60 lines
2.0 KiB
Ruby
60 lines
2.0 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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# Objects inheriting from this class will get passed as a dependency to `DiscourseAi::Summarization::FoldContent`.
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# This collaborator knows how to source the content to summarize and the prompts used in the process,
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# one for summarizing a chunk and another for concatenating them if necessary.
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class Base
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def initialize(target)
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@target = target
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end
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attr_reader :target
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# The summary type differentiates instances of `AiSummary` pointing to a single target.
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# See the `summary_type` enum for available options.
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def type
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raise NotImplementedError
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end
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# @returns { Hash } - Content to summarize.
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#
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# This method returns a hash with the content to summarize and additional information.
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# The only mandatory key is `contents`, which must be an array of hashes with
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# the following structure:
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#
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# {
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# poster: A way to tell who write the content,
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# id: A number to signal order,
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# text: Text to summarize
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# }
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#
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# Additionally, you could add more context, which will be available in the prompt. e.g.:
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#
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# {
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# resource_path: "#{Discourse.base_path}/t/-/#{target.id}",
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# content_title: target.title,
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# contents: [...]
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# }
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#
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def targets_data
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raise NotImplementedError
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end
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# @returns { DiscourseAi::Completions::Prompt } - Prompt passed to the LLM when concatenating multiple chunks.
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def contatenation_prompt(_texts_to_summarize)
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raise NotImplementedError
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end
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# @returns { DiscourseAi::Completions::Prompt } - Prompt passed to the LLM on each chunk,
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# and when the whole content fits in one call.
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def summarize_single_prompt(_input, _opts)
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raise NotImplementedError
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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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