feat: rename keep-notes to memento-note, migrate to PostgreSQL, fix MCP bugs

- Rename directory keep-notes -> memento-note with all code references
- Prisma: SQLite -> PostgreSQL (both app and MCP server schemas)
- Sync MCP schema with main app (add missing fields, relations, indexes)
- Delete 17 SQLite migrations (clean slate for PostgreSQL)
- Remove SQLite dependencies (@libsql/client, better-sqlite3, etc.)
- Fix MCP server: hardcoded Windows DB paths -> DATABASE_URL env var
- Fix MCP server: .dockerignore excluded index-sse.js (SSE mode broken)
- MCP Dockerfile: node:20 -> node:22
- Docker Compose: add postgres service, remove SQLite volume
- Generate favicon.ico, icon-192.png, icon-512.png, apple-icon.png
- Update layout.tsx icons and manifest.json for PNG icons
- Update all .env files for PostgreSQL
- Rewrite README.md with updated sections
- Remove mcp-server/node_modules and prisma/client-generated from git tracking

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

memento-note/lib/utils.ts

@@ -0,0 +1,207 @@
+import { clsx, type ClassValue } from "clsx"
+import { twMerge } from "tailwind-merge"
+import { LABEL_COLORS, LabelColorName, QueryType } from "./types"
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs))
+}
+
+export function getHashColor(name: string): LabelColorName {
+  let hash = 0;
+  for (let i = 0; i < name.length; i++) {
+    hash = name.charCodeAt(i) + ((hash << 5) - hash);
+  }
+  
+  const colors = Object.keys(LABEL_COLORS) as LabelColorName[];
+  // Skip 'gray' for colorful tags
+  const colorfulColors = colors.filter(c => c !== 'gray');
+  const colorIndex = Math.abs(hash) % colorfulColors.length;
+  
+  return colorfulColors[colorIndex];
+}
+
+export function cosineSimilarity(vecA: number[], vecB: number[]): number {
+  if (vecA.length !== vecB.length) return 0;
+
+  let dotProduct = 0;
+  let mA = 0;
+  let mB = 0;
+
+  for (let i = 0; i < vecA.length; i++) {
+    dotProduct += vecA[i] * vecB[i];
+    mA += vecA[i] * vecA[i];
+    mB += vecB[i] * vecB[i];
+  }
+
+  mA = Math.sqrt(mA);
+  mB = Math.sqrt(mB);
+
+  if (mA === 0 || mB === 0) return 0;
+
+  return dotProduct / (mA * mB);
+}
+
+/**
+ * Validate an embedding vector for quality issues
+ */
+export function validateEmbedding(embedding: number[]): { valid: boolean; issues: string[] } {
+  const issues: string[] = [];
+
+  // Check 1: Dimensionality > 0
+  if (!embedding || embedding.length === 0) {
+    issues.push('Embedding is empty or has zero dimensionality');
+    return { valid: false, issues };
+  }
+
+  // Check 2: Valid numbers (no NaN or Infinity)
+  let hasNaN = false;
+  let hasInfinity = false;
+  let hasZeroVector = true;
+
+  for (let i = 0; i < embedding.length; i++) {
+    const val = embedding[i];
+    if (isNaN(val)) hasNaN = true;
+    if (!isFinite(val)) hasInfinity = true;
+    if (val !== 0) hasZeroVector = false;
+  }
+
+  if (hasNaN) {
+    issues.push('Embedding contains NaN values');
+  }
+  if (hasInfinity) {
+    issues.push('Embedding contains Infinity values');
+  }
+  if (hasZeroVector) {
+    issues.push('Embedding is a zero vector (all values are 0)');
+  }
+
+  // Check 3: L2 norm is in reasonable range (0.7 to 1.2)
+  const l2Norm = calculateL2Norm(embedding);
+  if (l2Norm < 0.7 || l2Norm > 1.2) {
+    issues.push(`L2 norm is ${l2Norm.toFixed(3)} (expected range: 0.7-1.2)`);
+  }
+
+  return {
+    valid: issues.length === 0,
+    issues
+  };
+}
+
+/**
+ * Calculate L2 norm of a vector
+ */
+export function calculateL2Norm(vector: number[]): number {
+  let sum = 0;
+  for (let i = 0; i < vector.length; i++) {
+    sum += vector[i] * vector[i];
+  }
+  return Math.sqrt(sum);
+}
+
+/**
+ * Normalize an embedding to unit L2 norm
+ */
+export function normalizeEmbedding(embedding: number[]): number[] {
+  const norm = calculateL2Norm(embedding);
+  if (norm === 0) return embedding; // Can't normalize zero vector
+
+  return embedding.map(val => val / norm);
+}
+
+/**
+ * Calculate the RRF (Reciprocal Rank Fusion) constant k
+ *
+ * RRF Formula: score = Σ 1 / (k + rank)
+ *
+ * The k constant controls how much we penalize lower rankings:
+ * - Lower k (e.g., 20) penalizes low ranks more heavily
+ * - Higher k (e.g., 60) is more lenient with low ranks
+ *
+ * Adaptive formula: k = max(20, totalNotes / 10)
+ * - For small datasets (< 200 notes): k = 20 (strict)
+ * - For larger datasets: k scales linearly
+ *
+ * Examples:
+ * - 50 notes → k = 20
+ * - 200 notes → k = 20
+ * - 500 notes → k = 50
+ * - 1000 notes → k = 100
+ */
+export function calculateRRFK(totalNotes: number): number {
+  const BASE_K = 20;
+  const adaptiveK = Math.floor(totalNotes / 10);
+
+  return Math.max(BASE_K, adaptiveK);
+}
+
+/**
+ * Detect the type of search query to adapt search weights
+ *
+ * Detection rules:
+ * 1. EXACT: Query contains quotes (e.g., "Error 404")
+ * 2. CONCEPTUAL: Query starts with question words or is a phrase like "how to X"
+ * 3. MIXED: No specific pattern detected
+ *
+ * Examples:
+ * - "exact phrase" → 'exact'
+ * - "how to cook pasta" → 'conceptual'
+ * - "what is python" → 'conceptual'
+ * - "javascript tutorial" → 'mixed'
+ */
+export function detectQueryType(query: string): QueryType {
+  const trimmed = query.trim().toLowerCase();
+
+  // Rule 1: Check for quotes (exact match)
+  if ((query.startsWith('"') && query.endsWith('"')) ||
+      (query.startsWith("'") && query.endsWith("'"))) {
+    return 'exact';
+  }
+
+  // Rule 2: Check for conceptual patterns
+  const conceptualPatterns = [
+    /^(how|what|when|where|why|who|which|whose|can|could|would|should|is|are|do|does|did)\b/,
+    /^(how to|ways to|best way to|guide for|tips for|learn about|understand)/,
+    /^(tutorial|guide|introduction|overview|explanation|examples)/,
+  ];
+
+  for (const pattern of conceptualPatterns) {
+    if (pattern.test(trimmed)) {
+      return 'conceptual';
+    }
+  }
+
+  // Default: mixed search
+  return 'mixed';
+}
+
+/**
+ * Get search weight multipliers based on query type
+ *
+ * Returns keyword and semantic weight multipliers:
+ * - EXACT: Boost keyword matches (2.0x), reduce semantic (0.7x)
+ * - CONCEPTUAL: Reduce keyword (0.7x), boost semantic (1.5x)
+ * - MIXED: Default weights (1.0x, 1.0x)
+ */
+export function getSearchWeights(queryType: QueryType): {
+  keywordWeight: number;
+  semanticWeight: number;
+} {
+  switch (queryType) {
+    case 'exact':
+      return {
+        keywordWeight: 2.0,
+        semanticWeight: 0.7
+      };
+    case 'conceptual':
+      return {
+        keywordWeight: 0.7,
+        semanticWeight: 1.5
+      };
+    case 'mixed':
+    default:
+      return {
+        keywordWeight: 1.0,
+        semanticWeight: 1.0
+      };
+  }
+}