feat: move caching logic to label_suggestion
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@@ -7,17 +7,11 @@ class Captain::BaseEditorService
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# 120000 * 4 = 480,000 characters (rounding off downwards to 400,000 to be safe)
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TOKEN_LIMIT = 400_000
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GPT_MODEL = Llm::Config::DEFAULT_MODEL
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CACHEABLE_EVENTS = [].freeze
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pattr_initialize [:account!, :event!]
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def perform
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return value_from_cache if value_from_cache.present?
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response = send("#{event_name}_message")
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save_to_cache(response) if response.present?
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response
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send("#{event_name}_message")
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end
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private
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@@ -26,52 +20,10 @@ class Captain::BaseEditorService
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event['name']
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end
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def cache_key
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return nil unless event_is_cacheable?
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return nil unless conversation
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# since the value from cache depends on the conversation last_activity_at, it will always be fresh
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format(::Redis::Alfred::OPENAI_CONVERSATION_KEY, event_name: event_name, conversation_id: conversation.id,
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updated_at: conversation.last_activity_at.to_i)
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end
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def value_from_cache
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return nil unless event_is_cacheable?
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return nil if cache_key.blank?
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deserialize_cached_value(Redis::Alfred.get(cache_key))
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end
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def deserialize_cached_value(value)
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return nil if value.blank?
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JSON.parse(value, symbolize_names: true)
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rescue JSON::ParserError
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# If json parse failed, returning the value as is will fail too
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# since we access the keys as symbols down the line
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# So it's best to return nil
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nil
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end
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def save_to_cache(response)
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return nil unless event_is_cacheable?
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# Serialize to JSON
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# This makes parsing easy when response is a hash
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Redis::Alfred.setex(cache_key, response.to_json)
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end
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def conversation
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@conversation ||= account.conversations.find_by(display_id: event['data']['conversation_display_id'])
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end
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def event_is_cacheable?
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# self.class::CACHEABLE_EVENTS is way to access CACHEABLE_EVENTS defined in the class hierarchy of the current object.
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# This ensures that if CACHEABLE_EVENTS is updated elsewhere in it's ancestors, we access the latest value.
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self.class::CACHEABLE_EVENTS.include?(event_name)
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end
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def api_base
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endpoint = InstallationConfig.find_by(name: 'CAPTAIN_OPEN_AI_ENDPOINT')&.value.presence || 'https://api.openai.com/'
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endpoint = endpoint.chomp('/')
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@@ -1,10 +1,14 @@
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class Captain::LabelSuggestionService < Captain::BaseEditorService
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CACHEABLE_EVENTS = %w[label_suggestion].freeze
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def label_suggestion_message
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# Check cache first
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cached_response = read_from_cache
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return cached_response if cached_response.present?
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# Build content
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content = labels_with_messages
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return nil if content.blank?
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# Make API call
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response = make_api_call(
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model: GPT_MODEL, # TODO: Use separate model for label suggestion
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messages: [
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@@ -14,13 +18,41 @@ class Captain::LabelSuggestionService < Captain::BaseEditorService
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)
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return response if response[:error].present?
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# LLMs are not deterministic - sometimes response includes "Labels:" prefix
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# TODO: Fix with better prompt
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{ message: response[:message] ? response[:message].gsub(/^(label|labels):/i, '') : '' }
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# Clean up response
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result = { message: response[:message] ? response[:message].gsub(/^(label|labels):/i, '') : '' }
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# Cache successful result
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write_to_cache(result)
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result
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end
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private
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def cache_key
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return nil unless conversation
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format(
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::Redis::Alfred::OPENAI_CONVERSATION_KEY,
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event_name: 'label_suggestion',
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conversation_id: conversation.id,
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updated_at: conversation.last_activity_at.to_i
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)
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end
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def read_from_cache
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return nil unless cache_key
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cached = Redis::Alfred.get(cache_key)
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JSON.parse(cached, symbolize_names: true) if cached.present?
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rescue JSON::ParserError
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nil
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end
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def write_to_cache(response)
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Redis::Alfred.setex(cache_key, response.to_json) if cache_key
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end
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def labels_with_messages
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return nil unless valid_conversation?(conversation)
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