use klipy instead of tenor

src/services/ai/embeddings.ts

@@ -0,0 +1,144 @@
+/**
+ * Embedding service for semantic memory similarity
+ * Uses OpenAI-compatible embeddings API (can use OpenRouter or OpenAI directly)
+ */
+
+import OpenAI from "openai";
+import { config } from "../../core/config";
+import { createLogger } from "../../core/logger";
+
+const logger = createLogger("AI:Embeddings");
+
+// Embedding model to use (OpenRouter supports several embedding models)
+const EMBEDDING_MODEL = "openai/text-embedding-3-small";
+const EMBEDDING_DIMENSIONS = 1536;
+
+/**
+ * OpenRouter-based embedding provider
+ */
+class EmbeddingService {
+  private client: OpenAI;
+  private enabled: boolean;
+
+  constructor() {
+    this.client = new OpenAI({
+      baseURL: "https://openrouter.ai/api/v1",
+      apiKey: config.ai.openRouterApiKey,
+      defaultHeaders: {
+        "HTTP-Referer": "https://github.com/crunk-bun",
+        "X-Title": "Joel Discord Bot",
+      },
+    });
+    this.enabled = true;
+  }
+
+  /**
+   * Generate an embedding for a piece of text
+   */
+  async embed(text: string): Promise<number[] | null> {
+    if (!this.enabled) {
+      return null;
+    }
+
+    try {
+      const response = await this.client.embeddings.create({
+        model: EMBEDDING_MODEL,
+        input: text.slice(0, 8000), // Limit input length
+      });
+
+      const embedding = response.data[0]?.embedding;
+      
+      if (!embedding) {
+        logger.warn("No embedding returned from API");
+        return null;
+      }
+
+      logger.debug("Generated embedding", { 
+        textLength: text.length, 
+        dimensions: embedding.length 
+      });
+
+      return embedding;
+    } catch (error) {
+      // If embeddings fail, disable and log - don't crash
+      logger.error("Failed to generate embedding", error);
+      
+      // Check if it's a model not available error
+      const err = error as Error & { status?: number };
+      if (err.status === 404 || err.message?.includes("not available")) {
+        logger.warn("Embedding model not available, disabling embeddings");
+        this.enabled = false;
+      }
+      
+      return null;
+    }
+  }
+
+  /**
+   * Generate embeddings for multiple texts in batch
+   */
+  async embedBatch(texts: string[]): Promise<(number[] | null)[]> {
+    if (!this.enabled || texts.length === 0) {
+      return texts.map(() => null);
+    }
+
+    try {
+      const response = await this.client.embeddings.create({
+        model: EMBEDDING_MODEL,
+        input: texts.map(t => t.slice(0, 8000)),
+      });
+
+      return response.data.map(d => d.embedding);
+    } catch (error) {
+      logger.error("Failed to generate batch embeddings", error);
+      return texts.map(() => null);
+    }
+  }
+
+  /**
+   * Calculate cosine similarity between two embedding vectors
+   */
+  cosineSimilarity(a: number[], b: number[]): number {
+    if (a.length !== b.length) {
+      throw new Error("Embeddings 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];
+    }
+
+    normA = Math.sqrt(normA);
+    normB = Math.sqrt(normB);
+
+    if (normA === 0 || normB === 0) {
+      return 0;
+    }
+
+    return dotProduct / (normA * normB);
+  }
+
+  /**
+   * Check if embeddings are enabled and working
+   */
+  isEnabled(): boolean {
+    return this.enabled;
+  }
+}
+
+// Singleton instance
+let embeddingService: EmbeddingService | null = null;
+
+export function getEmbeddingService(): EmbeddingService {
+  if (!embeddingService) {
+    embeddingService = new EmbeddingService();
+  }
+  return embeddingService;
+}
+
+export { EmbeddingService, EMBEDDING_DIMENSIONS };