_tools.find { |c| c.signature.dig(:name) == function_name }
return nil if tool_klass.nil?
arguments = {}
tool_klass.signature[:parameters].to_a.each do |param|
name = param[:name]
value = parsed_function.at(name)&.text
if param[:type] == "array" && value
value =
begin
JSON.parse(value)
rescue JSON::ParserError
[value.to_s]
end
elsif param[:type] == "string" && value
value = strip_quotes(value).to_s
elsif param[:type] == "integer" && value
value = strip_quotes(value).to_i
end
if param[:enum] && value && !param[:enum].include?(value)
# invalid enum value
value = nil
end
arguments[name.to_sym] = value if value
end
tool_klass.new(
arguments,
tool_call_id: function_id || function_name,
persona_options: options[tool_klass].to_h,
bot_user: bot_user,
llm: llm,
context: context,
)
end
def strip_quotes(value)
if value.is_a?(String)
if value.start_with?('"') && value.end_with?('"')
value = value[1..-2]
elsif value.start_with?("'") && value.end_with?("'")
value = value[1..-2]
else
value
end
else
value
end
end
def rag_fragments_prompt(conversation_context, llm:, user:)
upload_refs =
UploadReference.where(target_id: id, target_type: "AiPersona").pluck(:upload_id)
return nil if !SiteSetting.ai_embeddings_enabled?
return nil if conversation_context.blank? || upload_refs.blank?
latest_interactions =
conversation_context.select { |ctx| %i[model user].include?(ctx[:type]) }.last(10)
return nil if latest_interactions.empty?
# first response
if latest_interactions.length == 1
consolidated_question = latest_interactions[0][:content]
else
consolidated_question =
DiscourseAi::AiBot::QuestionConsolidator.consolidate_question(
llm,
latest_interactions,
user,
)
end
return nil if !consolidated_question
strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
vector_rep =
DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
reranker = DiscourseAi::Inference::HuggingFaceTextEmbeddings
interactions_vector = vector_rep.vector_from(consolidated_question)
rag_conversation_chunks = self.class.rag_conversation_chunks
candidate_fragment_ids =
vector_rep.asymmetric_rag_fragment_similarity_search(
interactions_vector,
target_type: "AiPersona",
target_id: id,
limit:
(
if reranker.reranker_configured?
rag_conversation_chunks * 5
else
rag_conversation_chunks
end
),
offset: 0,
)
fragments =
RagDocumentFragment.where(upload_id: upload_refs, id: candidate_fragment_ids).pluck(
:fragment,
:metadata,
)
if reranker.reranker_configured?
guidance = fragments.map { |fragment, _metadata| fragment }
ranks =
DiscourseAi::Inference::HuggingFaceTextEmbeddings
.rerank(conversation_context.last[:content], guidance)
.to_a
.take(rag_conversation_chunks)
.map { _1[:index] }
if ranks.empty?
fragments = fragments.take(rag_conversation_chunks)
else
fragments = ranks.map { |idx| fragments[idx] }
end
end
<<~TEXT
<guidance>
The following texts will give you additional guidance for your response.
We included them because we believe they are relevant to this conversation topic.
Texts:
#{
fragments
.map do |fragment, metadata|
if metadata.present?
["# #{metadata}", fragment].join("\n")
else
fragment
end
end
.join("\n")
}
</guidance>
TEXT
end
end
end
end
end