generated from coulomb/repo-seed
Bernd Worsch
0f505feb2d
Closes the long-range feedback loop: outcome signals now enrich the full
traceability chain and feed back into routing, triage, and AI proposals.
Schema (T01):
- outcome_correlations (CHECK correlation_type)
- pattern_performance_records
- adaptive_threshold_configs
- institutional_knowledge_entries (GIN tsvector FTS)
- learning_insights (CHECK insight_type)
- ALTER TABLE decision_records + requirement_candidates: outcome_summary JSONB
- AFTER INSERT trigger trg_enrich_lineage on outcome_signals
- contracts/core/ updated (outcome-summary-columns-v1, append-only addendum)
Correlation engine (T02):
- Application/Helper/CorrelationEngine.hs: pure annotation→outcome SQL
- Web/Controller/OutcomeCorrelations.hs: ComputeCorrelationsAction + index
Pattern performance (T03):
- Web/Controller/PatternPerformance.hs: ComputePatternPerformanceAction
Adaptive thresholds (T04):
- Web/Controller/AdaptiveThresholds.hs: CalibrateThresholdsAction
- Application/Helper/FrictionScore.hs: applyAdaptiveWeights
Institutional knowledge (T05):
- DistilDecisionAction in DecisionRecords controller
- Web/Controller/InstitutionalKnowledge.hs: QueryKnowledgeBaseAction
Lineage enrichment (T06):
- Web/Controller/LineageEnrichment.hs: EnrichLineageAction (batch backfill)
- enrich_lineage_on_outcome_batch() PL/pgSQL helper in migration
Learning dashboard (T07):
- Web/Controller/LearningDashboard.hs: 5-panel autoRefresh view
- "Learning" nav link in FrontController
API v2 learning endpoints (T08):
- GET /api/v2/outcome-correlations, /pattern-performance, /knowledge-base/{id}
- OpenAPI schemas: OutcomeCorrelation, PatternPerformanceRecord, InstitutionalKnowledgeEntry
GAAF scorecard + docs (T09):
- Core 3.8→3.9, Functional 3.6→3.8, overall 3.61→3.68
- CLAUDE.md: IHF v0.2 complete, no active workplan
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
59 lines
2.6 KiB
Haskell
59 lines
2.6 KiB
Haskell
module Web.Controller.OutcomeCorrelations where
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-- IHF Phase 12 — Platform Memory (IHUB-WP-0013 T02)
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import Web.Controller.Prelude
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import Web.View.OutcomeCorrelations.Index
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import Application.Helper.CorrelationEngine (computeAnnotationCorrelations)
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import Data.Aeson ((.=), object)
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instance Controller OutcomeCorrelationsController where
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beforeAction = ensureIsUser
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action OutcomeCorrelationsAction = do
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mHubFilter <- paramOrNothing @(Id Hub) "hubId"
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correlations <- case mHubFilter of
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Nothing -> query @OutcomeCorrelation
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|> orderByDesc #correlationScore
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|> fetch
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Just hid -> query @OutcomeCorrelation
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|> filterWhere (#hubId, hid)
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|> orderByDesc #correlationScore
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|> fetch
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hubs <- query @Hub |> orderByAsc #name |> fetch
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render IndexView { correlations, hubs, mHubFilter }
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action ComputeCorrelationsAction { hubId } = do
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rows <- liftIO $ computeAnnotationCorrelations hubId
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now <- getCurrentTime
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-- Upsert: delete existing rows for this hub then insert fresh
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deleteWhere @OutcomeCorrelation (#hubId, hubId)
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forM_ rows \(category, score, sampleCount) ->
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newRecord @OutcomeCorrelation
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|> set #hubId hubId
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|> set #annotationCategory category
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|> set #correlationType "annotation_predictor"
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|> set #correlationScore score
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|> set #sampleCount sampleCount
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|> set #computedAt now
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|> createRecord
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-- Generate LearningInsight for top-scoring category
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case rows of
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((topCat, topScore, _) : _) | topScore >= 0.4 ->
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newRecord @LearningInsight
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|> set #hubId hubId
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|> set #insightType "annotation_predictor"
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|> set #title ("Strong predictor: annotation category '" <> topCat <> "'")
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|> set #body ("Annotation category '" <> topCat <> "' shows a correlation score of "
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<> show topScore <> " with positive outcomes. Consider weighting this category "
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<> "higher in triage and routing decisions.")
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|> set #evidenceLinks (A.toJSON
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[object ["type" .= ("outcome_correlation" :: Text), "category" .= topCat]])
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|> createRecord
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>> pure ()
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_ -> pure ()
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setSuccessMessage ("Correlations computed: " <> show (length rows) <> " categories")
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redirectTo OutcomeCorrelationsAction
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