discourse-ai/lib/automation/report_runner.rb

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# frozen_string_literal: true
module DiscourseAi
module Automation
class ReportRunner
def self.default_instructions
# not localizing for now cause non English LLM will require
# a fair bit of experimentation
<<~TEXT
Generate report:
## Report Guidelines:
- Length & Style: Aim for 12 dense paragraphs in a narrative style, focusing on internal forum discussions.
- Accuracy: Only include verified information with no embellishments.
- Sourcing: ALWAYS Back statements with links to forum discussions.
- Markdown Usage: Enhance readability with **bold**, *italic*, and > quotes.
- Linking: Use `#{Discourse.base_url}/t/-/TOPIC_ID/POST_NUMBER` for direct references.
- User Mentions: Reference users with @USERNAME
- Add many topic links: strive to link to at least 30 topics in the report. Topic Id is meaningless to end users if you need to throw in a link use [ref](...) or better still just embed it into the [sentence](...)
- Categories and tags: use the format #TAG and #CATEGORY to denote tags and categories
## Structure:
- Key statistics: Specify date range, call out important stats like number of new topics and posts
- Overview: Briefly state trends within period.
- Highlighted content: 5 paragaraphs highlighting important topics people should know about. If possible have each paragraph link to multiple related topics.
- Key insights and trends linking to a selection of posts that back them
TEXT
end
def self.run!(**args)
new(**args).run!
end
def initialize(
sender_username:,
model:,
sample_size:,
instructions:,
tokens_per_post:,
days:,
offset:,
receivers: nil,
topic_id: nil,
title: nil,
category_ids: nil,
tags: nil,
priority_group_id: nil,
allow_secure_categories: false,
debug_mode: false
)
@sender = User.find_by(username: sender_username)
@receivers = User.where(username: receivers)
@email_receivers = receivers&.filter { |r| r.include? "@" }
@title =
if title.present?
title
else
I18n.t("discourse_automation.llm_report.title")
end
@model = model
@llm = DiscourseAi::Completions::Llm.proxy(model)
@category_ids = category_ids
@tags = tags
@allow_secure_categories = allow_secure_categories
@debug_mode = debug_mode
@sample_size = sample_size.to_i < 10 ? 10 : sample_size.to_i
@instructions = instructions
@days = days.to_i
@offset = offset.to_i
@priority_group_id = priority_group_id
@tokens_per_post = tokens_per_post.to_i
@topic_id = topic_id.presence&.to_i
if !@topic_id && !@receivers.present? && !@email_receivers.present?
raise ArgumentError, "Must specify topic_id or receivers"
end
end
def run!
start_date = (@offset + @days).days.ago
end_date = start_date + @days.days
title =
@title.gsub(
"%DATE%",
start_date.strftime("%Y-%m-%d") + " - " + end_date.strftime("%Y-%m-%d"),
)
prioritized_group_ids = [@priority_group_id] if @priority_group_id.present?
context =
DiscourseAi::Automation::ReportContextGenerator.generate(
start_date: start_date,
duration: @days.days,
max_posts: @sample_size,
tags: @tags,
category_ids: @category_ids,
prioritized_group_ids: prioritized_group_ids,
allow_secure_categories: @allow_secure_categories,
tokens_per_post: @tokens_per_post,
tokenizer: @llm.tokenizer,
)
input = <<~INPUT
#{@instructions}
<context>
#{context}
</context>
#{@instructions}
INPUT
prompt = {
insts: "You are a helpful bot specializing in summarizing activity on Discourse sites",
input: input,
final_insts: "Here is the report I generated for you",
params: {
@model => {
temperature: 0,
},
},
}
result = +""
puts if Rails.env.development? && @debug_mode
@llm.completion!(prompt, Discourse.system_user) do |response|
print response if Rails.env.development? && @debug_mode
result << response
end
receiver_usernames = @receivers.map(&:username).join(",")
if @topic_id
PostCreator.create!(@sender, raw: result, topic_id: @topic_id, skip_validations: true)
# no debug mode for topics, it is too noisy
end
if receiver_usernames.present?
post =
PostCreator.create!(
@sender,
raw: result,
title: title,
archetype: Archetype.private_message,
target_usernames: receiver_usernames,
skip_validations: true,
)
if @debug_mode
input = input.split("\n").map { |line| " #{line}" }.join("\n")
raw = <<~RAW
```
tokens: #{@llm.tokenizer.tokenize(input).length}
start_date: #{start_date},
duration: #{@days.days},
max_posts: #{@sample_size},
tags: #{@tags},
category_ids: #{@category_ids},
priority_group: #{@priority_group_id}
LLM context was:
```
#{input}
RAW
PostCreator.create!(@sender, raw: raw, topic_id: post.topic_id, skip_validations: true)
end
end
if @email_receivers.present?
@email_receivers.each do |to_address|
Email::Sender.new(
::AiReportMailer.send_report(to_address, subject: title, body: result),
:ai_report,
).send
end
end
end
end
end
end