feat: ai search

src/lib/qdrant.ts

@@ -0,0 +1,124 @@
+import { QdrantClient } from '@qdrant/js-client-rest';
+
+const qdrantUrl = process.env.QDRANT_URL || 'http://localhost:6333';
+const qdrantApiKey = process.env.QDRANT_API_KEY || '';
+
+export const qdrant = new QdrantClient({
+    url: qdrantUrl,
+    apiKey: qdrantApiKey || undefined,
+});
+
+export const COLLECTION_NAME = 'klz_products';
+export const VECTOR_SIZE = 1536; // OpenAI text-embedding-3-small
+
+/**
+ * Ensure the collection exists in Qdrant.
+ */
+export async function ensureCollection() {
+    try {
+        const collections = await qdrant.getCollections();
+        const exists = collections.collections.some(c => c.name === COLLECTION_NAME);
+        if (!exists) {
+            await qdrant.createCollection(COLLECTION_NAME, {
+                vectors: {
+                    size: VECTOR_SIZE,
+                    distance: 'Cosine',
+                },
+            });
+            console.log(`Successfully created Qdrant collection: ${COLLECTION_NAME}`);
+        }
+    } catch (error) {
+        console.error('Error ensuring Qdrant collection:', error);
+    }
+}
+
+/**
+ * Generate an embedding for a given text using OpenRouter (OpenAI embedding proxy)
+ */
+export async function generateEmbedding(text: string): Promise<number[]> {
+    const openRouterKey = process.env.OPENROUTER_API_KEY;
+    if (!openRouterKey) {
+        throw new Error('OPENROUTER_API_KEY is not set');
+    }
+
+    const response = await fetch('https://openrouter.ai/api/v1/embeddings', {
+        method: 'POST',
+        headers: {
+            'Authorization': `Bearer ${openRouterKey}`,
+            'Content-Type': 'application/json',
+            'HTTP-Referer': process.env.NEXT_PUBLIC_BASE_URL || 'https://klz-cables.com',
+            'X-Title': 'KLZ Cables Search AI',
+        },
+        body: JSON.stringify({
+            model: 'openai/text-embedding-3-small',
+            input: text,
+        }),
+    });
+
+    if (!response.ok) {
+        const errorBody = await response.text();
+        throw new Error(`Failed to generate embedding: ${response.status} ${response.statusText} ${errorBody}`);
+    }
+
+    const data = await response.json();
+    return data.data[0].embedding;
+}
+
+/**
+ * Upsert a product into Qdrant
+ */
+export async function upsertProductVector(id: string | number, text: string, payload: Record<string, any>) {
+    try {
+        await ensureCollection();
+        const vector = await generateEmbedding(text);
+
+        await qdrant.upsert(COLLECTION_NAME, {
+            wait: true,
+            points: [
+                {
+                    id: id,
+                    vector,
+                    payload,
+                }
+            ]
+        });
+    } catch (error) {
+        console.error('Error writing to Qdrant:', error);
+    }
+}
+
+/**
+ * Delete a product from Qdrant
+ */
+export async function deleteProductVector(id: string | number) {
+    try {
+        await ensureCollection();
+        await qdrant.delete(COLLECTION_NAME, {
+            wait: true,
+            points: [id] as [string | number],
+        });
+    } catch (error) {
+        console.error('Error deleting from Qdrant:', error);
+    }
+}
+
+/**
+ * Search products in Qdrant
+ */
+export async function searchProducts(query: string, limit = 5) {
+    try {
+        await ensureCollection();
+        const vector = await generateEmbedding(query);
+
+        const results = await qdrant.search(COLLECTION_NAME, {
+            vector,
+            limit,
+            with_payload: true,
+        });
+
+        return results;
+    } catch (error) {
+        console.error('Error searching in Qdrant:', error);
+        return [];
+    }
+}