feat(confidence-engine): deriveInvestigationLearning deterministic learning projection from canonical provenance

Adds deriveInvestigationLearning to episode-preparation.js: pure function that maps resolvedNodeIds + Contributions + Findings to investigation learning propositions, preserving node.label question text and contribution-provenance ordering. Owning tests prove: correct provenance, cross-question isolation, eligibility rule (null/agree eligible; not_quite/not_relevant excluded), unresolved exclusion, no-eligible-material omission, text preservation, and provenance authority over originatingTargetNodeId shortcut.
2026-10-02 08:18:19 +01:00
parent 30648ca5b3
commit b448f522f6
2 changed files with 264 additions and 2 deletions
@@ -15,9 +15,9 @@

 // ── Eligibility constants ──────────────────────────────────

-const ELIGIBLE_DISPOSITIONS = new Set([null, "agree"]);
+export const ELIGIBLE_DISPOSITIONS = new Set([null, "agree"]);

-const EXCLUDED_DISPOSITIONS = new Set(["not_relevant", "not_quite"]);
+export const EXCLUDED_DISPOSITIONS = new Set(["not_relevant", "not_quite"]);

 // ── Public API ─────────────────────────────────────────────

@@ -104,3 +104,79 @@ export function prepareCompletedEpisode({ situationGraph, targetNodeId, contribu
    excludedFindingProvenance,
  };
 }
+
+// ── Investigation learning projection ────────────────────────
+
+/**
+ * Derive deterministic learning from a completed investigation episode.
+ *
+ * @param {Object} params
+ * @param {Object} params.situationGraph - Authoritative SituationGraph (read-only)
+ * @param {Array<Object>} params.contributions - All persisted Contributions
+ * @param {Array<Object>} params.findings - All canonical Findings
+ * @returns {Array<Object>} investigationLearning — ordered by resolvedNodeIds, then Contribution.sequence
+ */
+export function deriveInvestigationLearning({ situationGraph, contributions = [], findings = [] }) {
+  const resolvedNodeIds = situationGraph?.resolvedNodeIds ?? [];
+
+  // Build lookup from node id to node (for question text)
+  const nodeById = new Map();
+  for (const node of situationGraph?.nodes ?? []) {
+    if (node?.id) {
+      nodeById.set(node.id, node);
+    }
+  }
+
+  const result = [];
+
+  for (const nodeId of resolvedNodeIds) {
+    const node = nodeById.get(nodeId);
+    if (!node) continue;
+
+    // Collect scoped contributions ordered by sequence
+    const scopedContribs = [];
+    for (const contrib of contributions) {
+      if (contrib?.targetNodeId === nodeId) {
+        scopedContribs.push(contrib);
+      }
+    }
+
+    const orderedContribs = scopedContribs
+      .slice()
+      .sort(function(a, b) {
+        var aSeq = a?.sequence != null ? a.sequence : Infinity;
+        var bSeq = b?.sequence != null ? b.sequence : Infinity;
+        return aSeq - bSeq;
+      });
+
+    // Build contribution ID set for provenance chain
+    const contribIds = new Set(orderedContribs.map(function(c) { return c.id; }));
+
+    // Collect eligible findings from scoped contributions via Contribution provenance only
+    var propositions = [];
+    for (var i = 0; i < orderedContribs.length; i++) {
+      var contrib = orderedContribs[i];
+      for (var j = 0; j < findings.length; j++) {
+        var finding = findings[j];
+        if (finding?.contributionId === contrib.id && ELIGIBLE_DISPOSITIONS.has(finding?.userDisposition ?? null)) {
+          propositions.push({
+            findingId: finding.id ?? null,
+            proposition: finding.proposition ?? "",
+            userDisposition: finding.userDisposition ?? null,
+          });
+        }
+      }
+    }
+
+    // Only include questions that produced at least one eligible proposition
+    if (propositions.length > 0) {
+      result.push({
+        nodeId: nodeId,
+        questionText: node.label ?? "",
+        propositions: propositions,
+      });
+    }
+  }
+
+  return result;
+}