{"version":3,"file":"zettelkasten-DezbXghl.mjs","names":["MAX_CHUNKS"],"sources":["../src/zettelkasten/explore.ts","../src/zettelkasten/surprise.ts","../src/zettelkasten/converse.ts","../src/zettelkasten/health.ts","../src/zettelkasten/suggest.ts","../src/zettelkasten/god-notes.ts","../src/zettelkasten/communities.ts"],"sourcesContent":["import type { StorageBackend } from \"../storage/interface.js\";\nimport { dirname } from \"node:path\";\n\nexport interface ExploreOptions {\n startNote: string;\n depth?: number;\n direction?: \"forward\" | \"backward\" | \"both\";\n mode?: \"sequential\" | \"associative\" | \"all\";\n}\n\nexport interface ExploreNode {\n path: string;\n title: string | null;\n depth: number;\n linkType: \"sequential\" | \"associative\";\n inbound: number;\n outbound: number;\n}\n\nexport interface ExploreResult {\n root: string;\n nodes: ExploreNode[];\n edges: Array<{ from: string; to: string; type: \"sequential\" | \"associative\" }>;\n branchingPoints: string[];\n maxDepthReached: boolean;\n}\n\nfunction classifyEdge(source: string, target: string): \"sequential\" | \"associative\" {\n return dirname(source) === dirname(target) ? \"sequential\" : \"associative\";\n}\n\nasync function resolveStart(backend: StorageBackend, startNote: string): Promise {\n // Try direct lookup first\n const files = await backend.getVaultFilesByPaths([startNote]);\n if (files.length > 0) return files[0].vaultPath;\n\n // Try alias lookup\n const alias = await backend.getVaultAlias(startNote);\n if (!alias) return null;\n\n const canonical = await backend.getVaultFilesByPaths([alias.canonicalPath]);\n return canonical.length > 0 ? canonical[0].vaultPath : null;\n}\n\nasync function getForwardNeighbors(backend: StorageBackend, path: string): Promise {\n const links = await backend.getLinksFromSource(path);\n return links.filter(l => l.targetPath !== null).map(l => l.targetPath as string);\n}\n\nasync function getBackwardNeighbors(backend: StorageBackend, path: string): Promise {\n const links = await backend.getLinksToTarget(path);\n return links.map(l => l.sourcePath);\n}\n\nasync function getFileInfo(\n backend: StorageBackend,\n path: string,\n): Promise<{ title: string | null; inbound: number; outbound: number }> {\n const [files, health] = await Promise.all([\n backend.getVaultFilesByPaths([path]),\n backend.getVaultHealth(path),\n ]);\n\n return {\n title: files[0]?.title ?? null,\n inbound: health?.inboundCount ?? 0,\n outbound: health?.outboundCount ?? 0,\n };\n}\n\n/**\n * Traverse the Zettelkasten link graph using BFS, following chains of thought\n * from a starting note up to a configurable depth.\n */\nexport async function zettelExplore(backend: StorageBackend, opts: ExploreOptions): Promise {\n const depth = Math.min(Math.max(opts.depth ?? 3, 1), 10);\n const direction = opts.direction ?? \"both\";\n const mode = opts.mode ?? \"all\";\n\n const root = await resolveStart(backend, opts.startNote);\n if (!root) {\n return {\n root: opts.startNote,\n nodes: [],\n edges: [],\n branchingPoints: [],\n maxDepthReached: false,\n };\n }\n\n const visited = new Set([root]);\n const nodes: ExploreNode[] = [];\n const edges: Array<{ from: string; to: string; type: \"sequential\" | \"associative\" }> = [];\n let maxDepthReached = false;\n\n const queue: Array<{ path: string; depth: number }> = [{ path: root, depth: 0 }];\n\n while (queue.length > 0) {\n const current = queue.shift()!;\n\n if (current.depth >= depth) {\n maxDepthReached = true;\n continue;\n }\n\n const neighbors: Array<{ neighbor: string; from: string; to: string }> = [];\n\n if (direction === \"forward\" || direction === \"both\") {\n for (const n of await getForwardNeighbors(backend, current.path)) {\n neighbors.push({ neighbor: n, from: current.path, to: n });\n }\n }\n\n if (direction === \"backward\" || direction === \"both\") {\n for (const n of await getBackwardNeighbors(backend, current.path)) {\n neighbors.push({ neighbor: n, from: n, to: current.path });\n }\n }\n\n for (const { neighbor, from, to } of neighbors) {\n const edgeType = classifyEdge(from, to);\n\n if (mode !== \"all\" && edgeType !== mode) {\n continue;\n }\n\n const alreadyHasEdge = edges.some((e) => e.from === from && e.to === to);\n if (!alreadyHasEdge) {\n edges.push({ from, to, type: edgeType });\n }\n\n if (!visited.has(neighbor)) {\n visited.add(neighbor);\n\n const info = await getFileInfo(backend, neighbor);\n nodes.push({\n path: neighbor,\n title: info.title,\n depth: current.depth + 1,\n linkType: edgeType,\n inbound: info.inbound,\n outbound: info.outbound,\n });\n\n queue.push({ path: neighbor, depth: current.depth + 1 });\n }\n }\n }\n\n const branchingPoints = nodes\n .filter((n) => n.outbound > 2)\n .map((n) => n.path);\n\n const rootInfo = await getFileInfo(backend, root);\n if (rootInfo.outbound > 2) {\n branchingPoints.unshift(root);\n }\n\n return { root, nodes, edges, branchingPoints, maxDepthReached };\n}\n","import type { StorageBackend } from \"../storage/interface.js\";\nimport {\n deserializeEmbedding,\n generateEmbedding,\n cosineSimilarity,\n} from \"../memory/embeddings.js\";\n\nexport interface SurpriseOptions {\n referencePath: string;\n vaultProjectId: number;\n limit?: number;\n minSimilarity?: number;\n minGraphDistance?: number;\n}\n\nexport interface SurpriseResult {\n path: string;\n title: string | null;\n cosineSimilarity: number;\n graphDistance: number;\n surpriseScore: number;\n sharedSnippet: string;\n}\n\nconst MAX_CHUNKS = 5000;\nconst BFS_HOP_CAP = 20;\n\nasync function getFileEmbeddings(\n backend: StorageBackend,\n projectId: number,\n): Promise> {\n const rows = await backend.getChunksWithEmbeddings(projectId, MAX_CHUNKS);\n\n const byPath = new Map();\n for (const row of rows) {\n const vec = deserializeEmbedding(row.embedding);\n const entry = byPath.get(row.path);\n if (!entry) {\n byPath.set(row.path, { sum: new Float32Array(vec), count: 1, text: row.text });\n } else {\n for (let i = 0; i < vec.length; i++) {\n entry.sum[i] += vec[i];\n }\n entry.count++;\n }\n }\n\n const result = new Map();\n for (const [path, { sum, count, text }] of byPath) {\n const avg = new Float32Array(sum.length);\n for (let i = 0; i < sum.length; i++) {\n avg[i] = sum[i] / count;\n }\n result.set(path, { embedding: avg, text });\n }\n return result;\n}\n\nasync function getReferenceEmbedding(\n backend: StorageBackend,\n projectId: number,\n path: string,\n): Promise<{ embedding: Float32Array; found: boolean }> {\n const rows = await backend.getChunksForPath(projectId, path);\n\n if (rows.length === 0) {\n return { embedding: new Float32Array(0), found: false };\n }\n\n const embRows = rows.filter(r => r.embedding !== null) as Array<{ text: string; embedding: Buffer }>;\n if (embRows.length === 0) {\n return { embedding: new Float32Array(0), found: false };\n }\n\n const dim = deserializeEmbedding(embRows[0].embedding).length;\n const sum = new Float32Array(dim);\n for (const row of embRows) {\n const vec = deserializeEmbedding(row.embedding);\n for (let i = 0; i < dim; i++) {\n sum[i] += vec[i];\n }\n }\n const avg = new Float32Array(dim);\n for (let i = 0; i < dim; i++) {\n avg[i] = sum[i] / embRows.length;\n }\n return { embedding: avg, found: true };\n}\n\nasync function bfsGraphDistance(backend: StorageBackend, source: string, target: string): Promise {\n if (source === target) return 0;\n\n const visited = new Set([source]);\n const queue: Array<{ path: string; hops: number }> = [{ path: source, hops: 0 }];\n\n while (queue.length > 0) {\n const { path, hops } = queue.shift()!;\n if (hops >= BFS_HOP_CAP) continue;\n\n const [forwardLinks, backwardLinks] = await Promise.all([\n backend.getLinksFromSource(path),\n backend.getLinksToTarget(path),\n ]);\n\n const neighbors: string[] = [\n ...forwardLinks.filter(l => l.targetPath !== null).map(l => l.targetPath as string),\n ...backwardLinks.map(l => l.sourcePath),\n ];\n\n for (const neighbor of neighbors) {\n if (neighbor === target) return hops + 1;\n if (!visited.has(neighbor)) {\n visited.add(neighbor);\n queue.push({ path: neighbor, hops: hops + 1 });\n }\n }\n }\n\n return Infinity;\n}\n\nfunction getBestChunkText(\n chunkRows: Array<{ text: string; embedding: Buffer | null }>,\n refEmbedding: Float32Array,\n): string {\n const rows = chunkRows.filter(r => r.embedding !== null) as Array<{ text: string; embedding: Buffer }>;\n if (rows.length === 0) return \"\";\n\n let bestText = rows[0].text;\n let bestSim = -Infinity;\n\n for (const row of rows) {\n const vec = deserializeEmbedding(row.embedding);\n const sim = cosineSimilarity(refEmbedding, vec);\n if (sim > bestSim) {\n bestSim = sim;\n bestText = row.text;\n }\n }\n\n return bestText.trim().slice(0, 200);\n}\n\n/**\n * Find notes that are semantically similar to a reference note but graph-distant —\n * revealing surprising conceptual connections across unrelated areas of the Zettelkasten.\n */\nexport async function zettelSurprise(\n backend: StorageBackend,\n opts: SurpriseOptions,\n): Promise {\n const limit = opts.limit ?? 10;\n const minSimilarity = opts.minSimilarity ?? 0.3;\n const minGraphDistance = opts.minGraphDistance ?? 3;\n\n let { embedding: refEmbedding, found } = await getReferenceEmbedding(\n backend,\n opts.vaultProjectId,\n opts.referencePath,\n );\n\n // Fall back to generating an embedding from the file title if no chunks exist\n if (!found) {\n const files = await backend.getVaultFilesByPaths([opts.referencePath]);\n const text = files[0]?.title ?? opts.referencePath;\n refEmbedding = await generateEmbedding(text, true);\n }\n\n const allFileEmbeddings = await getFileEmbeddings(backend, opts.vaultProjectId);\n\n // Remove the reference note itself from candidates\n allFileEmbeddings.delete(opts.referencePath);\n\n // First pass: filter by semantic similarity to avoid BFS on all nodes\n const semanticCandidates: Array<{ path: string; sim: number }> = [];\n for (const [path, { embedding }] of allFileEmbeddings) {\n const sim = cosineSimilarity(refEmbedding, embedding);\n if (sim >= minSimilarity) {\n semanticCandidates.push({ path, sim });\n }\n }\n\n // Compute graph distances for semantic candidates\n const results: SurpriseResult[] = [];\n\n for (const { path, sim } of semanticCandidates) {\n const graphDistance = await bfsGraphDistance(backend, opts.referencePath, path);\n\n const effectiveDistance = isFinite(graphDistance) ? graphDistance : BFS_HOP_CAP;\n if (effectiveDistance < minGraphDistance) continue;\n\n const files = await backend.getVaultFilesByPaths([path]);\n const chunkRows = await backend.getChunksForPath(opts.vaultProjectId, path, 20);\n\n const surpriseScore = sim * Math.log2(effectiveDistance + 1);\n const sharedSnippet = getBestChunkText(chunkRows, refEmbedding);\n\n results.push({\n path,\n title: files[0]?.title ?? null,\n cosineSimilarity: sim,\n graphDistance: isFinite(graphDistance) ? graphDistance : Infinity,\n surpriseScore,\n sharedSnippet,\n });\n }\n\n results.sort((a, b) => b.surpriseScore - a.surpriseScore);\n return results.slice(0, limit);\n}\n","import type { StorageBackend } from \"../storage/interface.js\";\nimport type { SearchResult } from \"../memory/search.js\";\nimport { generateEmbedding } from \"../memory/embeddings.js\";\n\nexport interface ConverseOptions {\n /** The user's question or topic to explore. */\n question: string;\n /** project_id for vault chunks in memory_chunks. */\n vaultProjectId: number;\n /** Graph expansion depth. Default 2. */\n depth?: number;\n /** Maximum number of relevant notes to return. Default 15. */\n limit?: number;\n}\n\nexport interface ConverseConnection {\n fromPath: string;\n toPath: string;\n /** Top-level folder of fromPath. */\n fromDomain: string;\n /** Top-level folder of toPath. */\n toDomain: string;\n /** Link count between these two notes (can be > 1). */\n strength: number;\n}\n\nexport interface ConverseResult {\n relevantNotes: Array<{\n path: string;\n title: string | null;\n snippet: string;\n score: number;\n domain: string;\n }>;\n /** Cross-domain connections found among the selected notes. */\n connections: ConverseConnection[];\n /** Unique domains involved across all selected notes. */\n domains: string[];\n /** AI-ready prompt combining notes + connections for insight generation. */\n synthesisPrompt: string;\n}\n\n// ---------------------------------------------------------------------------\n// Helpers\n// ---------------------------------------------------------------------------\n\n/** Extract the top-level folder from a vault path (first path segment). */\nfunction extractDomain(vaultPath: string): string {\n const slash = vaultPath.indexOf(\"/\");\n return slash === -1 ? vaultPath : vaultPath.slice(0, slash);\n}\n\n/**\n * Expand one level of graph neighbors for a set of paths.\n * Returns all outbound and inbound neighbor paths (excluding already-visited).\n */\nasync function expandNeighbors(backend: StorageBackend, paths: Set): Promise {\n if (paths.size === 0) return [];\n const pathList = Array.from(paths);\n\n const [forwardLinks, backwardLinks] = await Promise.all([\n backend.getVaultLinksFromPaths(pathList),\n Promise.all(pathList.map(p => backend.getLinksToTarget(p))),\n ]);\n\n const neighbors: string[] = [];\n for (const link of forwardLinks) {\n if (link.targetPath) neighbors.push(link.targetPath);\n }\n for (const linkList of backwardLinks) {\n for (const link of linkList) {\n neighbors.push(link.sourcePath);\n }\n }\n return neighbors;\n}\n\n/**\n * Hybrid search combining keyword + semantic results using the StorageBackend.\n */\nasync function hybridSearch(\n backend: StorageBackend,\n query: string,\n queryEmbedding: Float32Array,\n opts: { projectIds?: number[]; maxResults?: number },\n): Promise {\n const maxResults = opts.maxResults ?? 10;\n const kw = 0.5;\n const sw = 0.5;\n\n const [keywordResults, semanticResults] = await Promise.all([\n backend.searchKeyword(query, { ...opts, maxResults: 50 }),\n backend.searchSemantic(queryEmbedding, { ...opts, maxResults: 50 }),\n ]);\n\n if (keywordResults.length === 0 && semanticResults.length === 0) return [];\n\n const keyFor = (r: SearchResult) =>\n `${r.projectId}:${r.path}:${r.startLine}:${r.endLine}`;\n\n function minMaxNormalize(scores: number[]): number[] {\n const min = Math.min(...scores);\n const max = Math.max(...scores);\n const range = max - min;\n if (range === 0) return scores.map(() => 1.0);\n return scores.map(s => (s - min) / range);\n }\n\n const kwNorm = minMaxNormalize(keywordResults.map(r => r.score));\n const semNorm = minMaxNormalize(semanticResults.map(r => r.score));\n\n const combined = new Map();\n\n for (let i = 0; i < keywordResults.length; i++) {\n const r = keywordResults[i];\n const k = keyFor(r);\n combined.set(k, { ...r, combinedScore: kw * kwNorm[i] });\n }\n\n for (let i = 0; i < semanticResults.length; i++) {\n const r = semanticResults[i];\n const k = keyFor(r);\n const existing = combined.get(k);\n if (existing) {\n existing.combinedScore += sw * semNorm[i];\n } else {\n combined.set(k, { ...r, combinedScore: sw * semNorm[i] });\n }\n }\n\n const sorted = Array.from(combined.values())\n .sort((a, b) => b.combinedScore - a.combinedScore)\n .slice(0, maxResults);\n\n return sorted.map(r => ({ ...r, score: r.combinedScore }));\n}\n\n// ---------------------------------------------------------------------------\n// Main export\n// ---------------------------------------------------------------------------\n\n/**\n * Let the vault \"talk back\" — find notes relevant to a question, expand\n * through the link graph, identify cross-domain connections, and return a\n * structured result including a synthesis prompt for an AI to generate insights.\n */\nexport async function zettelConverse(\n backend: StorageBackend,\n opts: ConverseOptions,\n): Promise {\n const depth = Math.max(opts.depth ?? 2, 0);\n const limit = Math.max(opts.limit ?? 15, 1);\n const candidateLimit = 20;\n\n // ------------------------------------------------------------------\n // 1. Hybrid search: find top candidates via BM25 + semantic similarity\n // ------------------------------------------------------------------\n const queryEmbedding = await generateEmbedding(opts.question, true);\n\n const searchResults = await hybridSearch(\n backend,\n opts.question,\n queryEmbedding,\n {\n projectIds: [opts.vaultProjectId],\n maxResults: candidateLimit,\n },\n );\n\n // Map of path -> best score + snippet from search results\n const searchHits = new Map();\n for (const r of searchResults) {\n const existing = searchHits.get(r.path);\n if (!existing || r.score > existing.score) {\n searchHits.set(r.path, { score: r.score, snippet: r.snippet });\n }\n }\n\n // ------------------------------------------------------------------\n // 2. Graph expansion: BFS from each search result up to `depth` levels\n // ------------------------------------------------------------------\n const allPaths = new Set(searchHits.keys());\n let frontier = new Set(searchHits.keys());\n\n for (let d = 0; d < depth; d++) {\n const neighbors = await expandNeighbors(backend, frontier);\n const newFrontier = new Set();\n for (const n of neighbors) {\n if (!allPaths.has(n)) {\n allPaths.add(n);\n newFrontier.add(n);\n }\n }\n if (newFrontier.size === 0) break;\n frontier = newFrontier;\n }\n\n // ------------------------------------------------------------------\n // 3. Deduplicate + trim to limit\n // ------------------------------------------------------------------\n const searchRanked = Array.from(searchHits.entries())\n .sort((a, b) => b[1].score - a[1].score)\n .map(([path, info]) => ({ path, ...info, isSearchResult: true }));\n\n const neighborPaths = Array.from(allPaths).filter((p) => !searchHits.has(p));\n\n // Fetch health data for neighbor ranking\n const neighborHealthRows = await Promise.all(\n neighborPaths.map(p => backend.getVaultHealth(p))\n );\n const neighborRanked = neighborPaths\n .map((path, idx) => ({\n path,\n score: 0,\n snippet: \"\",\n inbound: neighborHealthRows[idx]?.inboundCount ?? 0,\n isSearchResult: false,\n }))\n .sort((a, b) => b.inbound - a.inbound);\n\n const budgetForNeighbors = Math.max(limit - searchRanked.length, 0);\n const selectedNeighbors = neighborRanked.slice(0, budgetForNeighbors);\n\n const selectedSearchPaths = searchRanked.slice(0, limit);\n const selectedPaths = new Set([\n ...selectedSearchPaths.map((r) => r.path),\n ...selectedNeighbors.map((r) => r.path),\n ]);\n\n // ------------------------------------------------------------------\n // 4. Build relevantNotes with titles + domains\n // ------------------------------------------------------------------\n\n // Fetch titles in bulk\n const allSelectedPaths = Array.from(selectedPaths);\n const fileRows = await backend.getVaultFilesByPaths(allSelectedPaths);\n const titleMap = new Map(fileRows.map(f => [f.vaultPath, f.title]));\n\n const relevantNotes: ConverseResult[\"relevantNotes\"] = [];\n\n for (const r of selectedSearchPaths) {\n if (!selectedPaths.has(r.path)) continue;\n relevantNotes.push({\n path: r.path,\n title: titleMap.get(r.path) ?? null,\n snippet: r.snippet,\n score: r.score,\n domain: extractDomain(r.path),\n });\n }\n\n for (const r of selectedNeighbors) {\n relevantNotes.push({\n path: r.path,\n title: titleMap.get(r.path) ?? null,\n snippet: r.snippet,\n score: 0,\n domain: extractDomain(r.path),\n });\n }\n\n // ------------------------------------------------------------------\n // 5. Find connections between the selected notes\n // ------------------------------------------------------------------\n let connections: ConverseConnection[] = [];\n\n if (selectedPaths.size > 0) {\n const pathList = Array.from(selectedPaths);\n const pathSet = new Set(pathList);\n\n // Get all outbound links from the selected paths\n const linkRows = await backend.getVaultLinksFromPaths(pathList);\n\n // Count links between selected paths\n const edgeCounts = new Map();\n for (const link of linkRows) {\n if (link.targetPath && pathSet.has(link.targetPath)) {\n const key = `${link.sourcePath}|||${link.targetPath}`;\n edgeCounts.set(key, (edgeCounts.get(key) ?? 0) + 1);\n }\n }\n\n for (const [key, cnt] of edgeCounts) {\n const [sourcePath, targetPath] = key.split(\"|||\");\n connections.push({\n fromPath: sourcePath,\n toPath: targetPath,\n fromDomain: extractDomain(sourcePath),\n toDomain: extractDomain(targetPath),\n strength: cnt,\n });\n }\n }\n\n // ------------------------------------------------------------------\n // 6. Domains + cross-domain filter\n // ------------------------------------------------------------------\n const domainSet = new Set(relevantNotes.map((n) => n.domain));\n const domains = Array.from(domainSet).sort();\n\n const crossDomainConnections = connections.filter(\n (c) => c.fromDomain !== c.toDomain,\n );\n\n // ------------------------------------------------------------------\n // 7. Build synthesis prompt\n // ------------------------------------------------------------------\n const notesSummary = relevantNotes\n .map((n, i) => {\n const title = n.title ? `\"${n.title}\"` : \"(untitled)\";\n const domain = n.domain;\n const scoreLabel = n.score > 0 ? ` [relevance: ${n.score.toFixed(3)}]` : \" [context]\";\n const snippet = n.snippet.trim().slice(0, 300);\n return `${i + 1}. [${domain}] ${title}${scoreLabel}\\n Path: ${n.path}\\n \"${snippet}\"`;\n })\n .join(\"\\n\\n\");\n\n const connectionSummary =\n crossDomainConnections.length > 0\n ? crossDomainConnections\n .map(\n (c) =>\n `- \"${c.fromPath}\" (${c.fromDomain}) → \"${c.toPath}\" (${c.toDomain}) [strength: ${c.strength}]`,\n )\n .join(\"\\n\")\n : \"(no cross-domain connections found)\";\n\n const domainList = domains.join(\", \");\n\n const synthesisPrompt = `You are a Zettelkasten research assistant. The vault has surfaced the following notes in response to this question:\n\nQUESTION: ${opts.question}\n\n---\n\nRELEVANT NOTES (${relevantNotes.length} notes across ${domains.length} domain(s): ${domainList}):\n\n${notesSummary}\n\n---\n\nCROSS-DOMAIN CONNECTIONS (links bridging different knowledge areas):\n\n${connectionSummary}\n\n---\n\nSYNTHESIS TASK:\n\nBased on these notes and the connections between them, please:\n\n1. Identify the key insights that emerge in direct response to the question.\n2. Highlight any unexpected connections between notes from different domains (${domainList}).\n3. Point out tensions, contradictions, or open questions the vault raises but does not resolve.\n4. Suggest what is notably absent — what the vault does NOT yet contain that would strengthen the understanding of this topic.\n5. Propose 2-3 new notes that would meaningfully extend this knowledge cluster.\n\nThink like a scholar who has deeply internalized these ideas and is now synthesizing them for the first time.`;\n\n return {\n relevantNotes,\n connections: crossDomainConnections,\n domains,\n synthesisPrompt,\n };\n}\n","import type { StorageBackend } from \"../storage/interface.js\";\n\nexport interface HealthOptions {\n scope?: \"full\" | \"recent\" | \"project\";\n projectPath?: string;\n recentDays?: number;\n include?: Array<\"dead_links\" | \"orphans\" | \"disconnected\" | \"low_connectivity\">;\n}\n\nexport interface DeadLink {\n sourcePath: string;\n targetRaw: string;\n lineNumber: number;\n}\n\nexport interface HealthResult {\n totalFiles: number;\n totalLinks: number;\n deadLinks: DeadLink[];\n orphans: string[];\n disconnectedClusters: number;\n lowConnectivity: string[];\n healthScore: number;\n computedAt: number;\n /** Breakdown of links by confidence level (if confidence tagging is active). */\n linkConfidence?: {\n extracted: number;\n inferred: number;\n ambiguous: number;\n };\n}\n\nfunction countComponents(nodes: string[], edges: Array<{ source: string; target: string }>): number {\n if (nodes.length === 0) return 0;\n\n const parent = new Map();\n const rank = new Map();\n\n for (const n of nodes) {\n parent.set(n, n);\n rank.set(n, 0);\n }\n\n function find(x: string): string {\n let root = x;\n while (parent.get(root) !== root) {\n root = parent.get(root)!;\n }\n let current = x;\n while (current !== root) {\n const next = parent.get(current)!;\n parent.set(current, root);\n current = next;\n }\n return root;\n }\n\n function union(a: string, b: string): void {\n const ra = find(a);\n const rb = find(b);\n if (ra === rb) return;\n const rankA = rank.get(ra) ?? 0;\n const rankB = rank.get(rb) ?? 0;\n if (rankA < rankB) {\n parent.set(ra, rb);\n } else if (rankA > rankB) {\n parent.set(rb, ra);\n } else {\n parent.set(rb, ra);\n rank.set(ra, rankA + 1);\n }\n }\n\n for (const { source, target } of edges) {\n if (parent.has(source) && parent.has(target)) {\n union(source, target);\n }\n }\n\n const roots = new Set();\n for (const n of nodes) {\n roots.add(find(n));\n }\n return roots.size;\n}\n\n/**\n * Audit the structural health of the Zettelkasten vault using graph metrics.\n */\nexport async function zettelHealth(backend: StorageBackend, opts?: HealthOptions): Promise {\n const options = opts ?? {};\n const scope = options.scope ?? \"full\";\n const include = options.include ?? [\"dead_links\", \"orphans\", \"disconnected\", \"low_connectivity\"];\n\n const computedAt = Date.now();\n\n // --- totalFiles ---\n let totalFiles = 0;\n if (scope === \"full\") {\n totalFiles = await backend.countVaultFiles();\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n totalFiles = await backend.countVaultFilesWithPrefix(prefix);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n totalFiles = await backend.countVaultFilesAfter(cutoff);\n }\n\n // --- totalLinks ---\n let totalLinks = 0;\n if (scope === \"full\") {\n // Count total links via link graph length\n const graph = await backend.getVaultLinkGraph();\n totalLinks = graph.length;\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n totalLinks = await backend.countVaultLinksWithPrefix(prefix);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n totalLinks = await backend.countVaultLinksAfter(cutoff);\n }\n\n // --- deadLinks ---\n let deadLinks: DeadLink[] = [];\n if (include.includes(\"dead_links\")) {\n if (scope === \"full\") {\n deadLinks = await backend.getDeadLinksWithLineNumbers();\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n deadLinks = await backend.getDeadLinksWithPrefix(prefix);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n deadLinks = await backend.getDeadLinksAfter(cutoff);\n }\n }\n\n // --- orphans ---\n let orphans: string[] = [];\n if (include.includes(\"orphans\")) {\n if (scope === \"full\") {\n const orphanRows = await backend.getOrphans();\n orphans = orphanRows.map(r => r.vaultPath);\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n orphans = await backend.getOrphansWithPrefix(prefix);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n orphans = await backend.getOrphansAfter(cutoff);\n }\n }\n\n // --- disconnectedClusters (union-find) ---\n let disconnectedClusters = 1;\n if (include.includes(\"disconnected\")) {\n let allNodes: string[];\n let allEdges: Array<{ source: string; target: string }>;\n\n if (scope === \"full\") {\n [allNodes, allEdges] = await Promise.all([\n backend.getAllVaultFilePaths(),\n backend.getVaultLinkEdges(),\n ]);\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n [allNodes, allEdges] = await Promise.all([\n backend.getVaultFilePathsWithPrefix(prefix),\n backend.getVaultLinkEdgesWithPrefix(prefix),\n ]);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n [allNodes, allEdges] = await Promise.all([\n backend.getVaultFilePathsAfter(cutoff),\n backend.getVaultLinkEdgesAfter(cutoff),\n ]);\n }\n\n disconnectedClusters = countComponents(allNodes, allEdges);\n }\n\n // --- lowConnectivity ---\n let lowConnectivity: string[] = [];\n if (include.includes(\"low_connectivity\")) {\n if (scope === \"full\") {\n lowConnectivity = await backend.getLowConnectivity();\n } else if (scope === \"project\") {\n const prefix = options.projectPath ?? \"\";\n lowConnectivity = await backend.getLowConnectivityWithPrefix(prefix);\n } else {\n const days = options.recentDays ?? 30;\n const cutoff = computedAt - days * 86400000;\n lowConnectivity = await backend.getLowConnectivityAfter(cutoff);\n }\n }\n\n // --- linkConfidence ---\n // Count links by confidence level using a sampling of all vault file paths.\n // We reuse the link graph data we already fetched for totalLinks when scope=full.\n let linkConfidence: HealthResult[\"linkConfidence\"] | undefined;\n if (scope === \"full\") {\n try {\n // Get all file paths to iterate their outbound links with confidence\n const samplePaths = await backend.getAllVaultFilePaths();\n const sampleSize = Math.min(samplePaths.length, 200); // limit for performance\n const sampled = samplePaths.slice(0, sampleSize);\n let extracted = 0;\n let inferred = 0;\n let ambiguous = 0;\n for (const path of sampled) {\n const links = await backend.getLinksFromSource(path);\n for (const link of links) {\n const c = link.confidence ?? \"EXTRACTED\";\n if (c === \"EXTRACTED\") extracted++;\n else if (c === \"INFERRED\") inferred++;\n else ambiguous++;\n }\n }\n // Extrapolate if we sampled\n const factor = samplePaths.length > 0 ? samplePaths.length / sampleSize : 1;\n linkConfidence = {\n extracted: Math.round(extracted * factor),\n inferred: Math.round(inferred * factor),\n ambiguous: Math.round(ambiguous * factor),\n };\n } catch {\n // Backend may not support confidence field yet — ignore\n }\n }\n\n // --- healthScore ---\n const deadRatio = totalLinks > 0 ? deadLinks.length / totalLinks : 0;\n const orphanRatio = totalFiles > 0 ? orphans.length / totalFiles : 0;\n const lowConnRatio = totalFiles > 0 ? lowConnectivity.length / totalFiles : 0;\n const healthScore = Math.round(\n 100 * (1 - deadRatio) * (1 - orphanRatio * 0.5) * (1 - lowConnRatio * 0.3),\n );\n\n return {\n totalFiles,\n totalLinks,\n deadLinks,\n orphans,\n disconnectedClusters,\n lowConnectivity,\n healthScore,\n computedAt,\n linkConfidence,\n };\n}\n","import type { StorageBackend } from \"../storage/interface.js\";\nimport { deserializeEmbedding, cosineSimilarity } from \"../memory/embeddings.js\";\nimport { basename } from \"node:path\";\nimport { STOP_WORDS } from \"../utils/stop-words.js\";\n\nexport interface SuggestOptions {\n notePath: string;\n vaultProjectId: number;\n limit?: number;\n excludeLinked?: boolean;\n}\n\nexport interface Suggestion {\n path: string;\n title: string | null;\n score: number;\n semanticScore: number;\n tagScore: number;\n neighborScore: number;\n reason: string;\n suggestedWikilink: string;\n}\n\nconst MAX_CHUNKS = 5000;\nconst SEMANTIC_WEIGHT = 0.5;\nconst TAG_WEIGHT = 0.2;\nconst NEIGHBOR_WEIGHT = 0.3;\n\n// STOP_WORDS imported from utils/stop-words.ts\n\nfunction extractTagsFromChunkTexts(texts: string[]): Set {\n const tags = new Set();\n for (const text of texts) {\n // Match YAML frontmatter tags block: \"tags:\\n - tag1\\n - tag2\"\n const match = text.match(/^tags:\\s*\\n((?:[ \\t]*-[ \\t]*.+\\n?)*)/m);\n if (!match) continue;\n const block = match[1];\n const lines = block.split(\"\\n\");\n for (const line of lines) {\n const tagMatch = line.match(/^[ \\t]*-[ \\t]*(.+)/);\n if (tagMatch) {\n const tag = tagMatch[1].trim().toLowerCase();\n if (tag) tags.add(tag);\n }\n }\n }\n return tags;\n}\n\nfunction jaccardSimilarity(a: Set, b: Set): number {\n if (a.size === 0 && b.size === 0) return 0;\n let intersection = 0;\n for (const tag of a) {\n if (b.has(tag)) intersection++;\n }\n const union = a.size + b.size - intersection;\n return union === 0 ? 0 : intersection / union;\n}\n\nfunction buildReason(\n semanticScore: number,\n tagScore: number,\n neighborScore: number,\n neighborCount: number,\n): string {\n const signals: Array<{ label: string; value: number }> = [\n { label: `Semantically similar (${semanticScore.toFixed(2)})`, value: semanticScore * SEMANTIC_WEIGHT },\n { label: `Shared tags (${tagScore.toFixed(2)} Jaccard)`, value: tagScore * TAG_WEIGHT },\n { label: `Linked by ${neighborCount} mutual connection${neighborCount !== 1 ? \"s\" : \"\"}`, value: neighborScore * NEIGHBOR_WEIGHT },\n ];\n signals.sort((a, b) => b.value - a.value);\n return signals[0].label;\n}\n\nfunction suggestedWikilink(vaultPath: string): string {\n const base = basename(vaultPath);\n const name = base.endsWith(\".md\") ? base.slice(0, -3) : base;\n return `[[${name}]]`;\n}\n\n/**\n * Proactively find notes worth linking to a given note, combining semantic similarity,\n * shared tags, and graph-neighborhood signals into a ranked list of suggestions.\n */\nexport async function zettelSuggest(\n backend: StorageBackend,\n opts: SuggestOptions,\n): Promise {\n const limit = opts.limit ?? 5;\n const excludeLinked = opts.excludeLinked ?? true;\n\n // Step 1: get current outbound links\n const outboundLinks = await backend.getLinksFromSource(opts.notePath);\n const linkedPaths = new Set(outboundLinks.filter(l => l.targetPath !== null).map(l => l.targetPath as string));\n\n // Step 2a: get all file-level embeddings for semantic scoring\n const chunkRows = await backend.getChunksWithEmbeddings(opts.vaultProjectId, MAX_CHUNKS);\n\n const byPath = new Map();\n for (const row of chunkRows) {\n const vec = deserializeEmbedding(row.embedding);\n const entry = byPath.get(row.path);\n if (!entry) {\n byPath.set(row.path, { sum: new Float32Array(vec), count: 1 });\n } else {\n for (let i = 0; i < vec.length; i++) {\n entry.sum[i] += vec[i];\n }\n entry.count++;\n }\n }\n\n const allEmbeddings = new Map();\n for (const [path, { sum, count }] of byPath) {\n const avg = new Float32Array(sum.length);\n for (let i = 0; i < sum.length; i++) {\n avg[i] = sum[i] / count;\n }\n allEmbeddings.set(path, avg);\n }\n allEmbeddings.delete(opts.notePath);\n\n // Step 2b: get source embedding\n const sourceEmbedding = allEmbeddings.get(opts.notePath) ?? null;\n\n // Step 2c: get source tags\n const sourceChunkTexts = await backend.getChunksForPath(opts.vaultProjectId, opts.notePath, 5);\n const sourceTags = extractTagsFromChunkTexts(sourceChunkTexts.map(r => r.text));\n\n // Step 2d: compute graph neighborhood (friends-of-friends)\n const directLinks = await backend.getLinksFromSource(opts.notePath);\n const directTargets = directLinks.filter(l => l.targetPath !== null).map(l => l.targetPath as string);\n\n const friendLinkCounts = new Map();\n for (const target of directTargets) {\n const friendLinks = await backend.getLinksFromSource(target);\n for (const link of friendLinks) {\n if (link.targetPath && link.targetPath !== opts.notePath) {\n friendLinkCounts.set(link.targetPath, (friendLinkCounts.get(link.targetPath) ?? 0) + 1);\n }\n }\n }\n const maxFriendLinks = Math.max(1, ...friendLinkCounts.values());\n\n // Get all vault files to enumerate candidates\n const allFiles = await backend.getAllVaultFiles();\n\n const suggestions: Suggestion[] = [];\n\n for (const fileRow of allFiles) {\n const vault_path = fileRow.vaultPath;\n const title = fileRow.title;\n\n if (vault_path === opts.notePath) continue;\n if (excludeLinked && linkedPaths.has(vault_path)) continue;\n\n // Semantic score\n let semanticScore = 0;\n if (sourceEmbedding) {\n const candidateEmbedding = allEmbeddings.get(vault_path);\n if (candidateEmbedding) {\n semanticScore = Math.max(0, cosineSimilarity(sourceEmbedding, candidateEmbedding));\n }\n }\n\n // Tag score (only compute if candidate might have chunks)\n let tagScore = 0;\n if (allEmbeddings.has(vault_path)) {\n const candidateChunkTexts = await backend.getChunksForPath(opts.vaultProjectId, vault_path, 5);\n const candidateTags = extractTagsFromChunkTexts(candidateChunkTexts.map(r => r.text));\n tagScore = jaccardSimilarity(sourceTags, candidateTags);\n }\n\n // Neighbor score\n const friendCount = friendLinkCounts.get(vault_path) ?? 0;\n const neighborScore = friendCount / maxFriendLinks;\n\n const score =\n SEMANTIC_WEIGHT * semanticScore +\n TAG_WEIGHT * tagScore +\n NEIGHBOR_WEIGHT * neighborScore;\n\n // Only include if there is at least some signal\n if (score <= 0) continue;\n\n const reason = buildReason(semanticScore, tagScore, neighborScore, friendCount);\n\n suggestions.push({\n path: vault_path,\n title,\n score,\n semanticScore,\n tagScore,\n neighborScore,\n reason,\n suggestedWikilink: suggestedWikilink(vault_path),\n });\n }\n\n suggestions.sort((a, b) => b.score - a.score);\n return suggestions.slice(0, limit);\n}\n","/**\n * God-note detection — find hub notes via degree centrality on the vault link graph.\n *\n * Hub notes (or \"god notes\") are notes with unusually high inbound link counts.\n * They represent core concepts that the rest of the vault frequently references.\n * Structural pages (index, MOC, master notes, tag pages) are filtered out.\n */\n\nimport type { StorageBackend } from \"../storage/interface.js\";\n\nexport interface GodNoteOptions {\n /** Maximum number of hub notes to return. Default: 20. */\n limit?: number;\n /** Minimum inbound link count to qualify as a hub. Default: 3. */\n minInbound?: number;\n /** Include outbound counts in the result. Default: true. */\n includeOutbound?: boolean;\n}\n\nexport interface GodNote {\n path: string;\n title: string | null;\n inboundCount: number;\n outboundCount: number;\n /** Ratio of inbound to total degree — higher means more \"sink\"-like. */\n inboundRatio: number;\n}\n\nexport interface GodNoteResult {\n godNotes: GodNote[];\n totalVaultFiles: number;\n /** Median inbound count across all vault files (for context). */\n medianInbound: number;\n}\n\n/** Patterns that identify structural/meta pages rather than concept notes. */\nconst STRUCTURAL_PATTERNS = [\n /\\bindex\\b/i,\n /\\bMOC\\b/,\n /\\bmaster\\b/i,\n /\\btag\\s*page/i,\n /\\bhome\\b/i,\n /\\bdashboard\\b/i,\n /\\btemplate/i,\n /^_/,\n];\n\nfunction isStructuralNote(title: string | null, path: string): boolean {\n const text = title ?? path;\n return STRUCTURAL_PATTERNS.some((pattern) => pattern.test(text));\n}\n\n/**\n * Find hub/\"god\" notes in the vault — notes with the highest inbound link counts,\n * excluding structural pages.\n */\nexport async function zettelGodNotes(\n backend: StorageBackend,\n opts?: GodNoteOptions,\n): Promise {\n const limit = opts?.limit ?? 20;\n const minInbound = opts?.minInbound ?? 3;\n\n // Get the full link graph\n const linkGraph = await backend.getVaultLinkGraph();\n\n // Count inbound and outbound per path\n const inboundCounts = new Map();\n const outboundCounts = new Map();\n\n for (const { source_path, target_path } of linkGraph) {\n inboundCounts.set(target_path, (inboundCounts.get(target_path) ?? 0) + 1);\n outboundCounts.set(source_path, (outboundCounts.get(source_path) ?? 0) + 1);\n }\n\n // Get all vault files for title lookup and total count\n const allFiles = await backend.getAllVaultFiles();\n const titleMap = new Map();\n for (const f of allFiles) {\n titleMap.set(f.vaultPath, f.title);\n }\n\n const totalVaultFiles = allFiles.length;\n\n // Compute median inbound for context\n const allInbounds = allFiles.map((f) => inboundCounts.get(f.vaultPath) ?? 0);\n allInbounds.sort((a, b) => a - b);\n const medianInbound =\n allInbounds.length > 0\n ? allInbounds[Math.floor(allInbounds.length / 2)]\n : 0;\n\n // Build candidate list: all paths with inbound >= minInbound, excluding structural\n const candidates: GodNote[] = [];\n\n for (const [path, inbound] of inboundCounts) {\n if (inbound < minInbound) continue;\n\n const title = titleMap.get(path) ?? null;\n if (isStructuralNote(title, path)) continue;\n\n const outbound = outboundCounts.get(path) ?? 0;\n const totalDegree = inbound + outbound;\n const inboundRatio = totalDegree > 0 ? inbound / totalDegree : 0;\n\n candidates.push({\n path,\n title,\n inboundCount: inbound,\n outboundCount: outbound,\n inboundRatio: Math.round(inboundRatio * 1000) / 1000,\n });\n }\n\n // Sort by inbound count descending\n candidates.sort((a, b) => b.inboundCount - a.inboundCount);\n\n return {\n godNotes: candidates.slice(0, limit),\n totalVaultFiles,\n medianInbound,\n };\n}\n","/**\n * Community detection for the Zettelkasten vault link graph.\n *\n * Implements the Louvain algorithm in pure TypeScript for discovering\n * densely connected clusters of notes. This reveals emergent knowledge\n * communities that may not be obvious from folder structure alone.\n *\n * The Louvain method optimizes modularity in two phases:\n * 1. Local: each node greedily moves to the community that maximizes modularity gain.\n * 2. Aggregation: communities are collapsed into super-nodes, forming a new graph.\n * Repeat until no further improvement.\n */\n\nimport type { StorageBackend } from \"../storage/interface.js\";\n\nexport interface CommunityOptions {\n /** Minimum community size to include in results. Default: 3. */\n minSize?: number;\n /** Maximum number of communities to return. Default: 20. */\n maxCommunities?: number;\n /** Resolution parameter for Louvain (higher = more communities). Default: 1.0. */\n resolution?: number;\n}\n\nexport interface CommunityNode {\n path: string;\n title: string | null;\n /** Degree (inbound + outbound) within the community. */\n internalDegree: number;\n}\n\nexport interface Community {\n id: number;\n /** Label derived from most common words in note titles. */\n label: string;\n nodes: CommunityNode[];\n size: number;\n /** Cohesion: ratio of internal edges to total possible internal edges. */\n cohesion: number;\n /** Top folders represented in this community. */\n topFolders: string[];\n}\n\nexport interface CommunityResult {\n communities: Community[];\n totalNodes: number;\n totalEdges: number;\n /** Global modularity score of the partition. */\n modularity: number;\n}\n\n// ---------------------------------------------------------------------------\n// Louvain algorithm implementation\n// ---------------------------------------------------------------------------\n\ninterface Graph {\n /** All node IDs */\n nodes: string[];\n /** Adjacency list: node -> Map */\n adj: Map>;\n /** Total edge weight (sum of all edge weights, counting undirected edges once) */\n totalWeight: number;\n /** Weighted degree per node */\n degree: Map;\n}\n\nfunction buildUndirectedGraph(\n edges: Array<{ source_path: string; target_path: string }>\n): Graph {\n const adj = new Map>();\n const nodeSet = new Set();\n\n function getOrCreate(node: string): Map {\n let m = adj.get(node);\n if (!m) {\n m = new Map();\n adj.set(node, m);\n }\n nodeSet.add(node);\n return m;\n }\n\n let totalWeight = 0;\n\n for (const { source_path, target_path } of edges) {\n if (source_path === target_path) continue; // skip self-loops\n\n const aMap = getOrCreate(source_path);\n const bMap = getOrCreate(target_path);\n\n // Undirected: add weight in both directions\n aMap.set(target_path, (aMap.get(target_path) ?? 0) + 1);\n bMap.set(source_path, (bMap.get(source_path) ?? 0) + 1);\n totalWeight += 1; // each undirected edge counted once\n }\n\n // Compute weighted degree\n const degree = new Map();\n for (const [node, neighbors] of adj) {\n let d = 0;\n for (const w of neighbors.values()) d += w;\n degree.set(node, d);\n }\n\n return {\n nodes: Array.from(nodeSet),\n adj,\n totalWeight,\n degree,\n };\n}\n\n/**\n * Run one pass of Phase 1: local node movement.\n * Returns true if any node changed community.\n */\nfunction louvainPhase1(\n graph: Graph,\n community: Map,\n resolution: number,\n): boolean {\n const m2 = 2 * graph.totalWeight;\n if (m2 === 0) return false;\n\n // Community -> sum of degrees of nodes in community\n const communityDegreeSum = new Map();\n // Community -> sum of internal edge weights\n const communityInternalWeight = new Map();\n\n for (const node of graph.nodes) {\n const c = community.get(node)!;\n communityDegreeSum.set(c, (communityDegreeSum.get(c) ?? 0) + (graph.degree.get(node) ?? 0));\n }\n\n // Compute internal weights\n for (const [node, neighbors] of graph.adj) {\n const nc = community.get(node)!;\n for (const [neighbor, weight] of neighbors) {\n if (community.get(neighbor) === nc) {\n communityInternalWeight.set(nc, (communityInternalWeight.get(nc) ?? 0) + weight);\n }\n }\n }\n // Each internal edge is counted twice (both endpoints), so divide\n for (const [c, w] of communityInternalWeight) {\n communityInternalWeight.set(c, w / 2);\n }\n\n let improved = false;\n // Shuffle node order for better convergence\n const shuffled = [...graph.nodes];\n for (let i = shuffled.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [shuffled[i], shuffled[j]] = [shuffled[j], shuffled[i]];\n }\n\n for (const node of shuffled) {\n const currentComm = community.get(node)!;\n const ki = graph.degree.get(node) ?? 0;\n const neighbors = graph.adj.get(node) ?? new Map();\n\n // Compute weight to each neighboring community\n const weightToComm = new Map();\n for (const [neighbor, weight] of neighbors) {\n const nc = community.get(neighbor)!;\n weightToComm.set(nc, (weightToComm.get(nc) ?? 0) + weight);\n }\n\n // Remove node from its current community\n communityDegreeSum.set(currentComm, (communityDegreeSum.get(currentComm) ?? 0) - ki);\n const weightToCurrent = weightToComm.get(currentComm) ?? 0;\n communityInternalWeight.set(\n currentComm,\n (communityInternalWeight.get(currentComm) ?? 0) - weightToCurrent,\n );\n\n // Find best community\n let bestComm = currentComm;\n let bestDelta = 0;\n\n for (const [candidateComm, weightToCandidate] of weightToComm) {\n const sigmaTot = communityDegreeSum.get(candidateComm) ?? 0;\n // Modularity gain of moving node to candidateComm\n const delta = weightToCandidate - resolution * (ki * sigmaTot) / m2;\n if (delta > bestDelta) {\n bestDelta = delta;\n bestComm = candidateComm;\n }\n }\n\n // Also consider staying (delta = 0, but need to re-add)\n // Move node to best community\n community.set(node, bestComm);\n communityDegreeSum.set(bestComm, (communityDegreeSum.get(bestComm) ?? 0) + ki);\n const weightToBest = weightToComm.get(bestComm) ?? 0;\n communityInternalWeight.set(\n bestComm,\n (communityInternalWeight.get(bestComm) ?? 0) + weightToBest,\n );\n\n if (bestComm !== currentComm) {\n improved = true;\n }\n }\n\n return improved;\n}\n\n/**\n * Compute modularity for a given partition.\n */\nfunction computeModularity(\n graph: Graph,\n community: Map,\n resolution: number,\n): number {\n const m2 = 2 * graph.totalWeight;\n if (m2 === 0) return 0;\n\n let q = 0;\n for (const [node, neighbors] of graph.adj) {\n const ci = community.get(node)!;\n const ki = graph.degree.get(node) ?? 0;\n\n for (const [neighbor, weight] of neighbors) {\n if (community.get(neighbor) === ci) {\n q += weight - resolution * (ki * (graph.degree.get(neighbor) ?? 0)) / m2;\n }\n }\n }\n return q / m2;\n}\n\n/**\n * Run Louvain community detection on the vault link graph.\n */\nfunction runLouvain(\n graph: Graph,\n resolution: number,\n): Map {\n // Initialize: each node in its own community\n const community = new Map();\n let nextComm = 0;\n for (const node of graph.nodes) {\n community.set(node, nextComm++);\n }\n\n // Phase 1: iteratively move nodes until no improvement\n const MAX_ITERATIONS = 20;\n for (let iter = 0; iter < MAX_ITERATIONS; iter++) {\n const improved = louvainPhase1(graph, community, resolution);\n if (!improved) break;\n }\n\n // Compact community IDs\n const commRemap = new Map();\n let remapIdx = 0;\n for (const [, c] of community) {\n if (!commRemap.has(c)) {\n commRemap.set(c, remapIdx++);\n }\n }\n for (const [node, c] of community) {\n community.set(node, commRemap.get(c)!);\n }\n\n return community;\n}\n\n// ---------------------------------------------------------------------------\n// Label generation\n// ---------------------------------------------------------------------------\n\nconst STOP_WORDS = new Set([\n \"the\", \"and\", \"for\", \"are\", \"but\", \"not\", \"you\", \"all\", \"can\", \"her\",\n \"was\", \"one\", \"our\", \"out\", \"has\", \"had\", \"how\", \"its\", \"may\", \"new\",\n \"now\", \"old\", \"see\", \"way\", \"who\", \"did\", \"get\", \"let\", \"say\", \"she\",\n \"too\", \"use\", \"from\", \"with\", \"this\", \"that\", \"will\", \"been\", \"have\",\n \"each\", \"make\", \"like\", \"long\", \"look\", \"many\", \"them\", \"then\", \"what\",\n \"when\", \"some\", \"time\", \"very\", \"your\", \"about\", \"could\", \"into\", \"just\",\n \"more\", \"note\", \"notes\", \"than\", \"over\",\n]);\n\nfunction generateCommunityLabel(titles: Array): string {\n const wordCounts = new Map();\n for (const title of titles) {\n if (!title) continue;\n const words = title\n .toLowerCase()\n .replace(/[^a-z0-9\\s]/g, \" \")\n .split(/\\s+/)\n .filter((w) => w.length > 2 && !STOP_WORDS.has(w));\n for (const word of words) {\n wordCounts.set(word, (wordCounts.get(word) ?? 0) + 1);\n }\n }\n const sorted = [...wordCounts.entries()].sort((a, b) => b[1] - a[1]);\n return sorted\n .slice(0, 3)\n .map(([w]) => w)\n .join(\" / \") || \"unnamed\";\n}\n\nfunction getTopFolder(path: string): string {\n const slash = path.indexOf(\"/\");\n return slash === -1 ? path : path.slice(0, slash);\n}\n\n// ---------------------------------------------------------------------------\n// Main export\n// ---------------------------------------------------------------------------\n\n/**\n * Detect communities in the vault link graph using the Louvain algorithm.\n * Returns clusters of densely connected notes with labels and cohesion scores.\n */\nexport async function zettelCommunities(\n backend: StorageBackend,\n opts?: CommunityOptions,\n): Promise {\n const minSize = opts?.minSize ?? 3;\n const maxCommunities = opts?.maxCommunities ?? 20;\n const resolution = opts?.resolution ?? 1.0;\n\n // Get the full link graph\n const linkGraph = await backend.getVaultLinkGraph();\n const graph = buildUndirectedGraph(linkGraph);\n\n if (graph.nodes.length === 0) {\n return {\n communities: [],\n totalNodes: 0,\n totalEdges: 0,\n modularity: 0,\n };\n }\n\n // Run Louvain\n const communityMap = runLouvain(graph, resolution);\n\n // Compute modularity\n const modularity = computeModularity(graph, communityMap, resolution);\n\n // Group nodes by community\n const groups = new Map();\n for (const [node, comm] of communityMap) {\n const arr = groups.get(comm);\n if (arr) arr.push(node);\n else groups.set(comm, [node]);\n }\n\n // Get all vault files for title lookup\n const allFiles = await backend.getAllVaultFiles();\n const titleMap = new Map();\n for (const f of allFiles) {\n titleMap.set(f.vaultPath, f.title);\n }\n\n // Build community results\n const communities: Community[] = [];\n let commId = 0;\n\n for (const [, members] of groups) {\n if (members.length < minSize) continue;\n\n const memberSet = new Set(members);\n const titles = members.map((p) => titleMap.get(p) ?? null);\n const label = generateCommunityLabel(titles);\n\n // Compute internal degree per node\n const nodes: CommunityNode[] = members.map((path) => {\n const neighbors = graph.adj.get(path) ?? new Map();\n let internalDeg = 0;\n for (const [neighbor, weight] of neighbors) {\n if (memberSet.has(neighbor)) internalDeg += weight;\n }\n return {\n path,\n title: titleMap.get(path) ?? null,\n internalDegree: internalDeg,\n };\n });\n\n // Sort nodes by internal degree descending\n nodes.sort((a, b) => b.internalDegree - a.internalDegree);\n\n // Compute cohesion: ratio of actual internal edges to possible\n let internalEdges = 0;\n for (const node of nodes) {\n internalEdges += node.internalDegree;\n }\n internalEdges /= 2; // each edge counted twice\n const possibleEdges = (members.length * (members.length - 1)) / 2;\n const cohesion = possibleEdges > 0 ? Math.round((internalEdges / possibleEdges) * 1000) / 1000 : 0;\n\n // Top folders\n const folderCounts = new Map();\n for (const path of members) {\n const folder = getTopFolder(path);\n folderCounts.set(folder, (folderCounts.get(folder) ?? 0) + 1);\n }\n const topFolders = [...folderCounts.entries()]\n .sort((a, b) => b[1] - a[1])\n .slice(0, 5)\n .map(([f]) => f);\n\n communities.push({\n id: commId++,\n label,\n nodes,\n size: members.length,\n cohesion,\n topFolders,\n });\n }\n\n // Sort by size descending\n communities.sort((a, b) => b.size - a.size);\n\n return {\n communities: communities.slice(0, maxCommunities),\n totalNodes: graph.nodes.length,\n totalEdges: graph.totalWeight,\n modularity: Math.round(modularity * 10000) / 10000,\n };\n}\n"],"mappings":";;;;;;AA2BA,SAAS,aAAa,QAAgB,QAA8C;AAClF,QAAO,QAAQ,OAAO,KAAK,QAAQ,OAAO,GAAG,eAAe;;AAG9D,eAAe,aAAa,SAAyB,WAA2C;CAE9F,MAAM,QAAQ,MAAM,QAAQ,qBAAqB,CAAC,UAAU,CAAC;AAC7D,KAAI,MAAM,SAAS,EAAG,QAAO,MAAM,GAAG;CAGtC,MAAM,QAAQ,MAAM,QAAQ,cAAc,UAAU;AACpD,KAAI,CAAC,MAAO,QAAO;CAEnB,MAAM,YAAY,MAAM,QAAQ,qBAAqB,CAAC,MAAM,cAAc,CAAC;AAC3E,QAAO,UAAU,SAAS,IAAI,UAAU,GAAG,YAAY;;AAGzD,eAAe,oBAAoB,SAAyB,MAAiC;AAE3F,SADc,MAAM,QAAQ,mBAAmB,KAAK,EACvC,QAAO,MAAK,EAAE,eAAe,KAAK,CAAC,KAAI,MAAK,EAAE,WAAqB;;AAGlF,eAAe,qBAAqB,SAAyB,MAAiC;AAE5F,SADc,MAAM,QAAQ,iBAAiB,KAAK,EACrC,KAAI,MAAK,EAAE,WAAW;;AAGrC,eAAe,YACb,SACA,MACsE;CACtE,MAAM,CAAC,OAAO,UAAU,MAAM,QAAQ,IAAI,CACxC,QAAQ,qBAAqB,CAAC,KAAK,CAAC,EACpC,QAAQ,eAAe,KAAK,CAC7B,CAAC;AAEF,QAAO;EACL,OAAO,MAAM,IAAI,SAAS;EAC1B,SAAS,QAAQ,gBAAgB;EACjC,UAAU,QAAQ,iBAAiB;EACpC;;;;;;AAOH,eAAsB,cAAc,SAAyB,MAA8C;CACzG,MAAM,QAAQ,KAAK,IAAI,KAAK,IAAI,KAAK,SAAS,GAAG,EAAE,EAAE,GAAG;CACxD,MAAM,YAAY,KAAK,aAAa;CACpC,MAAM,OAAO,KAAK,QAAQ;CAE1B,MAAM,OAAO,MAAM,aAAa,SAAS,KAAK,UAAU;AACxD,KAAI,CAAC,KACH,QAAO;EACL,MAAM,KAAK;EACX,OAAO,EAAE;EACT,OAAO,EAAE;EACT,iBAAiB,EAAE;EACnB,iBAAiB;EAClB;CAGH,MAAM,UAAU,IAAI,IAAY,CAAC,KAAK,CAAC;CACvC,MAAM,QAAuB,EAAE;CAC/B,MAAM,QAAiF,EAAE;CACzF,IAAI,kBAAkB;CAEtB,MAAM,QAAgD,CAAC;EAAE,MAAM;EAAM,OAAO;EAAG,CAAC;AAEhF,QAAO,MAAM,SAAS,GAAG;EACvB,MAAM,UAAU,MAAM,OAAO;AAE7B,MAAI,QAAQ,SAAS,OAAO;AAC1B,qBAAkB;AAClB;;EAGF,MAAM,YAAmE,EAAE;AAE3E,MAAI,cAAc,aAAa,cAAc,OAC3C,MAAK,MAAM,KAAK,MAAM,oBAAoB,SAAS,QAAQ,KAAK,CAC9D,WAAU,KAAK;GAAE,UAAU;GAAG,MAAM,QAAQ;GAAM,IAAI;GAAG,CAAC;AAI9D,MAAI,cAAc,cAAc,cAAc,OAC5C,MAAK,MAAM,KAAK,MAAM,qBAAqB,SAAS,QAAQ,KAAK,CAC/D,WAAU,KAAK;GAAE,UAAU;GAAG,MAAM;GAAG,IAAI,QAAQ;GAAM,CAAC;AAI9D,OAAK,MAAM,EAAE,UAAU,MAAM,QAAQ,WAAW;GAC9C,MAAM,WAAW,aAAa,MAAM,GAAG;AAEvC,OAAI,SAAS,SAAS,aAAa,KACjC;AAIF,OAAI,CADmB,MAAM,MAAM,MAAM,EAAE,SAAS,QAAQ,EAAE,OAAO,GAAG,CAEtE,OAAM,KAAK;IAAE;IAAM;IAAI,MAAM;IAAU,CAAC;AAG1C,OAAI,CAAC,QAAQ,IAAI,SAAS,EAAE;AAC1B,YAAQ,IAAI,SAAS;IAErB,MAAM,OAAO,MAAM,YAAY,SAAS,SAAS;AACjD,UAAM,KAAK;KACT,MAAM;KACN,OAAO,KAAK;KACZ,OAAO,QAAQ,QAAQ;KACvB,UAAU;KACV,SAAS,KAAK;KACd,UAAU,KAAK;KAChB,CAAC;AAEF,UAAM,KAAK;KAAE,MAAM;KAAU,OAAO,QAAQ,QAAQ;KAAG,CAAC;;;;CAK9D,MAAM,kBAAkB,MACrB,QAAQ,MAAM,EAAE,WAAW,EAAE,CAC7B,KAAK,MAAM,EAAE,KAAK;AAGrB,MADiB,MAAM,YAAY,SAAS,KAAK,EACpC,WAAW,EACtB,iBAAgB,QAAQ,KAAK;AAG/B,QAAO;EAAE;EAAM;EAAO;EAAO;EAAiB;EAAiB;;;;;ACtIjE,MAAMA,eAAa;AACnB,MAAM,cAAc;AAEpB,eAAe,kBACb,SACA,WACiE;CACjE,MAAM,OAAO,MAAM,QAAQ,wBAAwB,WAAWA,aAAW;CAEzE,MAAM,yBAAS,IAAI,KAAiE;AACpF,MAAK,MAAM,OAAO,MAAM;EACtB,MAAM,MAAM,qBAAqB,IAAI,UAAU;EAC/C,MAAM,QAAQ,OAAO,IAAI,IAAI,KAAK;AAClC,MAAI,CAAC,MACH,QAAO,IAAI,IAAI,MAAM;GAAE,KAAK,IAAI,aAAa,IAAI;GAAE,OAAO;GAAG,MAAM,IAAI;GAAM,CAAC;OACzE;AACL,QAAK,IAAI,IAAI,GAAG,IAAI,IAAI,QAAQ,IAC9B,OAAM,IAAI,MAAM,IAAI;AAEtB,SAAM;;;CAIV,MAAM,yBAAS,IAAI,KAAwD;AAC3E,MAAK,MAAM,CAAC,MAAM,EAAE,KAAK,OAAO,WAAW,QAAQ;EACjD,MAAM,MAAM,IAAI,aAAa,IAAI,OAAO;AACxC,OAAK,IAAI,IAAI,GAAG,IAAI,IAAI,QAAQ,IAC9B,KAAI,KAAK,IAAI,KAAK;AAEpB,SAAO,IAAI,MAAM;GAAE,WAAW;GAAK;GAAM,CAAC;;AAE5C,QAAO;;AAGT,eAAe,sBACb,SACA,WACA,MACsD;CACtD,MAAM,OAAO,MAAM,QAAQ,iBAAiB,WAAW,KAAK;AAE5D,KAAI,KAAK,WAAW,EAClB,QAAO;EAAE,WAAW,IAAI,aAAa,EAAE;EAAE,OAAO;EAAO;CAGzD,MAAM,UAAU,KAAK,QAAO,MAAK,EAAE,cAAc,KAAK;AACtD,KAAI,QAAQ,WAAW,EACrB,QAAO;EAAE,WAAW,IAAI,aAAa,EAAE;EAAE,OAAO;EAAO;CAGzD,MAAM,MAAM,qBAAqB,QAAQ,GAAG,UAAU,CAAC;CACvD,MAAM,MAAM,IAAI,aAAa,IAAI;AACjC,MAAK,MAAM,OAAO,SAAS;EACzB,MAAM,MAAM,qBAAqB,IAAI,UAAU;AAC/C,OAAK,IAAI,IAAI,GAAG,IAAI,KAAK,IACvB,KAAI,MAAM,IAAI;;CAGlB,MAAM,MAAM,IAAI,aAAa,IAAI;AACjC,MAAK,IAAI,IAAI,GAAG,IAAI,KAAK,IACvB,KAAI,KAAK,IAAI,KAAK,QAAQ;AAE5B,QAAO;EAAE,WAAW;EAAK,OAAO;EAAM;;AAGxC,eAAe,iBAAiB,SAAyB,QAAgB,QAAiC;AACxG,KAAI,WAAW,OAAQ,QAAO;CAE9B,MAAM,UAAU,IAAI,IAAY,CAAC,OAAO,CAAC;CACzC,MAAM,QAA+C,CAAC;EAAE,MAAM;EAAQ,MAAM;EAAG,CAAC;AAEhF,QAAO,MAAM,SAAS,GAAG;EACvB,MAAM,EAAE,MAAM,SAAS,MAAM,OAAO;AACpC,MAAI,QAAQ,YAAa;EAEzB,MAAM,CAAC,cAAc,iBAAiB,MAAM,QAAQ,IAAI,CACtD,QAAQ,mBAAmB,KAAK,EAChC,QAAQ,iBAAiB,KAAK,CAC/B,CAAC;EAEF,MAAM,YAAsB,CAC1B,GAAG,aAAa,QAAO,MAAK,EAAE,eAAe,KAAK,CAAC,KAAI,MAAK,EAAE,WAAqB,EACnF,GAAG,cAAc,KAAI,MAAK,EAAE,WAAW,CACxC;AAED,OAAK,MAAM,YAAY,WAAW;AAChC,OAAI,aAAa,OAAQ,QAAO,OAAO;AACvC,OAAI,CAAC,QAAQ,IAAI,SAAS,EAAE;AAC1B,YAAQ,IAAI,SAAS;AACrB,UAAM,KAAK;KAAE,MAAM;KAAU,MAAM,OAAO;KAAG,CAAC;;;;AAKpD,QAAO;;AAGT,SAAS,iBACP,WACA,cACQ;CACR,MAAM,OAAO,UAAU,QAAO,MAAK,EAAE,cAAc,KAAK;AACxD,KAAI,KAAK,WAAW,EAAG,QAAO;CAE9B,IAAI,WAAW,KAAK,GAAG;CACvB,IAAI,UAAU;AAEd,MAAK,MAAM,OAAO,MAAM;EAEtB,MAAM,MAAM,iBAAiB,cADjB,qBAAqB,IAAI,UAAU,CACA;AAC/C,MAAI,MAAM,SAAS;AACjB,aAAU;AACV,cAAW,IAAI;;;AAInB,QAAO,SAAS,MAAM,CAAC,MAAM,GAAG,IAAI;;;;;;AAOtC,eAAsB,eACpB,SACA,MAC2B;CAC3B,MAAM,QAAQ,KAAK,SAAS;CAC5B,MAAM,gBAAgB,KAAK,iBAAiB;CAC5C,MAAM,mBAAmB,KAAK,oBAAoB;CAElD,IAAI,EAAE,WAAW,cAAc,UAAU,MAAM,sBAC7C,SACA,KAAK,gBACL,KAAK,cACN;AAGD,KAAI,CAAC,MAGH,gBAAe,MAAM,mBAFP,MAAM,QAAQ,qBAAqB,CAAC,KAAK,cAAc,CAAC,EACnD,IAAI,SAAS,KAAK,eACQ,KAAK;CAGpD,MAAM,oBAAoB,MAAM,kBAAkB,SAAS,KAAK,eAAe;AAG/E,mBAAkB,OAAO,KAAK,cAAc;CAG5C,MAAM,qBAA2D,EAAE;AACnE,MAAK,MAAM,CAAC,MAAM,EAAE,gBAAgB,mBAAmB;EACrD,MAAM,MAAM,iBAAiB,cAAc,UAAU;AACrD,MAAI,OAAO,cACT,oBAAmB,KAAK;GAAE;GAAM;GAAK,CAAC;;CAK1C,MAAM,UAA4B,EAAE;AAEpC,MAAK,MAAM,EAAE,MAAM,SAAS,oBAAoB;EAC9C,MAAM,gBAAgB,MAAM,iBAAiB,SAAS,KAAK,eAAe,KAAK;EAE/E,MAAM,oBAAoB,SAAS,cAAc,GAAG,gBAAgB;AACpE,MAAI,oBAAoB,iBAAkB;EAE1C,MAAM,QAAQ,MAAM,QAAQ,qBAAqB,CAAC,KAAK,CAAC;EACxD,MAAM,YAAY,MAAM,QAAQ,iBAAiB,KAAK,gBAAgB,MAAM,GAAG;EAE/E,MAAM,gBAAgB,MAAM,KAAK,KAAK,oBAAoB,EAAE;EAC5D,MAAM,gBAAgB,iBAAiB,WAAW,aAAa;AAE/D,UAAQ,KAAK;GACX;GACA,OAAO,MAAM,IAAI,SAAS;GAC1B,kBAAkB;GAClB,eAAe,SAAS,cAAc,GAAG,gBAAgB;GACzD;GACA;GACD,CAAC;;AAGJ,SAAQ,MAAM,GAAG,MAAM,EAAE,gBAAgB,EAAE,cAAc;AACzD,QAAO,QAAQ,MAAM,GAAG,MAAM;;;;;;ACjKhC,SAAS,cAAc,WAA2B;CAChD,MAAM,QAAQ,UAAU,QAAQ,IAAI;AACpC,QAAO,UAAU,KAAK,YAAY,UAAU,MAAM,GAAG,MAAM;;;;;;AAO7D,eAAe,gBAAgB,SAAyB,OAAuC;AAC7F,KAAI,MAAM,SAAS,EAAG,QAAO,EAAE;CAC/B,MAAM,WAAW,MAAM,KAAK,MAAM;CAElC,MAAM,CAAC,cAAc,iBAAiB,MAAM,QAAQ,IAAI,CACtD,QAAQ,uBAAuB,SAAS,EACxC,QAAQ,IAAI,SAAS,KAAI,MAAK,QAAQ,iBAAiB,EAAE,CAAC,CAAC,CAC5D,CAAC;CAEF,MAAM,YAAsB,EAAE;AAC9B,MAAK,MAAM,QAAQ,aACjB,KAAI,KAAK,WAAY,WAAU,KAAK,KAAK,WAAW;AAEtD,MAAK,MAAM,YAAY,cACrB,MAAK,MAAM,QAAQ,SACjB,WAAU,KAAK,KAAK,WAAW;AAGnC,QAAO;;;;;AAMT,eAAe,aACb,SACA,OACA,gBACA,MACyB;CACzB,MAAM,aAAa,KAAK,cAAc;CACtC,MAAM,KAAK;CACX,MAAM,KAAK;CAEX,MAAM,CAAC,gBAAgB,mBAAmB,MAAM,QAAQ,IAAI,CAC1D,QAAQ,cAAc,OAAO;EAAE,GAAG;EAAM,YAAY;EAAI,CAAC,EACzD,QAAQ,eAAe,gBAAgB;EAAE,GAAG;EAAM,YAAY;EAAI,CAAC,CACpE,CAAC;AAEF,KAAI,eAAe,WAAW,KAAK,gBAAgB,WAAW,EAAG,QAAO,EAAE;CAE1E,MAAM,UAAU,MACd,GAAG,EAAE,UAAU,GAAG,EAAE,KAAK,GAAG,EAAE,UAAU,GAAG,EAAE;CAE/C,SAAS,gBAAgB,QAA4B;EACnD,MAAM,MAAM,KAAK,IAAI,GAAG,OAAO;EAE/B,MAAM,QADM,KAAK,IAAI,GAAG,OAAO,GACX;AACpB,MAAI,UAAU,EAAG,QAAO,OAAO,UAAU,EAAI;AAC7C,SAAO,OAAO,KAAI,OAAM,IAAI,OAAO,MAAM;;CAG3C,MAAM,SAAS,gBAAgB,eAAe,KAAI,MAAK,EAAE,MAAM,CAAC;CAChE,MAAM,UAAU,gBAAgB,gBAAgB,KAAI,MAAK,EAAE,MAAM,CAAC;CAElE,MAAM,2BAAW,IAAI,KAAuD;AAE5E,MAAK,IAAI,IAAI,GAAG,IAAI,eAAe,QAAQ,KAAK;EAC9C,MAAM,IAAI,eAAe;EACzB,MAAM,IAAI,OAAO,EAAE;AACnB,WAAS,IAAI,GAAG;GAAE,GAAG;GAAG,eAAe,KAAK,OAAO;GAAI,CAAC;;AAG1D,MAAK,IAAI,IAAI,GAAG,IAAI,gBAAgB,QAAQ,KAAK;EAC/C,MAAM,IAAI,gBAAgB;EAC1B,MAAM,IAAI,OAAO,EAAE;EACnB,MAAM,WAAW,SAAS,IAAI,EAAE;AAChC,MAAI,SACF,UAAS,iBAAiB,KAAK,QAAQ;MAEvC,UAAS,IAAI,GAAG;GAAE,GAAG;GAAG,eAAe,KAAK,QAAQ;GAAI,CAAC;;AAQ7D,QAJe,MAAM,KAAK,SAAS,QAAQ,CAAC,CACzC,MAAM,GAAG,MAAM,EAAE,gBAAgB,EAAE,cAAc,CACjD,MAAM,GAAG,WAAW,CAET,KAAI,OAAM;EAAE,GAAG;EAAG,OAAO,EAAE;EAAe,EAAE;;;;;;;AAY5D,eAAsB,eACpB,SACA,MACyB;CACzB,MAAM,QAAQ,KAAK,IAAI,KAAK,SAAS,GAAG,EAAE;CAC1C,MAAM,QAAQ,KAAK,IAAI,KAAK,SAAS,IAAI,EAAE;CAC3C,MAAM,iBAAiB;CAKvB,MAAM,iBAAiB,MAAM,kBAAkB,KAAK,UAAU,KAAK;CAEnE,MAAM,gBAAgB,MAAM,aAC1B,SACA,KAAK,UACL,gBACA;EACE,YAAY,CAAC,KAAK,eAAe;EACjC,YAAY;EACb,CACF;CAGD,MAAM,6BAAa,IAAI,KAAiD;AACxE,MAAK,MAAM,KAAK,eAAe;EAC7B,MAAM,WAAW,WAAW,IAAI,EAAE,KAAK;AACvC,MAAI,CAAC,YAAY,EAAE,QAAQ,SAAS,MAClC,YAAW,IAAI,EAAE,MAAM;GAAE,OAAO,EAAE;GAAO,SAAS,EAAE;GAAS,CAAC;;CAOlE,MAAM,WAAW,IAAI,IAAY,WAAW,MAAM,CAAC;CACnD,IAAI,WAAW,IAAI,IAAY,WAAW,MAAM,CAAC;AAEjD,MAAK,IAAI,IAAI,GAAG,IAAI,OAAO,KAAK;EAC9B,MAAM,YAAY,MAAM,gBAAgB,SAAS,SAAS;EAC1D,MAAM,8BAAc,IAAI,KAAa;AACrC,OAAK,MAAM,KAAK,UACd,KAAI,CAAC,SAAS,IAAI,EAAE,EAAE;AACpB,YAAS,IAAI,EAAE;AACf,eAAY,IAAI,EAAE;;AAGtB,MAAI,YAAY,SAAS,EAAG;AAC5B,aAAW;;CAMb,MAAM,eAAe,MAAM,KAAK,WAAW,SAAS,CAAC,CAClD,MAAM,GAAG,MAAM,EAAE,GAAG,QAAQ,EAAE,GAAG,MAAM,CACvC,KAAK,CAAC,MAAM,WAAW;EAAE;EAAM,GAAG;EAAM,gBAAgB;EAAM,EAAE;CAEnE,MAAM,gBAAgB,MAAM,KAAK,SAAS,CAAC,QAAQ,MAAM,CAAC,WAAW,IAAI,EAAE,CAAC;CAG5E,MAAM,qBAAqB,MAAM,QAAQ,IACvC,cAAc,KAAI,MAAK,QAAQ,eAAe,EAAE,CAAC,CAClD;CACD,MAAM,iBAAiB,cACpB,KAAK,MAAM,SAAS;EACnB;EACA,OAAO;EACP,SAAS;EACT,SAAS,mBAAmB,MAAM,gBAAgB;EAClD,gBAAgB;EACjB,EAAE,CACF,MAAM,GAAG,MAAM,EAAE,UAAU,EAAE,QAAQ;CAExC,MAAM,qBAAqB,KAAK,IAAI,QAAQ,aAAa,QAAQ,EAAE;CACnE,MAAM,oBAAoB,eAAe,MAAM,GAAG,mBAAmB;CAErE,MAAM,sBAAsB,aAAa,MAAM,GAAG,MAAM;CACxD,MAAM,gBAAgB,IAAI,IAAY,CACpC,GAAG,oBAAoB,KAAK,MAAM,EAAE,KAAK,EACzC,GAAG,kBAAkB,KAAK,MAAM,EAAE,KAAK,CACxC,CAAC;CAOF,MAAM,mBAAmB,MAAM,KAAK,cAAc;CAClD,MAAM,WAAW,MAAM,QAAQ,qBAAqB,iBAAiB;CACrE,MAAM,WAAW,IAAI,IAA2B,SAAS,KAAI,MAAK,CAAC,EAAE,WAAW,EAAE,MAAM,CAAC,CAAC;CAE1F,MAAM,gBAAiD,EAAE;AAEzD,MAAK,MAAM,KAAK,qBAAqB;AACnC,MAAI,CAAC,cAAc,IAAI,EAAE,KAAK,CAAE;AAChC,gBAAc,KAAK;GACjB,MAAM,EAAE;GACR,OAAO,SAAS,IAAI,EAAE,KAAK,IAAI;GAC/B,SAAS,EAAE;GACX,OAAO,EAAE;GACT,QAAQ,cAAc,EAAE,KAAK;GAC9B,CAAC;;AAGJ,MAAK,MAAM,KAAK,kBACd,eAAc,KAAK;EACjB,MAAM,EAAE;EACR,OAAO,SAAS,IAAI,EAAE,KAAK,IAAI;EAC/B,SAAS,EAAE;EACX,OAAO;EACP,QAAQ,cAAc,EAAE,KAAK;EAC9B,CAAC;CAMJ,IAAI,cAAoC,EAAE;AAE1C,KAAI,cAAc,OAAO,GAAG;EAC1B,MAAM,WAAW,MAAM,KAAK,cAAc;EAC1C,MAAM,UAAU,IAAI,IAAI,SAAS;EAGjC,MAAM,WAAW,MAAM,QAAQ,uBAAuB,SAAS;EAG/D,MAAM,6BAAa,IAAI,KAAqB;AAC5C,OAAK,MAAM,QAAQ,SACjB,KAAI,KAAK,cAAc,QAAQ,IAAI,KAAK,WAAW,EAAE;GACnD,MAAM,MAAM,GAAG,KAAK,WAAW,KAAK,KAAK;AACzC,cAAW,IAAI,MAAM,WAAW,IAAI,IAAI,IAAI,KAAK,EAAE;;AAIvD,OAAK,MAAM,CAAC,KAAK,QAAQ,YAAY;GACnC,MAAM,CAAC,YAAY,cAAc,IAAI,MAAM,MAAM;AACjD,eAAY,KAAK;IACf,UAAU;IACV,QAAQ;IACR,YAAY,cAAc,WAAW;IACrC,UAAU,cAAc,WAAW;IACnC,UAAU;IACX,CAAC;;;CAON,MAAM,YAAY,IAAI,IAAY,cAAc,KAAK,MAAM,EAAE,OAAO,CAAC;CACrE,MAAM,UAAU,MAAM,KAAK,UAAU,CAAC,MAAM;CAE5C,MAAM,yBAAyB,YAAY,QACxC,MAAM,EAAE,eAAe,EAAE,SAC3B;CAKD,MAAM,eAAe,cAClB,KAAK,GAAG,MAAM;EACb,MAAM,QAAQ,EAAE,QAAQ,IAAI,EAAE,MAAM,KAAK;EACzC,MAAM,SAAS,EAAE;EACjB,MAAM,aAAa,EAAE,QAAQ,IAAI,gBAAgB,EAAE,MAAM,QAAQ,EAAE,CAAC,KAAK;EACzE,MAAM,UAAU,EAAE,QAAQ,MAAM,CAAC,MAAM,GAAG,IAAI;AAC9C,SAAO,GAAG,IAAI,EAAE,KAAK,OAAO,IAAI,QAAQ,WAAW,aAAa,EAAE,KAAK,QAAQ,QAAQ;GACvF,CACD,KAAK,OAAO;CAEf,MAAM,oBACJ,uBAAuB,SAAS,IAC5B,uBACG,KACE,MACC,MAAM,EAAE,SAAS,KAAK,EAAE,WAAW,OAAO,EAAE,OAAO,KAAK,EAAE,SAAS,eAAe,EAAE,SAAS,GAChG,CACA,KAAK,KAAK,GACb;CAEN,MAAM,aAAa,QAAQ,KAAK,KAAK;AAgCrC,QAAO;EACL;EACA,aAAa;EACb;EACA,iBAlCsB;;YAEd,KAAK,SAAS;;;;kBAIR,cAAc,OAAO,gBAAgB,QAAQ,OAAO,cAAc,WAAW;;EAE7F,aAAa;;;;;;EAMb,kBAAkB;;;;;;;;;gFAS4D,WAAW;;;;;;EAYxF;;;;;AC5UH,SAAS,gBAAgB,OAAiB,OAA0D;AAClG,KAAI,MAAM,WAAW,EAAG,QAAO;CAE/B,MAAM,yBAAS,IAAI,KAAqB;CACxC,MAAM,uBAAO,IAAI,KAAqB;AAEtC,MAAK,MAAM,KAAK,OAAO;AACrB,SAAO,IAAI,GAAG,EAAE;AAChB,OAAK,IAAI,GAAG,EAAE;;CAGhB,SAAS,KAAK,GAAmB;EAC/B,IAAI,OAAO;AACX,SAAO,OAAO,IAAI,KAAK,KAAK,KAC1B,QAAO,OAAO,IAAI,KAAK;EAEzB,IAAI,UAAU;AACd,SAAO,YAAY,MAAM;GACvB,MAAM,OAAO,OAAO,IAAI,QAAQ;AAChC,UAAO,IAAI,SAAS,KAAK;AACzB,aAAU;;AAEZ,SAAO;;CAGT,SAAS,MAAM,GAAW,GAAiB;EACzC,MAAM,KAAK,KAAK,EAAE;EAClB,MAAM,KAAK,KAAK,EAAE;AAClB,MAAI,OAAO,GAAI;EACf,MAAM,QAAQ,KAAK,IAAI,GAAG,IAAI;EAC9B,MAAM,QAAQ,KAAK,IAAI,GAAG,IAAI;AAC9B,MAAI,QAAQ,MACV,QAAO,IAAI,IAAI,GAAG;WACT,QAAQ,MACjB,QAAO,IAAI,IAAI,GAAG;OACb;AACL,UAAO,IAAI,IAAI,GAAG;AAClB,QAAK,IAAI,IAAI,QAAQ,EAAE;;;AAI3B,MAAK,MAAM,EAAE,QAAQ,YAAY,MAC/B,KAAI,OAAO,IAAI,OAAO,IAAI,OAAO,IAAI,OAAO,CAC1C,OAAM,QAAQ,OAAO;CAIzB,MAAM,wBAAQ,IAAI,KAAa;AAC/B,MAAK,MAAM,KAAK,MACd,OAAM,IAAI,KAAK,EAAE,CAAC;AAEpB,QAAO,MAAM;;;;;AAMf,eAAsB,aAAa,SAAyB,MAA6C;CACvG,MAAM,UAAU,QAAQ,EAAE;CAC1B,MAAM,QAAQ,QAAQ,SAAS;CAC/B,MAAM,UAAU,QAAQ,WAAW;EAAC;EAAc;EAAW;EAAgB;EAAmB;CAEhG,MAAM,aAAa,KAAK,KAAK;CAG7B,IAAI,aAAa;AACjB,KAAI,UAAU,OACZ,cAAa,MAAM,QAAQ,iBAAiB;UACnC,UAAU,WAAW;EAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,eAAa,MAAM,QAAQ,0BAA0B,OAAO;QACvD;EAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,eAAa,MAAM,QAAQ,qBAAqB,OAAO;;CAIzD,IAAI,aAAa;AACjB,KAAI,UAAU,OAGZ,eADc,MAAM,QAAQ,mBAAmB,EAC5B;UACV,UAAU,WAAW;EAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,eAAa,MAAM,QAAQ,0BAA0B,OAAO;QACvD;EAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,eAAa,MAAM,QAAQ,qBAAqB,OAAO;;CAIzD,IAAI,YAAwB,EAAE;AAC9B,KAAI,QAAQ,SAAS,aAAa,CAChC,KAAI,UAAU,OACZ,aAAY,MAAM,QAAQ,6BAA6B;UAC9C,UAAU,WAAW;EAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,cAAY,MAAM,QAAQ,uBAAuB,OAAO;QACnD;EAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,cAAY,MAAM,QAAQ,kBAAkB,OAAO;;CAKvD,IAAI,UAAoB,EAAE;AAC1B,KAAI,QAAQ,SAAS,UAAU,CAC7B,KAAI,UAAU,OAEZ,YADmB,MAAM,QAAQ,YAAY,EACxB,KAAI,MAAK,EAAE,UAAU;UACjC,UAAU,WAAW;EAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,YAAU,MAAM,QAAQ,qBAAqB,OAAO;QAC/C;EAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,YAAU,MAAM,QAAQ,gBAAgB,OAAO;;CAKnD,IAAI,uBAAuB;AAC3B,KAAI,QAAQ,SAAS,eAAe,EAAE;EACpC,IAAI;EACJ,IAAI;AAEJ,MAAI,UAAU,OACZ,EAAC,UAAU,YAAY,MAAM,QAAQ,IAAI,CACvC,QAAQ,sBAAsB,EAC9B,QAAQ,mBAAmB,CAC5B,CAAC;WACO,UAAU,WAAW;GAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,IAAC,UAAU,YAAY,MAAM,QAAQ,IAAI,CACvC,QAAQ,4BAA4B,OAAO,EAC3C,QAAQ,4BAA4B,OAAO,CAC5C,CAAC;SACG;GAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,IAAC,UAAU,YAAY,MAAM,QAAQ,IAAI,CACvC,QAAQ,uBAAuB,OAAO,EACtC,QAAQ,uBAAuB,OAAO,CACvC,CAAC;;AAGJ,yBAAuB,gBAAgB,UAAU,SAAS;;CAI5D,IAAI,kBAA4B,EAAE;AAClC,KAAI,QAAQ,SAAS,mBAAmB,CACtC,KAAI,UAAU,OACZ,mBAAkB,MAAM,QAAQ,oBAAoB;UAC3C,UAAU,WAAW;EAC9B,MAAM,SAAS,QAAQ,eAAe;AACtC,oBAAkB,MAAM,QAAQ,6BAA6B,OAAO;QAC/D;EAEL,MAAM,SAAS,cADF,QAAQ,cAAc,MACA;AACnC,oBAAkB,MAAM,QAAQ,wBAAwB,OAAO;;CAOnE,IAAI;AACJ,KAAI,UAAU,OACZ,KAAI;EAEF,MAAM,cAAc,MAAM,QAAQ,sBAAsB;EACxD,MAAM,aAAa,KAAK,IAAI,YAAY,QAAQ,IAAI;EACpD,MAAM,UAAU,YAAY,MAAM,GAAG,WAAW;EAChD,IAAI,YAAY;EAChB,IAAI,WAAW;EACf,IAAI,YAAY;AAChB,OAAK,MAAM,QAAQ,SAAS;GAC1B,MAAM,QAAQ,MAAM,QAAQ,mBAAmB,KAAK;AACpD,QAAK,MAAM,QAAQ,OAAO;IACxB,MAAM,IAAI,KAAK,cAAc;AAC7B,QAAI,MAAM,YAAa;aACd,MAAM,WAAY;QACtB;;;EAIT,MAAM,SAAS,YAAY,SAAS,IAAI,YAAY,SAAS,aAAa;AAC1E,mBAAiB;GACf,WAAW,KAAK,MAAM,YAAY,OAAO;GACzC,UAAU,KAAK,MAAM,WAAW,OAAO;GACvC,WAAW,KAAK,MAAM,YAAY,OAAO;GAC1C;SACK;CAMV,MAAM,YAAY,aAAa,IAAI,UAAU,SAAS,aAAa;CACnE,MAAM,cAAc,aAAa,IAAI,QAAQ,SAAS,aAAa;CACnE,MAAM,eAAe,aAAa,IAAI,gBAAgB,SAAS,aAAa;CAC5E,MAAM,cAAc,KAAK,MACvB,OAAO,IAAI,cAAc,IAAI,cAAc,OAAQ,IAAI,eAAe,IACvE;AAED,QAAO;EACL;EACA;EACA;EACA;EACA;EACA;EACA;EACA;EACA;EACD;;;;;ACpOH,MAAM,aAAa;AACnB,MAAM,kBAAkB;AACxB,MAAM,aAAa;AACnB,MAAM,kBAAkB;AAIxB,SAAS,0BAA0B,OAA8B;CAC/D,MAAM,uBAAO,IAAI,KAAa;AAC9B,MAAK,MAAM,QAAQ,OAAO;EAExB,MAAM,QAAQ,KAAK,MAAM,wCAAwC;AACjE,MAAI,CAAC,MAAO;EAEZ,MAAM,QADQ,MAAM,GACA,MAAM,KAAK;AAC/B,OAAK,MAAM,QAAQ,OAAO;GACxB,MAAM,WAAW,KAAK,MAAM,qBAAqB;AACjD,OAAI,UAAU;IACZ,MAAM,MAAM,SAAS,GAAG,MAAM,CAAC,aAAa;AAC5C,QAAI,IAAK,MAAK,IAAI,IAAI;;;;AAI5B,QAAO;;AAGT,SAAS,kBAAkB,GAAgB,GAAwB;AACjE,KAAI,EAAE,SAAS,KAAK,EAAE,SAAS,EAAG,QAAO;CACzC,IAAI,eAAe;AACnB,MAAK,MAAM,OAAO,EAChB,KAAI,EAAE,IAAI,IAAI,CAAE;CAElB,MAAM,QAAQ,EAAE,OAAO,EAAE,OAAO;AAChC,QAAO,UAAU,IAAI,IAAI,eAAe;;AAG1C,SAAS,YACP,eACA,UACA,eACA,eACQ;CACR,MAAM,UAAmD;EACvD;GAAE,OAAO,yBAAyB,cAAc,QAAQ,EAAE,CAAC;GAAI,OAAO,gBAAgB;GAAiB;EACvG;GAAE,OAAO,gBAAgB,SAAS,QAAQ,EAAE,CAAC;GAAY,OAAO,WAAW;GAAY;EACvF;GAAE,OAAO,aAAa,cAAc,oBAAoB,kBAAkB,IAAI,MAAM;GAAM,OAAO,gBAAgB;GAAiB;EACnI;AACD,SAAQ,MAAM,GAAG,MAAM,EAAE,QAAQ,EAAE,MAAM;AACzC,QAAO,QAAQ,GAAG;;AAGpB,SAAS,kBAAkB,WAA2B;CACpD,MAAM,OAAO,SAAS,UAAU;AAEhC,QAAO,KADM,KAAK,SAAS,MAAM,GAAG,KAAK,MAAM,GAAG,GAAG,GAAG,KACvC;;;;;;AAOnB,eAAsB,cACpB,SACA,MACuB;CACvB,MAAM,QAAQ,KAAK,SAAS;CAC5B,MAAM,gBAAgB,KAAK,iBAAiB;CAG5C,MAAM,gBAAgB,MAAM,QAAQ,mBAAmB,KAAK,SAAS;CACrE,MAAM,cAAc,IAAI,IAAI,cAAc,QAAO,MAAK,EAAE,eAAe,KAAK,CAAC,KAAI,MAAK,EAAE,WAAqB,CAAC;CAG9G,MAAM,YAAY,MAAM,QAAQ,wBAAwB,KAAK,gBAAgB,WAAW;CAExF,MAAM,yBAAS,IAAI,KAAmD;AACtE,MAAK,MAAM,OAAO,WAAW;EAC3B,MAAM,MAAM,qBAAqB,IAAI,UAAU;EAC/C,MAAM,QAAQ,OAAO,IAAI,IAAI,KAAK;AAClC,MAAI,CAAC,MACH,QAAO,IAAI,IAAI,MAAM;GAAE,KAAK,IAAI,aAAa,IAAI;GAAE,OAAO;GAAG,CAAC;OACzD;AACL,QAAK,IAAI,IAAI,GAAG,IAAI,IAAI,QAAQ,IAC9B,OAAM,IAAI,MAAM,IAAI;AAEtB,SAAM;;;CAIV,MAAM,gCAAgB,IAAI,KAA2B;AACrD,MAAK,MAAM,CAAC,MAAM,EAAE,KAAK,YAAY,QAAQ;EAC3C,MAAM,MAAM,IAAI,aAAa,IAAI,OAAO;AACxC,OAAK,IAAI,IAAI,GAAG,IAAI,IAAI,QAAQ,IAC9B,KAAI,KAAK,IAAI,KAAK;AAEpB,gBAAc,IAAI,MAAM,IAAI;;AAE9B,eAAc,OAAO,KAAK,SAAS;CAGnC,MAAM,kBAAkB,cAAc,IAAI,KAAK,SAAS,IAAI;CAI5D,MAAM,aAAa,2BADM,MAAM,QAAQ,iBAAiB,KAAK,gBAAgB,KAAK,UAAU,EAAE,EAChC,KAAI,MAAK,EAAE,KAAK,CAAC;CAI/E,MAAM,iBADc,MAAM,QAAQ,mBAAmB,KAAK,SAAS,EACjC,QAAO,MAAK,EAAE,eAAe,KAAK,CAAC,KAAI,MAAK,EAAE,WAAqB;CAErG,MAAM,mCAAmB,IAAI,KAAqB;AAClD,MAAK,MAAM,UAAU,eAAe;EAClC,MAAM,cAAc,MAAM,QAAQ,mBAAmB,OAAO;AAC5D,OAAK,MAAM,QAAQ,YACjB,KAAI,KAAK,cAAc,KAAK,eAAe,KAAK,SAC9C,kBAAiB,IAAI,KAAK,aAAa,iBAAiB,IAAI,KAAK,WAAW,IAAI,KAAK,EAAE;;CAI7F,MAAM,iBAAiB,KAAK,IAAI,GAAG,GAAG,iBAAiB,QAAQ,CAAC;CAGhE,MAAM,WAAW,MAAM,QAAQ,kBAAkB;CAEjD,MAAM,cAA4B,EAAE;AAEpC,MAAK,MAAM,WAAW,UAAU;EAC9B,MAAM,aAAa,QAAQ;EAC3B,MAAM,QAAQ,QAAQ;AAEtB,MAAI,eAAe,KAAK,SAAU;AAClC,MAAI,iBAAiB,YAAY,IAAI,WAAW,CAAE;EAGlD,IAAI,gBAAgB;AACpB,MAAI,iBAAiB;GACnB,MAAM,qBAAqB,cAAc,IAAI,WAAW;AACxD,OAAI,mBACF,iBAAgB,KAAK,IAAI,GAAG,iBAAiB,iBAAiB,mBAAmB,CAAC;;EAKtF,IAAI,WAAW;AACf,MAAI,cAAc,IAAI,WAAW,CAG/B,YAAW,kBAAkB,YADP,2BADM,MAAM,QAAQ,iBAAiB,KAAK,gBAAgB,YAAY,EAAE,EAC1B,KAAI,MAAK,EAAE,KAAK,CAAC,CAC9B;EAIzD,MAAM,cAAc,iBAAiB,IAAI,WAAW,IAAI;EACxD,MAAM,gBAAgB,cAAc;EAEpC,MAAM,QACJ,kBAAkB,gBAClB,aAAa,WACb,kBAAkB;AAGpB,MAAI,SAAS,EAAG;EAEhB,MAAM,SAAS,YAAY,eAAe,UAAU,eAAe,YAAY;AAE/E,cAAY,KAAK;GACf,MAAM;GACN;GACA;GACA;GACA;GACA;GACA;GACA,mBAAmB,kBAAkB,WAAW;GACjD,CAAC;;AAGJ,aAAY,MAAM,GAAG,MAAM,EAAE,QAAQ,EAAE,MAAM;AAC7C,QAAO,YAAY,MAAM,GAAG,MAAM;;;;;;ACpKpC,MAAM,sBAAsB;CAC1B;CACA;CACA;CACA;CACA;CACA;CACA;CACA;CACD;AAED,SAAS,iBAAiB,OAAsB,MAAuB;CACrE,MAAM,OAAO,SAAS;AACtB,QAAO,oBAAoB,MAAM,YAAY,QAAQ,KAAK,KAAK,CAAC;;;;;;AAOlE,eAAsB,eACpB,SACA,MACwB;CACxB,MAAM,QAAQ,MAAM,SAAS;CAC7B,MAAM,aAAa,MAAM,cAAc;CAGvC,MAAM,YAAY,MAAM,QAAQ,mBAAmB;CAGnD,MAAM,gCAAgB,IAAI,KAAqB;CAC/C,MAAM,iCAAiB,IAAI,KAAqB;AAEhD,MAAK,MAAM,EAAE,aAAa,iBAAiB,WAAW;AACpD,gBAAc,IAAI,cAAc,cAAc,IAAI,YAAY,IAAI,KAAK,EAAE;AACzE,iBAAe,IAAI,cAAc,eAAe,IAAI,YAAY,IAAI,KAAK,EAAE;;CAI7E,MAAM,WAAW,MAAM,QAAQ,kBAAkB;CACjD,MAAM,2BAAW,IAAI,KAA4B;AACjD,MAAK,MAAM,KAAK,SACd,UAAS,IAAI,EAAE,WAAW,EAAE,MAAM;CAGpC,MAAM,kBAAkB,SAAS;CAGjC,MAAM,cAAc,SAAS,KAAK,MAAM,cAAc,IAAI,EAAE,UAAU,IAAI,EAAE;AAC5E,aAAY,MAAM,GAAG,MAAM,IAAI,EAAE;CACjC,MAAM,gBACJ,YAAY,SAAS,IACjB,YAAY,KAAK,MAAM,YAAY,SAAS,EAAE,IAC9C;CAGN,MAAM,aAAwB,EAAE;AAEhC,MAAK,MAAM,CAAC,MAAM,YAAY,eAAe;AAC3C,MAAI,UAAU,WAAY;EAE1B,MAAM,QAAQ,SAAS,IAAI,KAAK,IAAI;AACpC,MAAI,iBAAiB,OAAO,KAAK,CAAE;EAEnC,MAAM,WAAW,eAAe,IAAI,KAAK,IAAI;EAC7C,MAAM,cAAc,UAAU;EAC9B,MAAM,eAAe,cAAc,IAAI,UAAU,cAAc;AAE/D,aAAW,KAAK;GACd;GACA;GACA,cAAc;GACd,eAAe;GACf,cAAc,KAAK,MAAM,eAAe,IAAK,GAAG;GACjD,CAAC;;AAIJ,YAAW,MAAM,GAAG,MAAM,EAAE,eAAe,EAAE,aAAa;AAE1D,QAAO;EACL,UAAU,WAAW,MAAM,GAAG,MAAM;EACpC;EACA;EACD;;;;;ACvDH,SAAS,qBACP,OACO;CACP,MAAM,sBAAM,IAAI,KAAkC;CAClD,MAAM,0BAAU,IAAI,KAAa;CAEjC,SAAS,YAAY,MAAmC;EACtD,IAAI,IAAI,IAAI,IAAI,KAAK;AACrB,MAAI,CAAC,GAAG;AACN,uBAAI,IAAI,KAAK;AACb,OAAI,IAAI,MAAM,EAAE;;AAElB,UAAQ,IAAI,KAAK;AACjB,SAAO;;CAGT,IAAI,cAAc;AAElB,MAAK,MAAM,EAAE,aAAa,iBAAiB,OAAO;AAChD,MAAI,gBAAgB,YAAa;EAEjC,MAAM,OAAO,YAAY,YAAY;EACrC,MAAM,OAAO,YAAY,YAAY;AAGrC,OAAK,IAAI,cAAc,KAAK,IAAI,YAAY,IAAI,KAAK,EAAE;AACvD,OAAK,IAAI,cAAc,KAAK,IAAI,YAAY,IAAI,KAAK,EAAE;AACvD,iBAAe;;CAIjB,MAAM,yBAAS,IAAI,KAAqB;AACxC,MAAK,MAAM,CAAC,MAAM,cAAc,KAAK;EACnC,IAAI,IAAI;AACR,OAAK,MAAM,KAAK,UAAU,QAAQ,CAAE,MAAK;AACzC,SAAO,IAAI,MAAM,EAAE;;AAGrB,QAAO;EACL,OAAO,MAAM,KAAK,QAAQ;EAC1B;EACA;EACA;EACD;;;;;;AAOH,SAAS,cACP,OACA,WACA,YACS;CACT,MAAM,KAAK,IAAI,MAAM;AACrB,KAAI,OAAO,EAAG,QAAO;CAGrB,MAAM,qCAAqB,IAAI,KAAqB;CAEpD,MAAM,0CAA0B,IAAI,KAAqB;AAEzD,MAAK,MAAM,QAAQ,MAAM,OAAO;EAC9B,MAAM,IAAI,UAAU,IAAI,KAAK;AAC7B,qBAAmB,IAAI,IAAI,mBAAmB,IAAI,EAAE,IAAI,MAAM,MAAM,OAAO,IAAI,KAAK,IAAI,GAAG;;AAI7F,MAAK,MAAM,CAAC,MAAM,cAAc,MAAM,KAAK;EACzC,MAAM,KAAK,UAAU,IAAI,KAAK;AAC9B,OAAK,MAAM,CAAC,UAAU,WAAW,UAC/B,KAAI,UAAU,IAAI,SAAS,KAAK,GAC9B,yBAAwB,IAAI,KAAK,wBAAwB,IAAI,GAAG,IAAI,KAAK,OAAO;;AAKtF,MAAK,MAAM,CAAC,GAAG,MAAM,wBACnB,yBAAwB,IAAI,GAAG,IAAI,EAAE;CAGvC,IAAI,WAAW;CAEf,MAAM,WAAW,CAAC,GAAG,MAAM,MAAM;AACjC,MAAK,IAAI,IAAI,SAAS,SAAS,GAAG,IAAI,GAAG,KAAK;EAC5C,MAAM,IAAI,KAAK,MAAM,KAAK,QAAQ,IAAI,IAAI,GAAG;AAC7C,GAAC,SAAS,IAAI,SAAS,MAAM,CAAC,SAAS,IAAI,SAAS,GAAG;;AAGzD,MAAK,MAAM,QAAQ,UAAU;EAC3B,MAAM,cAAc,UAAU,IAAI,KAAK;EACvC,MAAM,KAAK,MAAM,OAAO,IAAI,KAAK,IAAI;EACrC,MAAM,YAAY,MAAM,IAAI,IAAI,KAAK,oBAAI,IAAI,KAAK;EAGlD,MAAM,+BAAe,IAAI,KAAqB;AAC9C,OAAK,MAAM,CAAC,UAAU,WAAW,WAAW;GAC1C,MAAM,KAAK,UAAU,IAAI,SAAS;AAClC,gBAAa,IAAI,KAAK,aAAa,IAAI,GAAG,IAAI,KAAK,OAAO;;AAI5D,qBAAmB,IAAI,cAAc,mBAAmB,IAAI,YAAY,IAAI,KAAK,GAAG;EACpF,MAAM,kBAAkB,aAAa,IAAI,YAAY,IAAI;AACzD,0BAAwB,IACtB,cACC,wBAAwB,IAAI,YAAY,IAAI,KAAK,gBACnD;EAGD,IAAI,WAAW;EACf,IAAI,YAAY;AAEhB,OAAK,MAAM,CAAC,eAAe,sBAAsB,cAAc;GAG7D,MAAM,QAAQ,oBAAoB,cAAc,MAF/B,mBAAmB,IAAI,cAAc,IAAI,MAEO;AACjE,OAAI,QAAQ,WAAW;AACrB,gBAAY;AACZ,eAAW;;;AAMf,YAAU,IAAI,MAAM,SAAS;AAC7B,qBAAmB,IAAI,WAAW,mBAAmB,IAAI,SAAS,IAAI,KAAK,GAAG;EAC9E,MAAM,eAAe,aAAa,IAAI,SAAS,IAAI;AACnD,0BAAwB,IACtB,WACC,wBAAwB,IAAI,SAAS,IAAI,KAAK,aAChD;AAED,MAAI,aAAa,YACf,YAAW;;AAIf,QAAO;;;;;AAMT,SAAS,kBACP,OACA,WACA,YACQ;CACR,MAAM,KAAK,IAAI,MAAM;AACrB,KAAI,OAAO,EAAG,QAAO;CAErB,IAAI,IAAI;AACR,MAAK,MAAM,CAAC,MAAM,cAAc,MAAM,KAAK;EACzC,MAAM,KAAK,UAAU,IAAI,KAAK;EAC9B,MAAM,KAAK,MAAM,OAAO,IAAI,KAAK,IAAI;AAErC,OAAK,MAAM,CAAC,UAAU,WAAW,UAC/B,KAAI,UAAU,IAAI,SAAS,KAAK,GAC9B,MAAK,SAAS,cAAc,MAAM,MAAM,OAAO,IAAI,SAAS,IAAI,MAAM;;AAI5E,QAAO,IAAI;;;;;AAMb,SAAS,WACP,OACA,YACqB;CAErB,MAAM,4BAAY,IAAI,KAAqB;CAC3C,IAAI,WAAW;AACf,MAAK,MAAM,QAAQ,MAAM,MACvB,WAAU,IAAI,MAAM,WAAW;CAIjC,MAAM,iBAAiB;AACvB,MAAK,IAAI,OAAO,GAAG,OAAO,gBAAgB,OAExC,KAAI,CADa,cAAc,OAAO,WAAW,WAAW,CAC7C;CAIjB,MAAM,4BAAY,IAAI,KAAqB;CAC3C,IAAI,WAAW;AACf,MAAK,MAAM,GAAG,MAAM,UAClB,KAAI,CAAC,UAAU,IAAI,EAAE,CACnB,WAAU,IAAI,GAAG,WAAW;AAGhC,MAAK,MAAM,CAAC,MAAM,MAAM,UACtB,WAAU,IAAI,MAAM,UAAU,IAAI,EAAE,CAAE;AAGxC,QAAO;;AAOT,MAAM,aAAa,IAAI,IAAI;CACzB;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAC/D;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAC/D;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAAO;CAC/D;CAAO;CAAO;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAC9D;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAChE;CAAQ;CAAQ;CAAQ;CAAQ;CAAQ;CAAS;CAAS;CAAQ;CAClE;CAAQ;CAAQ;CAAS;CAAQ;CAClC,CAAC;AAEF,SAAS,uBAAuB,QAAsC;CACpE,MAAM,6BAAa,IAAI,KAAqB;AAC5C,MAAK,MAAM,SAAS,QAAQ;AAC1B,MAAI,CAAC,MAAO;EACZ,MAAM,QAAQ,MACX,aAAa,CACb,QAAQ,gBAAgB,IAAI,CAC5B,MAAM,MAAM,CACZ,QAAQ,MAAM,EAAE,SAAS,KAAK,CAAC,WAAW,IAAI,EAAE,CAAC;AACpD,OAAK,MAAM,QAAQ,MACjB,YAAW,IAAI,OAAO,WAAW,IAAI,KAAK,IAAI,KAAK,EAAE;;AAIzD,QADe,CAAC,GAAG,WAAW,SAAS,CAAC,CAAC,MAAM,GAAG,MAAM,EAAE,KAAK,EAAE,GAAG,CAEjE,MAAM,GAAG,EAAE,CACX,KAAK,CAAC,OAAO,EAAE,CACf,KAAK,MAAM,IAAI;;AAGpB,SAAS,aAAa,MAAsB;CAC1C,MAAM,QAAQ,KAAK,QAAQ,IAAI;AAC/B,QAAO,UAAU,KAAK,OAAO,KAAK,MAAM,GAAG,MAAM;;;;;;AAWnD,eAAsB,kBACpB,SACA,MAC0B;CAC1B,MAAM,UAAU,MAAM,WAAW;CACjC,MAAM,iBAAiB,MAAM,kBAAkB;CAC/C,MAAM,aAAa,MAAM,cAAc;CAIvC,MAAM,QAAQ,qBADI,MAAM,QAAQ,mBAAmB,CACN;AAE7C,KAAI,MAAM,MAAM,WAAW,EACzB,QAAO;EACL,aAAa,EAAE;EACf,YAAY;EACZ,YAAY;EACZ,YAAY;EACb;CAIH,MAAM,eAAe,WAAW,OAAO,WAAW;CAGlD,MAAM,aAAa,kBAAkB,OAAO,cAAc,WAAW;CAGrE,MAAM,yBAAS,IAAI,KAAuB;AAC1C,MAAK,MAAM,CAAC,MAAM,SAAS,cAAc;EACvC,MAAM,MAAM,OAAO,IAAI,KAAK;AAC5B,MAAI,IAAK,KAAI,KAAK,KAAK;MAClB,QAAO,IAAI,MAAM,CAAC,KAAK,CAAC;;CAI/B,MAAM,WAAW,MAAM,QAAQ,kBAAkB;CACjD,MAAM,2BAAW,IAAI,KAA4B;AACjD,MAAK,MAAM,KAAK,SACd,UAAS,IAAI,EAAE,WAAW,EAAE,MAAM;CAIpC,MAAM,cAA2B,EAAE;CACnC,IAAI,SAAS;AAEb,MAAK,MAAM,GAAG,YAAY,QAAQ;AAChC,MAAI,QAAQ,SAAS,QAAS;EAE9B,MAAM,YAAY,IAAI,IAAI,QAAQ;EAElC,MAAM,QAAQ,uBADC,QAAQ,KAAK,MAAM,SAAS,IAAI,EAAE,IAAI,KAAK,CACd;EAG5C,MAAM,QAAyB,QAAQ,KAAK,SAAS;GACnD,MAAM,YAAY,MAAM,IAAI,IAAI,KAAK,oBAAI,IAAI,KAAK;GAClD,IAAI,cAAc;AAClB,QAAK,MAAM,CAAC,UAAU,WAAW,UAC/B,KAAI,UAAU,IAAI,SAAS,CAAE,gBAAe;AAE9C,UAAO;IACL;IACA,OAAO,SAAS,IAAI,KAAK,IAAI;IAC7B,gBAAgB;IACjB;IACD;AAGF,QAAM,MAAM,GAAG,MAAM,EAAE,iBAAiB,EAAE,eAAe;EAGzD,IAAI,gBAAgB;AACpB,OAAK,MAAM,QAAQ,MACjB,kBAAiB,KAAK;AAExB,mBAAiB;EACjB,MAAM,gBAAiB,QAAQ,UAAU,QAAQ,SAAS,KAAM;EAChE,MAAM,WAAW,gBAAgB,IAAI,KAAK,MAAO,gBAAgB,gBAAiB,IAAK,GAAG,MAAO;EAGjG,MAAM,+BAAe,IAAI,KAAqB;AAC9C,OAAK,MAAM,QAAQ,SAAS;GAC1B,MAAM,SAAS,aAAa,KAAK;AACjC,gBAAa,IAAI,SAAS,aAAa,IAAI,OAAO,IAAI,KAAK,EAAE;;EAE/D,MAAM,aAAa,CAAC,GAAG,aAAa,SAAS,CAAC,CAC3C,MAAM,GAAG,MAAM,EAAE,KAAK,EAAE,GAAG,CAC3B,MAAM,GAAG,EAAE,CACX,KAAK,CAAC,OAAO,EAAE;AAElB,cAAY,KAAK;GACf,IAAI;GACJ;GACA;GACA,MAAM,QAAQ;GACd;GACA;GACD,CAAC;;AAIJ,aAAY,MAAM,GAAG,MAAM,EAAE,OAAO,EAAE,KAAK;AAE3C,QAAO;EACL,aAAa,YAAY,MAAM,GAAG,eAAe;EACjD,YAAY,MAAM,MAAM;EACxB,YAAY,MAAM;EAClB,YAAY,KAAK,MAAM,aAAa,IAAM,GAAG;EAC9C"}