Keegan George 9be1049de6
DEV: Log AI related configuration to staff action log (#1416)
is update adds logging for changes made in the AI admin panel. When making configuration changes to Embeddings, LLMs, Personas, Tools, or Spam that aren't site setting related, changes will now be logged in Admin > Logs & Screening. This will help admins debug issues related to AI. In this update a helper lib is created called `AiStaffActionLogger` which can be easily used in the future to add logging support for any other admin config we need logged for AI.
2025-06-12 12:39:58 -07:00

294 lines
9.1 KiB
Ruby

# frozen_string_literal: true
module DiscourseAi
module Admin
class AiLlmsController < ::Admin::AdminController
requires_plugin ::DiscourseAi::PLUGIN_NAME
def index
llms = LlmModel.all.includes(:llm_quotas).order(:display_name)
render json: {
ai_llms:
ActiveModel::ArraySerializer.new(
llms,
each_serializer: LlmModelSerializer,
root: false,
scope: {
llm_usage: DiscourseAi::Configuration::LlmEnumerator.global_usage,
},
).as_json,
meta: {
provider_params: LlmModel.provider_params,
presets: DiscourseAi::Completions::Llm.presets,
providers: DiscourseAi::Completions::Llm.provider_names,
tokenizers:
DiscourseAi::Completions::Llm.tokenizer_names.map { |tn|
{ id: tn, name: tn.split("::").last }
},
},
}
end
def new
end
def edit
llm_model = LlmModel.find(params[:id])
render json: LlmModelSerializer.new(llm_model)
end
def create
llm_model = LlmModel.new(ai_llm_params)
# we could do nested attributes but the mechanics are not ideal leading
# to lots of complex debugging, this is simpler
quota_params.each { |quota| llm_model.llm_quotas.build(quota) } if quota_params
if llm_model.save
llm_model.toggle_companion_user
log_llm_model_creation(llm_model)
render json: LlmModelSerializer.new(llm_model), status: :created
else
render_json_error llm_model
end
end
def update
llm_model = LlmModel.find(params[:id])
# Capture initial state for logging
initial_attributes = llm_model.attributes.dup
initial_quotas = llm_model.llm_quotas.map(&:attributes)
if params[:ai_llm].key?(:llm_quotas)
if quota_params
existing_quota_group_ids = llm_model.llm_quotas.pluck(:group_id)
new_quota_group_ids = quota_params.map { |q| q[:group_id] }
llm_model
.llm_quotas
.where(group_id: existing_quota_group_ids - new_quota_group_ids)
.destroy_all
quota_params.each do |quota_param|
quota = llm_model.llm_quotas.find_or_initialize_by(group_id: quota_param[:group_id])
quota.update!(quota_param)
end
else
llm_model.llm_quotas.destroy_all
end
end
if llm_model.seeded?
return render_json_error(I18n.t("discourse_ai.llm.cannot_edit_builtin"), status: 403)
end
if llm_model.update(ai_llm_params(updating: llm_model))
llm_model.toggle_companion_user
log_llm_model_update(llm_model, initial_attributes, initial_quotas)
render json: LlmModelSerializer.new(llm_model)
else
render_json_error llm_model
end
end
def destroy
llm_model = LlmModel.find(params[:id])
if llm_model.seeded?
return render_json_error(I18n.t("discourse_ai.llm.cannot_delete_builtin"), status: 403)
end
in_use_by = DiscourseAi::Configuration::LlmValidator.new.modules_using(llm_model)
if !in_use_by.empty?
return(
render_json_error(
I18n.t(
"discourse_ai.llm.delete_failed",
settings: in_use_by.join(", "),
count: in_use_by.length,
),
status: 409,
)
)
end
# Capture model details for logging before destruction
model_details = {
model_id: llm_model.id,
display_name: llm_model.display_name,
name: llm_model.name,
provider: llm_model.provider,
}
# Clean up companion users
llm_model.enabled_chat_bot = false
llm_model.toggle_companion_user
if llm_model.destroy
log_llm_model_deletion(model_details)
head :no_content
else
render_json_error llm_model
end
end
def test
RateLimiter.new(current_user, "llm_test_#{current_user.id}", 3, 1.minute).performed!
llm_model = LlmModel.new(ai_llm_params)
DiscourseAi::Configuration::LlmValidator.new.run_test(llm_model)
render json: { success: true }
rescue DiscourseAi::Completions::Endpoints::Base::CompletionFailed => e
render json: { success: false, error: e.message }
end
private
def quota_params
if params[:ai_llm][:llm_quotas].present?
params[:ai_llm][:llm_quotas].map do |quota|
mapped = {}
mapped[:group_id] = quota[:group_id].to_i
mapped[:max_tokens] = quota[:max_tokens].to_i if quota[:max_tokens].present?
mapped[:max_usages] = quota[:max_usages].to_i if quota[:max_usages].present?
mapped[:duration_seconds] = quota[:duration_seconds].to_i
mapped
end
end
end
def ai_llm_params(updating: nil)
return {} if params[:ai_llm].blank?
permitted =
params.require(:ai_llm).permit(
:display_name,
:name,
:provider,
:tokenizer,
:max_prompt_tokens,
:max_output_tokens,
:api_key,
:enabled_chat_bot,
:vision_enabled,
:input_cost,
:cached_input_cost,
:output_cost,
)
provider = updating ? updating.provider : permitted[:provider]
permit_url = provider != LlmModel::BEDROCK_PROVIDER_NAME
new_url = params.dig(:ai_llm, :url)
permitted[:url] = new_url if permit_url && new_url
extra_field_names = LlmModel.provider_params.dig(provider&.to_sym)
if extra_field_names.present?
received_prov_params =
params.dig(:ai_llm, :provider_params)&.slice(*extra_field_names.keys)
if received_prov_params.present?
received_prov_params.each do |pname, value|
if extra_field_names[pname.to_sym] == :checkbox
received_prov_params[pname] = ActiveModel::Type::Boolean.new.cast(value)
end
end
permitted[:provider_params] = received_prov_params.permit!
end
end
permitted
end
def ai_llm_logger_fields
{
display_name: {
},
name: {
},
provider: {
},
tokenizer: {
},
url: {
},
max_prompt_tokens: {
},
max_output_tokens: {
},
enabled_chat_bot: {
},
vision_enabled: {
},
api_key: {
type: :sensitive,
},
input_cost: {
},
output_cost: {
},
# JSON fields should be tracked as simple changes
json_fields: [:provider_params],
}
end
def log_llm_model_creation(llm_model)
logger = DiscourseAi::Utils::AiStaffActionLogger.new(current_user)
entity_details = { model_id: llm_model.id, subject: llm_model.display_name }
# Add quota information as a special case
if llm_model.llm_quotas.any?
entity_details[:quotas] = llm_model
.llm_quotas
.map do |quota|
"Group #{quota.group_id}: #{quota.max_tokens} tokens, #{quota.max_usages} usages, #{quota.duration_seconds}s"
end
.join("; ")
end
logger.log_creation("llm_model", llm_model, ai_llm_logger_fields, entity_details)
end
def log_llm_model_update(llm_model, initial_attributes, initial_quotas)
logger = DiscourseAi::Utils::AiStaffActionLogger.new(current_user)
entity_details = { model_id: llm_model.id, subject: llm_model.display_name }
# Track quota changes separately as they're a special case
current_quotas = llm_model.llm_quotas.reload.map(&:attributes)
if initial_quotas != current_quotas
initial_quota_summary =
initial_quotas
.map { |q| "Group #{q["group_id"]}: #{q["max_tokens"]} tokens" }
.join("; ")
current_quota_summary =
current_quotas
.map { |q| "Group #{q["group_id"]}: #{q["max_tokens"]} tokens" }
.join("; ")
entity_details[:quotas_changed] = true
entity_details[:quotas] = "#{initial_quota_summary}#{current_quota_summary}"
end
logger.log_update(
"llm_model",
llm_model,
initial_attributes,
ai_llm_logger_fields,
entity_details,
)
end
def log_llm_model_deletion(model_details)
logger = DiscourseAi::Utils::AiStaffActionLogger.new(current_user)
model_details[:subject] = model_details[:display_name]
logger.log_deletion("llm_model", model_details)
end
end
end
end