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2be96e2098
Previously, sentiment and emotion classification only used configured classification model endpoints, which blocked sites that could not run those models. This change lets admins choose agent-backed LLM classifiers for sentiment and emotion while storing results under stable model keys so LLM changes do not force historic reclassification.
51 lines
1.9 KiB
Ruby
Vendored
51 lines
1.9 KiB
Ruby
Vendored
# frozen_string_literal: true
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module DiscourseAi
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module Agents
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class SentimentClassifier < Agent
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def self.default_enabled
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false
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end
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def system_prompt
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<<~PROMPT
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You classify Discourse forum posts by sentiment.
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Return a probability score for each sentiment label:
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- negative
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- neutral
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- positive
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Each score must be a float from 0 to 1. The scores must sum to 1.0. Higher scores mean the label is more applicable to the post.
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Classify the author's sentiment, not quoted text or replies from other people. Ignore markup, links, signatures, logs, and code blocks unless they clearly express sentiment. Interpret sarcasm and irony by their implied meaning.
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Use neutral as the highest score for factual announcements, questions, or technical debugging with little affect. For mixed sentiment, distribute probability across labels instead of forcing a single winner.
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Use the full post content provided by the user. Reply with valid JSON only. Do not include explanations, confidence fields, or any keys except the sentiment labels.
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PROMPT
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end
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def response_format
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%w[negative neutral positive].map { |label| { "key" => label, "type" => "number" } }
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end
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def examples
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[
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[
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"This release is fantastic. The new editor is fast, polished, and much easier to use.",
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{ negative: 0.02, neutral: 0.08, positive: 0.9 }.to_json,
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],
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[
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"The upgrade broke search for our team and the workaround did not help.",
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{ negative: 0.88, neutral: 0.09, positive: 0.03 }.to_json,
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],
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[
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"Version 3.2 is now available in the admin updates page.",
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{ negative: 0.03, neutral: 0.92, positive: 0.05 }.to_json,
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],
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]
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end
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end
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end
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end
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