5874029a03
Real-world observation: OpenAI embedding API takes 200-400ms typical, plus pgvector query overhead, the 500ms budget was being exceeded frequently, silently dropping memory recall. Agent typing delay is already 2-15s humanized, so a 2s recall budget is well within UX tolerance and gives ~4-5x margin over typical embedding latency. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
53 lines
1.7 KiB
Ruby
53 lines
1.7 KiB
Ruby
class Captain::ContactMemories::RecallService
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TIMEOUT_SECONDS = 2.0
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DEFAULT_TOP_K = 5
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def initialize(contact:, query_text:, unit_id: nil, top_k: DEFAULT_TOP_K)
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@contact = contact
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@query_text = query_text
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@unit_id = unit_id
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@top_k = top_k
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end
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def call
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return [] if @contact.blank? || @query_text.blank?
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# NOTE: Timeout.timeout is used here as a hard cap on both embedding API + DB query.
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# It has known hazards (async exception, can't cleanly interrupt C-level I/O, can
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# corrupt connection state) — tradeoff accepted because this service is non-critical:
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# any failure returns [] and the agent degrades gracefully without memory. Phase 6
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# will consider refactoring the DB portion to Postgres statement_timeout for safer
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# cancellation.
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Timeout.timeout(TIMEOUT_SECONDS) do
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query_embedding = Captain::Llm::EmbeddingService.new(account_id: @contact.account_id).get_embedding(@query_text)
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return [] if query_embedding.blank?
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nearest_memories(query_embedding)
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end
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rescue StandardError => e
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log_failure(e)
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[]
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end
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private
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def nearest_memories(query_embedding)
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Captain::ContactMemory
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.active
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.for_contact(@contact.id)
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.scope_compatible(@unit_id)
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.where.not(embedding: nil)
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.nearest_neighbors(:embedding, query_embedding, distance: 'cosine')
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.limit(@top_k)
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.to_a
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end
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def log_failure(error)
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Rails.logger.warn(
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"[ContactMemory::RecallService] #{error.class}: #{error.message} " \
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"(contact_id=#{@contact&.id} account_id=#{@contact&.account_id})"
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)
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Rails.logger.warn(error.backtrace.first(5).join("\n")) unless error.is_a?(Timeout::Error)
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end
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end
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