Initial commit: Brainy - Multi-Dimensional AI Database

Open source vector database with HNSW indexing, graph relationships,
and metadata facets. Features CLI with professional augmentation registry
integration for discovering extensions and capabilities.

dist/examples/basicUsage.js

@@ -0,0 +1,118 @@
+/**
+ * Basic usage example for the Soulcraft Brainy database
+ */
+import { BrainyData } from '../brainyData.js';
+// Example data - word embeddings
+const wordEmbeddings = {
+    cat: [0.2, 0.3, 0.4, 0.1],
+    dog: [0.3, 0.2, 0.4, 0.2],
+    fish: [0.1, 0.1, 0.8, 0.2],
+    bird: [0.1, 0.4, 0.2, 0.5],
+    tiger: [0.3, 0.4, 0.3, 0.1],
+    lion: [0.4, 0.3, 0.2, 0.1],
+    shark: [0.2, 0.1, 0.7, 0.3],
+    eagle: [0.2, 0.5, 0.1, 0.4]
+};
+// Example metadata
+const metadata = {
+    cat: { type: 'mammal', domesticated: true },
+    dog: { type: 'mammal', domesticated: true },
+    fish: { type: 'fish', domesticated: false },
+    bird: { type: 'bird', domesticated: false },
+    tiger: { type: 'mammal', domesticated: false },
+    lion: { type: 'mammal', domesticated: false },
+    shark: { type: 'fish', domesticated: false },
+    eagle: { type: 'bird', domesticated: false }
+};
+/**
+ * Run the example
+ */
+async function runExample() {
+    console.log('Initializing vector database...');
+    // Create a new vector database
+    const db = new BrainyData();
+    await db.init();
+    console.log('Adding vectors to the database...');
+    // Add vectors to the database
+    const ids = {};
+    for (const [word, vector] of Object.entries(wordEmbeddings)) {
+        ids[word] = await db.add(vector, metadata[word]);
+        console.log(`Added "${word}" with ID: ${ids[word]}`);
+    }
+    console.log('\nDatabase size:', db.size());
+    // Search for similar vectors
+    console.log('\nSearching for vectors similar to "cat"...');
+    const catResults = await db.search(wordEmbeddings['cat'], 3);
+    console.log('Results:');
+    for (const result of catResults) {
+        const word = Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown';
+        console.log(`- ${word} (score: ${result.score.toFixed(4)}, metadata:`, result.metadata, ')');
+    }
+    // Search for similar vectors
+    console.log('\nSearching for vectors similar to "fish"...');
+    const fishResults = await db.search(wordEmbeddings['fish'], 3);
+    console.log('Results:');
+    for (const result of fishResults) {
+        const word = Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown';
+        console.log(`- ${word} (score: ${result.score.toFixed(4)}, metadata:`, result.metadata, ')');
+    }
+    // Update metadata
+    console.log('\nUpdating metadata for "bird"...');
+    await db.updateMetadata(ids['bird'], {
+        ...metadata['bird'],
+        notes: 'Can fly'
+    });
+    // Get the updated document
+    const birdDoc = await db.get(ids['bird']);
+    console.log('Updated bird document:', birdDoc);
+    // Delete a vector
+    console.log('\nDeleting "shark"...');
+    await db.delete(ids['shark']);
+    console.log('Database size after deletion:', db.size());
+    // Search again to verify shark is gone
+    console.log('\nSearching for vectors similar to "fish" after deletion...');
+    const fishResultsAfterDeletion = await db.search(wordEmbeddings['fish'], 3);
+    console.log('Results:');
+    for (const result of fishResultsAfterDeletion) {
+        const word = Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown';
+        console.log(`- ${word} (score: ${result.score.toFixed(4)}, metadata:`, result.metadata, ')');
+    }
+    console.log('\nExample completed successfully!');
+}
+// Check if we're in a browser or Node.js environment
+if (typeof window !== 'undefined') {
+    // Browser environment
+    document.addEventListener('DOMContentLoaded', () => {
+        const button = document.createElement('button');
+        button.textContent = 'Run BrainyData Example';
+        button.addEventListener('click', async () => {
+            const output = document.createElement('pre');
+            document.body.appendChild(output);
+            // Redirect console.log to the output element
+            const originalLog = console.log;
+            console.log = (...args) => {
+                originalLog(...args);
+                output.textContent +=
+                    args
+                        .map((arg) => typeof arg === 'object' ? JSON.stringify(arg, null, 2) : arg)
+                        .join(' ') + '\n';
+            };
+            try {
+                await runExample();
+            }
+            catch (error) {
+                console.error('Error running example:', error);
+            }
+            // Restore console.log
+            console.log = originalLog;
+        });
+        document.body.appendChild(button);
+    });
+}
+else {
+    // Node.js environment
+    runExample().catch((error) => {
+        console.error('Error running example:', error);
+    });
+}
+//# sourceMappingURL=basicUsage.js.map