/** * FacilitatorViewAdapter — deterministic projection of the reasoning graph * into a concise, human-facing facilitator view (Version C). * * This adapter is pure and testable. It receives a prepared view model from * ReasoningWorkspace and returns a structured display model with up to four * primary sections: * * 1. What we know — supported observations, resolved state nodes * 2. Still investigating — unresolved unknowns, active unknown context * 3. Possible explanations — assumptions and tentative causal claims * 4. Quiet reasoning summary — secondary counts from the same graph * * All filtering, deduplication and ranking is deterministic and uses only * existing graph fields. No new backend data or API contracts are required. */ /* ── Normalisation helpers ─────────────────────────────────────── */ /** * Normalise a string for deduplication comparison. * Lowercase, trim, remove punctuation, collapse whitespace. */ function normaliseText(text) { if (!text || typeof text !== "string") return ""; return text .toLowerCase() .replace(/[^\w\s]/g, "") .replace(/\s+/g, " ") .trim(); } /** * Remove repeated boilerplate prefixes that add no meaning. */ function stripBoilerplate(text) { if (!text || typeof text !== "string") return text; const result = text.replace(/^need evidence about\s*/i, "").trim(); return result || null; } /** * Determine whether text is too long to scan usefully. */ function isTooLong(text, maxChars) { if (!text) return false; if (maxChars === undefined) maxChars = 280; return text.length > maxChars; } /* ── Filtering helpers ─────────────────────────────────────────── */ // Patterns that flag content as likely technical or boilerplate summary text. const TECHNICAL_SUMMARY_PATTERNS = [ /\bnodes?\s*[:\d]/i, /\bedges?\s*[:\d]/i, /\bsorted\s*/i, /by_kind/i, /\b(?:node|edge|unknown|state)\s+count/i, ]; /** * Decide whether a raw graph text item should be included in the panel. * Returns { included, displayText, reason } where reason is null when accepted. */ function filterItem(raw) { const label = raw.label; const description = raw.description; const kind = raw.kind; // Extract display text — prefer description if it adds beyond label let text = description || label; if (!text || typeof text !== "string") return { included: false, reason: "empty" }; const trimmed = text.trim(); if (!trimmed) return { included: false, reason: "empty" }; // Depriorise items that are purely technical summaries for (var i = 0; i < TECHNICAL_SUMMARY_PATTERNS.length; i++) { if (TECHNICAL_SUMMARY_PATTERNS[i].test(trimmed)) return { included: false, reason: "technical" }; } // Skip internal IDs — items whose text is just an ID or contains only one if (/^[a-z0-9-]{1,40}$/i.test(trimmed) && trimmed.length < 60) { return { included: false, reason: "internal-id" }; } // Depriorise items that are too long to scan usefully. // The adapter does not synthesise rewritten claims from verbose text. if (isTooLong(trimmed)) return { included: false, reason: "too-long" }; return { included: true, displayText: trimmed, sourceKind: kind }; } /* ── Node collection helpers ───────────────────────────────────── */ /** * Determine whether a node is resolved. */ function isResolved(node, resolvedIds) { return resolvedIds.has(node.id) || node.status === "resolved"; } /** * Determine whether this node is the active unknown target. */ function isActiveUnknown(node, activeUnknownNodeId) { return node.id === activeUnknownNodeId; } /* ── Core adapter function ─────────────────────────────────────── */ /** * Build a Version C facilitator view model from graph data. * * @param {Object} params * @param {Array} params.nodes — graph nodes * @param {Set} params.resolvedIds — resolved node IDs * @param {string|null} params.activeUnknownNodeId — ID of the active unknown * @param {Array} [params.edges=[]] — graph edges * @param {Object|null} [params.selectedQuestion=null] — current question object * @returns {Object} viewModel with sections: known, stillInvestigating, possibleExplanations, summaryCounts */ export function buildFacilitatorViewModel(_ref) { var nodes = _ref.nodes; var resolvedIds = _ref.resolvedIds; var activeUnknownNodeId = _ref.activeUnknownNodeId; var edges = _ref.edges; var selectedQuestion = _ref.selectedQuestion; if (!nodes) nodes = []; if (!resolvedIds) resolvedIds = new Set(); if (activeUnknownNodeId === undefined || activeUnknownNodeId === null) activeUnknownNodeId = null; if (!edges) edges = []; if (!selectedQuestion) selectedQuestion = null; // ── Phase 1: Categorise all nodes ─────────────────────────── var known = []; var stillInvestigating = []; var possibleExplanations = []; for (var _i = 0; _i < nodes.length; _i++) { var node = nodes[_i]; var resolved = isResolved(node, resolvedIds); var displayResult = filterItem(node); if (!displayResult.included) continue; var entry = { text: displayResult.displayText, normalised: normaliseText(displayResult.displayText), kind: node.kind, confidence: node.confidence || null, isResolved: resolved, isActiveUnknown: isActiveUnknown(node, activeUnknownNodeId), evidenceIds: node.evidenceIds || [], relevance: node.relevance != null ? node.relevance : null, priority: node.priority != null ? node.priority : null, }; if (resolved) { known.push(entry); continue; } // Unresolved content routing by kind switch (node.kind) { case "unknown": stillInvestigating.push(entry); break; case "assumption": possibleExplanations.push(entry); break; case "state": case "metric": // Unresolved states/metrics with extra content go to investigating. if (node.description && node.description !== node.label) { stillInvestigating.push(entry); } else { known.push(entry); } break; case "observation": // Unresolved observations are uncertain — put in stillInvestigating. stillInvestigating.push(entry); break; case "conclusion": possibleExplanations.push(entry); break; default: // Unknown kind — treat as unresolved unknown for safety. stillInvestigating.push(entry); } } // ── Phase 2: Deduplicate by normalised text ───────────────── /** * Deduplicate entries within a single list. * First occurrence wins; if a later entry has a higher-priority kind, replace it. */ function deduplicate(entries) { var seen = {}; // normalised → first entry return entries.filter(function (entry) { var key = entry.normalised; if (!key || !seen.hasOwnProperty(key)) { seen[key] = entry; return true; } // If already seen, prefer the one with a more specific kind order: // observation > unknown > assumption > state > metric var priorityOrder = ["observation", "unknown", "assumption", "state", "metric"]; var existingKindIdx = priorityOrder.indexOf(seen[key].kind); var newKindIdx = priorityOrder.indexOf(entry.kind); if (newKindIdx < existingKindIdx) { seen[key] = entry; return true; // replace with this one } return false; // skip — earlier winner stays }); } // Apply deduplication within each section independently var knownDedup = deduplicate(known); var unknownDedup = deduplicate(stillInvestigating); var assumptionDedup = deduplicate(possibleExplanations); // Cross-deduplicate: if "known" and "stillInvestigating" share normalised text, // move the item to stillInvestigating (uncertainty wins). var knownFinal = knownDedup; var stillInvestigatingFinal = unknownDedup; if (knownDedup.length > 0 && unknownDedup.length > 0) { var knownTexts = {}; for (var _j = 0; _j < unknownDedup.length; _j++) { knownTexts[unknownDedup[_j].normalised] = true; } knownFinal = knownDedup.filter(function (e) { return !knownTexts[e.normalised]; }); } // ── Phase 3: Rank items within each section ───────────────── /** * Rank known items. * Order: supported/resolved > high-confidence > concise > connected to active > last added. */ function rankKnown(items) { var confidenceRank = {}; confidenceRank["high"] = 0; confidenceRank["medium"] = 1; confidenceRank["low"] = 2; confidenceRank["null"] = 3; return items.slice().sort(function (a, b) { // Known items are all resolved. Prefer observations first. if (a.kind === "observation" && b.kind !== "observation") return -1; if (b.kind === "observation" && a.kind !== "observation") return 1; // Then by confidence var ca = confidenceRank[a.confidence] != null ? confidenceRank[a.confidence] : 3; var cb = confidenceRank[b.confidence] != null ? confidenceRank[b.confidence] : 3; if (ca !== cb) return ca - cb; // Prefer concise items if (a.text.length !== b.text.length) return a.text.length - b.text.length; return 0; }); } /** * Rank still-investigating items. * Order: active unknown > selected-question target > structurally eligible/high-priority > concise > remaining. */ function rankUnknowns(items) { var sqText = selectedQuestion && selectedQuestion.question ? normaliseText(selectedQuestion.question) : null; return items.slice().sort(function (a, b) { // Active unknown always first if (a.isActiveUnknown && !b.isActiveUnknown) return -1; if (!a.isActiveUnknown && b.isActiveUnknown) return 1; // Selected-question target: match by normalised text if (sqText && a.normalised === sqText && b.normalised !== sqText) return -1; if (sqText && a.normalised !== sqText && b.normalised === sqText) return 1; // Explicit priority fields where available in the graph var pa = a.priority != null ? a.priority : null; var pb = b.priority != null ? b.priority : null; if (pa != null && pb != null && pa !== pb) return pa - pb; // Prefer items linked to supported observations (has evidenceIds) var ae = a.evidenceIds ? a.evidenceIds.length : 0; var be = b.evidenceIds ? b.evidenceIds.length : 0; if (ae > 0 && be === 0) return -1; if (be > 0 && ae === 0) return 1; // Prefer concise items (shorter labels are more scannable) if (a.text.length !== b.text.length) return a.text.length - b.text.length; return 0; }); } /** * Rank possible explanation items. * Order: assumptions linked to supported observations > related to active unknown > concise > remaining. * Fallback ordering is by length (concise first), then kind preference. */ function rankAssumptions(items) { return items.slice().sort(function (a, b) { // Prefer assumptions with evidence linkage var ae = a.evidenceIds ? a.evidenceIds.length : 0; var be = b.evidenceIds ? b.evidenceIds.length : 0; if (ae > be) return -1; if (be > ae) return 1; // Then by length (concise first) if (a.text.length !== b.text.length) return a.text.length - b.text.length; return 0; }); } var rankedKnown = rankKnown(knownFinal); var rankedUnknowns = rankUnknowns(stillInvestigatingFinal); var rankedAssumptions = rankAssumptions(assumptionDedup); // ── Phase 4: Apply display limits ──────────────────────────── var MAX_KNOWN = 4; var MAX_INVESTIGATING = 4; var MAX_EXPLANATIONS = 3; var knownDisplay = rankedKnown.slice(0, MAX_KNOWN); var investigatingDisplay = rankedUnknowns.slice(0, MAX_INVESTIGATING); var explanationsDisplay = rankedAssumptions.slice(0, MAX_EXPLANATIONS); // ── Phase 5: Build display model ───────────────────────────── function toItemDisplay(entry) { return entry.text; } // Determine section titles based on investigation state var hasUnresolvedUnknowns = investigatingDisplay.some(function (e) { return e.kind === "unknown"; }); var knownSectionTitle = "What we know"; var isTerminal = !selectedQuestion && nodes.length > 0; if (isTerminal) { knownSectionTitle = "What the evidence supports"; } var investigatingSectionTitle = "Still investigating"; if (isTerminal && hasUnresolvedUnknowns) { investigatingSectionTitle = "Remaining cautions"; } // ── Phase 6: Compute quiet summary counts ──────────────────── // Count all items from the graph (including resolved), displayed as plain-language labels. var totalObservations = 0; for (var _k = 0; _k < nodes.length; _k++) { if (nodes[_k].kind === "observation" && filterItem(nodes[_k]).included) { totalObservations++; } } var unresolvedUnknownsCount = 0; for (var _l = 0; _l < nodes.length; _l++) { if (nodes[_l].kind === "unknown" && !isResolved(nodes[_l], resolvedIds)) { unresolvedUnknownsCount++; } } var unresolvedAssumptionsCount = 0; for (var _m = 0; _m < nodes.length; _m++) { if (nodes[_m].kind === "assumption" && !isResolved(nodes[_m], resolvedIds)) { unresolvedAssumptionsCount++; } } var relationshipsCount = edges ? edges.length : 0; // Build plain-language label string — only include non-zero counts. var summaryParts = []; if (totalObservations > 0) { summaryParts.push(totalObservations + " observation" + (totalObservations !== 1 ? "s" : "")); } if (unresolvedUnknownsCount > 0) { summaryParts.push(unresolvedUnknownsCount + " open question" + (unresolvedUnknownsCount !== 1 ? "s" : "")); } if (unresolvedAssumptionsCount > 0) { summaryParts.push(unresolvedAssumptionsCount + " assumption" + (unresolvedAssumptionsCount !== 1 ? "s" : "")); } // ── Phase 7: Determine terminal framing for investigating section ── var investigatingSectionHasItems = false; if (!isTerminal) { investigatingSectionHasItems = investigatingDisplay.length > 0; } else { investigatingSectionHasItems = hasUnresolvedUnknowns || explanationsDisplay.length > 0; } return { known: { title: knownSectionTitle, items: knownDisplay.map(toItemDisplay), hasItems: knownDisplay.length > 0, }, investigating: { title: investigatingSectionTitle, items: investigatingDisplay.map(toItemDisplay), hasItems: investigatingSectionHasItems, // Flag for the component to know whether to omit this section entirely. shouldOmit: isTerminal && !hasUnresolvedUnknowns && explanationsDisplay.length === 0, }, explanations: { title: "Possible explanations", items: explanationsDisplay.map(function (entry) { return { text: entry.text, // Structural uncertainty label — never depends on colour. label: entry.evidenceIds && entry.evidenceIds.length > 0 ? "To be tested" : "Not yet established", }; }), hasItems: explanationsDisplay.length > 0, }, summary: { text: summaryParts.length > 0 ? summaryParts.join(" · ") : null, }, _meta: { isTerminal: isTerminal, hasUnresolvedUnknowns: hasUnresolvedUnknowns, totalObservations: totalObservations, unresolvedUnknownsCount: unresolvedUnknownsCount, unresolvedAssumptionsCount: unresolvedAssumptionsCount, }, }; } export default buildFacilitatorViewModel;