chore: recovery checkpoint - v3.0 API successfully recovered

CRITICAL CHECKPOINT - DO NOT PUSH TO GITHUB

Recovery Status:
- Successfully recovered brainy.ts from compiled JavaScript
- All core v3.0 API methods functional (add, get, update, delete, relate, find, etc.)
- Neural subsystem intact (562KB embedded patterns, NLP working)
- Augmentation pipeline operational (20+ augmentations)
- HNSW clustering system complete
- Triple Intelligence compiled (needs constructor fix)
- Test suite validates functionality

Changes preserved:
- 898 files with changes from last 3 days
- 144,475 insertions
- All augmentation improvements
- All test coverage enhancements
- Complete v3.0 feature set

This is a LOCAL checkpoint only - contains recovered work after corruption incident.
Created backup in .backups/brainy-full-20250910-151314.tar.gz

Branch: recovery-checkpoint-20250910-151433
Date: Wed Sep 10 03:18:04 PM PDT 2025

.recovery-workspace/dist-backup-20250910-141917/scripts/precomputePatternEmbeddings.d.ts

@@ -0,0 +1,19 @@
+#!/usr/bin/env node
+/**
+ * 🧠 Pre-compute Pattern Embeddings Script
+ *
+ * This script pre-computes embeddings for all patterns and saves them to disk.
+ * Run this once after adding new patterns to avoid runtime embedding costs.
+ *
+ * How it works:
+ * 1. Load all patterns from library.json
+ * 2. Use Brainy's embedding model to encode each pattern's examples
+ * 3. Average the example embeddings to get a robust pattern representation
+ * 4. Save embeddings to patterns/embeddings.bin for instant loading
+ *
+ * Benefits:
+ * - Pattern matching becomes pure math (cosine similarity)
+ * - No embedding model calls during query processing
+ * - Patterns load instantly with pre-computed vectors
+ */
+export {};

.recovery-workspace/dist-backup-20250910-141917/scripts/precomputePatternEmbeddings.js

@@ -0,0 +1,100 @@
+#!/usr/bin/env node
+/**
+ * 🧠 Pre-compute Pattern Embeddings Script
+ *
+ * This script pre-computes embeddings for all patterns and saves them to disk.
+ * Run this once after adding new patterns to avoid runtime embedding costs.
+ *
+ * How it works:
+ * 1. Load all patterns from library.json
+ * 2. Use Brainy's embedding model to encode each pattern's examples
+ * 3. Average the example embeddings to get a robust pattern representation
+ * 4. Save embeddings to patterns/embeddings.bin for instant loading
+ *
+ * Benefits:
+ * - Pattern matching becomes pure math (cosine similarity)
+ * - No embedding model calls during query processing
+ * - Patterns load instantly with pre-computed vectors
+ */
+import { BrainyData } from '../brainyData.js';
+import patternData from '../patterns/library.json' assert { type: 'json' };
+import * as fs from 'fs/promises';
+import * as path from 'path';
+async function precomputeEmbeddings() {
+    console.log('🧠 Pre-computing pattern embeddings...');
+    // Initialize Brainy with minimal config
+    const brain = new BrainyData({
+        storage: { forceMemoryStorage: true },
+        logging: { verbose: false }
+    });
+    await brain.init();
+    console.log('✅ Brainy initialized');
+    const embeddings = {};
+    let processedCount = 0;
+    const totalPatterns = patternData.patterns.length;
+    for (const pattern of patternData.patterns) {
+        console.log(`\n📝 Processing pattern: ${pattern.id} (${++processedCount}/${totalPatterns})`);
+        console.log(`   Category: ${pattern.category}`);
+        console.log(`   Examples: ${pattern.examples.length}`);
+        // Embed all examples
+        const exampleEmbeddings = [];
+        for (const example of pattern.examples) {
+            try {
+                const embedding = await brain.embed(example);
+                exampleEmbeddings.push(embedding);
+                console.log(`   ✓ Embedded: "${example.substring(0, 50)}..."`);
+            }
+            catch (error) {
+                console.error(`   ✗ Failed to embed: "${example}"`, error);
+            }
+        }
+        if (exampleEmbeddings.length === 0) {
+            console.warn(`   ⚠️ No embeddings generated for pattern ${pattern.id}`);
+            continue;
+        }
+        // Average the embeddings for a robust representation
+        const avgEmbedding = averageVectors(exampleEmbeddings);
+        embeddings[pattern.id] = {
+            patternId: pattern.id,
+            embedding: avgEmbedding,
+            examples: pattern.examples,
+            averageMethod: 'arithmetic_mean'
+        };
+        console.log(`   ✅ Generated ${avgEmbedding.length}-dimensional embedding`);
+    }
+    // Save embeddings to file
+    const outputPath = path.join(process.cwd(), 'src', 'patterns', 'embeddings.json');
+    await fs.writeFile(outputPath, JSON.stringify(embeddings, null, 2));
+    console.log(`\n✅ Saved ${Object.keys(embeddings).length} pattern embeddings to ${outputPath}`);
+    // Calculate storage size
+    const stats = await fs.stat(outputPath);
+    console.log(`📊 File size: ${(stats.size / 1024).toFixed(2)} KB`);
+    // Print statistics
+    console.log('\n📈 Embedding Statistics:');
+    console.log(`   Total patterns: ${totalPatterns}`);
+    console.log(`   Successfully embedded: ${Object.keys(embeddings).length}`);
+    console.log(`   Failed: ${totalPatterns - Object.keys(embeddings).length}`);
+    console.log(`   Embedding dimensions: ${Object.values(embeddings)[0]?.embedding.length || 0}`);
+    await brain.close();
+    console.log('\n✅ Complete!');
+}
+function averageVectors(vectors) {
+    if (vectors.length === 0)
+        return [];
+    const dim = vectors[0].length;
+    const avg = new Array(dim).fill(0);
+    // Sum all vectors
+    for (const vec of vectors) {
+        for (let i = 0; i < dim; i++) {
+            avg[i] += vec[i];
+        }
+    }
+    // Divide by count to get average
+    for (let i = 0; i < dim; i++) {
+        avg[i] /= vectors.length;
+    }
+    return avg;
+}
+// Run the script
+precomputeEmbeddings().catch(console.error);
+//# sourceMappingURL=precomputePatternEmbeddings.js.map

.recovery-workspace/dist-backup-20250910-141917/scripts/precomputePatternEmbeddings.js.map

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