- 14 new Code node drivers implementing the pipeline stages: normalize
candidates, build context SQL, merge context + hash, decide AI need,
prepare AI prompt, validate AI response, AI-disabled fallback, reuse
cached classification, compute score, select top 10, prepare memory/
envio records, build message/digest, compare legacy x V2, debug panel.
- Consolidated the two context queries (followups/tasks) into a single
UNION ALL query tagged by tipo, and added a "Merge - Unir Shadow"
synchronization node before comparing legacy x V2: n8n does not
guarantee relative execution order between sibling branches that only
share an early common ancestor, so cross-branch data was only safe to
read by name once a real Merge node forced both to complete first.
- Reordered score calculation before the memory upsert (score is needed
in the persisted row) and turned both Data Table writes into
side-effect branches that fan out from the main path instead of
gating it, so the Data Table node's own output shape never needs to
carry the full pipeline state forward.
- Fixed a duplicate-identifier bug in the build script: score.js's
require('./cooldown') survived inlining and collided with
cooldown.js's own computeCooldown function in the same Code node
scope. build-workflow.js now strips inter-module requires when
inlining, and a new test syntax-checks every generated jsCode string
plus asserts no leftover require() calls.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
28 lines
1.1 KiB
JavaScript
28 lines
1.1 KiB
JavaScript
const items = $input.all().map((item) => item.json);
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const ONE_DAY = 24 * 60 * 60 * 1000;
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return items.map((data) => {
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const cls = data.aiClassification.classification;
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let nextReviewAt = null;
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if (cls === 'WAITING_REQUESTER' || cls === 'WAITING_VENDOR') nextReviewAt = data.now + 3 * ONE_DAY;
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else if (cls === 'SCHEDULED' || cls === 'NEEDS_COORDINATION') nextReviewAt = data.now + 2 * ONE_DAY;
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return {
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json: {
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ticket_id: data.ticket.id,
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last_analyzed_at: data.contextChanged ? data.now : (data.memory ? data.memory.last_analyzed_at : data.now),
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last_context_hash: data.contextHash,
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last_meaningful_change_at: data.contextChanged ? data.now : (data.memory ? data.memory.last_meaningful_change_at : data.now),
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ai_classification: cls,
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ai_confidence: data.aiClassification.confidence,
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blocker_type: data.aiClassification.blocker_type,
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waiting_for: data.aiClassification.waiting_for,
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next_action: data.aiClassification.next_action,
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next_review_at: nextReviewAt,
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last_score: data.scoreResult.score,
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last_ai_reason: data.aiClassification.reason,
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updated_at: data.now,
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},
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};
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});
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