[Add] Synaptic AI Pro

https://assetstore.unity.com/packages/tools/generative-ai/synaptic-ai-pro-natural-language-control-for-unity-336030

Assets/Synaptic AI Pro/MCPServer/utils/embedding.js

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+// utils/embedding.js - OpenAI Embedding API integration for tool similarity search
+
+import OpenAI from 'openai';
+
+const openai = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY || ''
+});
+
+// In-memory cache for embeddings
+const embeddingCache = new Map();
+
+/**
+ * Get embedding vector for text using OpenAI API
+ * @param {string} text - Text to embed
+ * @returns {Promise<number[]>} - Embedding vector (1536 dimensions)
+ */
+export async function getEmbedding(text) {
+    if (!text || text.trim().length === 0) {
+        console.warn('[Embedding] Empty text provided, returning zero vector');
+        return new Array(1536).fill(0);
+    }
+
+    // Check cache first
+    const cacheKey = text.toLowerCase().trim();
+    if (embeddingCache.has(cacheKey)) {
+        return embeddingCache.get(cacheKey);
+    }
+
+    try {
+        if (!process.env.OPENAI_API_KEY) {
+            console.warn('[Embedding] No OpenAI API key, using zero vector fallback');
+            return new Array(1536).fill(0);
+        }
+
+        const response = await openai.embeddings.create({
+            model: 'text-embedding-3-small',
+            input: text,
+        });
+
+        const embedding = response.data[0].embedding;
+
+        // Cache the result
+        embeddingCache.set(cacheKey, embedding);
+
+        return embedding;
+    } catch (error) {
+        console.error('[Embedding] Error:', error.message);
+        // Fallback: return zero vector
+        return new Array(1536).fill(0);
+    }
+}
+
+/**
+ * Calculate cosine similarity between two vectors
+ * @param {number[]} vecA - First vector
+ * @param {number[]} vecB - Second vector
+ * @returns {number} - Similarity score (0-1)
+ */
+export function cosineSimilarity(vecA, vecB) {
+    if (!vecA || !vecB || vecA.length !== vecB.length) {
+        return 0;
+    }
+
+    let dotProduct = 0;
+    let normA = 0;
+    let normB = 0;
+
+    for (let i = 0; i < vecA.length; i++) {
+        dotProduct += vecA[i] * vecB[i];
+        normA += vecA[i] * vecA[i];
+        normB += vecB[i] * vecB[i];
+    }
+
+    const denominator = Math.sqrt(normA) * Math.sqrt(normB);
+
+    if (denominator === 0) {
+        return 0;
+    }
+
+    return dotProduct / denominator;
+}
+
+/**
+ * Clear embedding cache (useful for testing)
+ */
+export function clearCache() {
+    embeddingCache.clear();
+}
+
+/**
+ * Get cache statistics
+ */
+export function getCacheStats() {
+    return {
+        size: embeddingCache.size,
+        keys: Array.from(embeddingCache.keys())
+    };
+}