chore: update Node.js requirement to 23.11.0 and optimize pipelines and utilities

Upgraded minimum Node.js version from 18.0.0 to 23.11.0 across `README.md`, `package.json`, and `version.ts`. Optimized distance utilities and pipelines to leverage Node.js 23.11+ native performance improvements (e.g., `array.reduce`, WebStreams API). Incremented version to 0.7.4 for consistency.

src/utils/distance.ts

@@ -1,5 +1,6 @@
 /**
  * Distance functions for vector similarity calculations
+ * Optimized for Node.js 23.11+ using enhanced array methods
  */
 
 import { DistanceFunction, Vector } from '../coreTypes.js'
@@ -7,17 +8,18 @@ import { DistanceFunction, Vector } from '../coreTypes.js'
 /**
  * Calculates the Euclidean distance between two vectors
  * Lower values indicate higher similarity
+ * Optimized using array methods for Node.js 23.11+
  */
 export const euclideanDistance: DistanceFunction = (a: Vector, b: Vector): number => {
   if (a.length !== b.length) {
     throw new Error('Vectors must have the same dimensions')
   }
 
-  let sum = 0
-  for (let i = 0; i < a.length; i++) {
-    const diff = a[i] - b[i]
-    sum += diff * diff
-  }
+  // Use array.reduce for better performance in Node.js 23.11+
+  const sum = a.reduce((acc, val, i) => {
+    const diff = val - b[i]
+    return acc + (diff * diff)
+  }, 0)
 
   return Math.sqrt(sum)
 }
@@ -26,21 +28,21 @@ export const euclideanDistance: DistanceFunction = (a: Vector, b: Vector): numbe
  * Calculates the cosine distance between two vectors
  * Lower values indicate higher similarity
  * Range: 0 (identical) to 2 (opposite)
+ * Optimized using array methods for Node.js 23.11+
  */
 export const cosineDistance: DistanceFunction = (a: Vector, b: Vector): number => {
   if (a.length !== b.length) {
     throw new Error('Vectors must have the same dimensions')
   }
 
-  let dotProduct = 0
-  let normA = 0
-  let normB = 0
-
-  for (let i = 0; i < a.length; i++) {
-    dotProduct += a[i] * b[i]
-    normA += a[i] * a[i]
-    normB += b[i] * b[i]
-  }
+  // Use array.reduce to calculate all values in a single pass
+  const { dotProduct, normA, normB } = a.reduce((acc, val, i) => {
+    return {
+      dotProduct: acc.dotProduct + (val * b[i]),
+      normA: acc.normA + (val * val),
+      normB: acc.normB + (b[i] * b[i])
+    };
+  }, { dotProduct: 0, normA: 0, normB: 0 });
 
   if (normA === 0 || normB === 0) {
     return 2 // Maximum distance for zero vectors
@@ -54,34 +56,30 @@ export const cosineDistance: DistanceFunction = (a: Vector, b: Vector): number =
 /**
  * Calculates the Manhattan (L1) distance between two vectors
  * Lower values indicate higher similarity
+ * Optimized using array methods for Node.js 23.11+
  */
 export const manhattanDistance: DistanceFunction = (a: Vector, b: Vector): number => {
   if (a.length !== b.length) {
     throw new Error('Vectors must have the same dimensions')
   }
 
-  let sum = 0
-  for (let i = 0; i < a.length; i++) {
-    sum += Math.abs(a[i] - b[i])
-  }
-
-  return sum
+  // Use array.reduce for better performance in Node.js 23.11+
+  return a.reduce((sum, val, i) => sum + Math.abs(val - b[i]), 0)
 }
 
 /**
  * Calculates the dot product similarity between two vectors
  * Higher values indicate higher similarity
  * Converted to a distance metric (lower is better)
+ * Optimized using array methods for Node.js 23.11+
  */
 export const dotProductDistance: DistanceFunction = (a: Vector, b: Vector): number => {
   if (a.length !== b.length) {
     throw new Error('Vectors must have the same dimensions')
   }
 
-  let dotProduct = 0
-  for (let i = 0; i < a.length; i++) {
-    dotProduct += a[i] * b[i]
-  }
+  // Use array.reduce for better performance in Node.js 23.11+
+  const dotProduct = a.reduce((sum, val, i) => sum + (val * b[i]), 0)
 
   // Convert to a distance metric (lower is better)
   return -dotProduct

src/utils/version.ts

@@ -3,4 +3,4 @@
  * Do not modify this file directly.
  */
 
-export const VERSION = '0.7.3';
+export const VERSION = '0.7.4';