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Previous to this change we relied on explicit loading for a files in Discourse AI. This had a few downsides: - Busywork whenever you add a file (an extra require relative) - We were not keeping to conventions internally ... some places were OpenAI others are OpenAi - Autoloader did not work which lead to lots of full application broken reloads when developing. This moves all of DiscourseAI into a Zeitwerk compatible structure. It also leaves some minimal amount of manual loading (automation - which is loading into an existing namespace that may or may not be there) To avoid needing /lib/discourse_ai/... we mount a namespace thus we are able to keep /lib pointed at ::DiscourseAi Various files were renamed to get around zeitwerk rules and minimize usage of custom inflections Though we can get custom inflections to work it is not worth it, will require a Discourse core patch which means we create a hard dependency.
151 lines
5.0 KiB
Ruby
151 lines
5.0 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Summarization
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module Strategies
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class FoldContent < ::Summarization::Base
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def initialize(completion_model)
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@completion_model = completion_model
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end
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attr_reader :completion_model
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delegate :correctly_configured?,
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:display_name,
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:configuration_hint,
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:model,
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to: :completion_model
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def summarize(content, user, &on_partial_blk)
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opts = content.except(:contents)
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llm = DiscourseAi::Completions::Llm.proxy(completion_model.model)
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chunks = split_into_chunks(llm.tokenizer, content[:contents])
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if chunks.length == 1
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{
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summary: summarize_single(llm, chunks.first[:summary], user, opts, &on_partial_blk),
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chunks: [],
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}
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else
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summaries = summarize_in_chunks(llm, chunks, user, opts)
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{
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summary:
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concatenate_summaries(
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llm,
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summaries.map { |s| s[:summary] },
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user,
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&on_partial_blk
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),
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chunks: summaries,
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}
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end
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end
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private
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def format_content_item(item)
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"(#{item[:id]} #{item[:poster]} said: #{item[:text]} "
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end
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def split_into_chunks(tokenizer, contents)
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section = { ids: [], summary: "" }
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chunks =
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contents.reduce([]) do |sections, item|
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new_content = format_content_item(item)
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if tokenizer.can_expand_tokens?(
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section[:summary],
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new_content,
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completion_model.available_tokens,
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)
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section[:summary] += new_content
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section[:ids] << item[:id]
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else
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sections << section
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section = { ids: [item[:id]], summary: new_content }
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end
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sections
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end
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chunks << section if section[:summary].present?
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chunks
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end
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def summarize_single(llm, text, user, opts, &on_partial_blk)
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prompt = summarization_prompt(text, opts)
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llm.completion!(prompt, user, &on_partial_blk)
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end
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def summarize_in_chunks(llm, chunks, user, opts)
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chunks.map do |chunk|
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prompt = summarization_prompt(chunk[:summary], opts)
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prompt[:post_insts] = "Don't use more than 400 words for the summary."
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chunk[:summary] = llm.completion!(prompt, user)
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chunk
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end
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end
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def concatenate_summaries(llm, summaries, user, &on_partial_blk)
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prompt = summarization_prompt(summaries.join("\n"), {})
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prompt[:insts] = <<~TEXT
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You are a bot that can concatenate disjoint summaries, creating a cohesive narrative.
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Keep the resulting summary in the same language used in the text below.
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TEXT
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llm.completion!(prompt, user, &on_partial_blk)
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end
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def summarization_prompt(input, opts)
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insts = <<~TEXT
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You are a summarization bot that effectively summarize any text
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Your replies contain ONLY a summarized version of the text I provided and you, using the same language.
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You understand and generate Discourse forum Markdown.
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You format the response, including links, using Markdown.
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Your summaries are always a cohesive narrative in the form of one or multiple paragraphs.
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TEXT
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insts += <<~TEXT if opts[:resource_path]
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Each post is formatted as "<POST_NUMBER>) <USERNAME> <MESSAGE> "
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Try generating links as well the format is #{opts[:resource_path]}/<POST_NUMBER>
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For example, a link to the 3rd post in the topic would be [post 3](#{opts[:resource_path]}/3)
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TEXT
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insts += "The discussion title is: #{opts[:content_title]}.\n" if opts[:content_title]
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prompt = { insts: insts, input: <<~TEXT }
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Here is the a list of posts, inside <input></input> XML tags:
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<input>
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#{input}
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</input>
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TEXT
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if opts[:resource_path]
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prompt[:examples] = [
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[
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"<input>1) user1 said: I love Mondays 2) user2 said: I hate Mondays</input>",
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"Two users are sharing their feelings toward Mondays. [user1](#{opts[:resource_path]}/1) hates them, while [user2](#{opts[:resource_path]}/2) loves them.",
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],
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[
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"<input>3) usuario1: Amo los lunes 6) usuario2: Odio los lunes</input>",
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"Dos usuarios charlan sobre los lunes. [usuario1](#{opts[:resource_path]}/3) dice que los ama, mientras que [usuario2](#{opts[:resource_path]}/2) los odia.",
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],
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]
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end
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prompt
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end
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end
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end
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end
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end
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