chore: sync lockfile and payload-ai extensions for release v1.9.10

packages/memory-mcp/src/qdrant.ts

@@ -0,0 +1,110 @@
+import { pipeline, env } from '@xenova/transformers';
+import { QdrantClient } from '@qdrant/js-client-rest';
+
+// Be sure to set local caching options for transformers
+env.allowRemoteModels = true;
+env.localModelPath = './models';
+
+export class QdrantMemoryService {
+    private client: QdrantClient;
+    private collectionName = 'mcp_memory';
+    private embedder: any = null;
+
+    constructor(url: string = 'http://localhost:6333') {
+        this.client = new QdrantClient({ url });
+    }
+
+    /**
+     * Initializes the embedding model and the Qdrant collection
+     */
+    async initialize() {
+        // 1. Load the embedding model (using a lightweight model suitable for semantic search)
+        console.error('Loading embedding model...');
+        this.embedder = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+
+        // 2. Ensure collection exists
+        console.error(`Checking for collection: ${this.collectionName}`);
+        try {
+            const collections = await this.client.getCollections();
+            const exists = collections.collections.some(c => c.name === this.collectionName);
+
+            if (!exists) {
+                console.error(`Creating collection: ${this.collectionName}`);
+                await this.client.createCollection(this.collectionName, {
+                    vectors: {
+                        size: 384, // size for all-MiniLM-L6-v2
+                        distance: 'Cosine'
+                    }
+                });
+                console.error('Collection created successfully.');
+            }
+        } catch (e) {
+            console.error('Failed to initialize Qdrant collection:', e);
+            throw e;
+        }
+    }
+
+    /**
+     * Generates a vector embedding for the given text
+     */
+    private async getEmbedding(text: string): Promise<number[]> {
+        if (!this.embedder) {
+            throw new Error('Embedder not initialized. Call initialize() first.');
+        }
+        const output = await this.embedder(text, { pooling: 'mean', normalize: true });
+        return Array.from(output.data);
+    }
+
+    /**
+     * Stores a memory entry into Qdrant
+     */
+    async storeMemory(label: string, content: string): Promise<boolean> {
+        try {
+            const fullText = `${label}: ${content}`;
+            const vector = await this.getEmbedding(fullText);
+            const id = crypto.randomUUID();
+
+            await this.client.upsert(this.collectionName, {
+                wait: true,
+                points: [
+                    {
+                        id,
+                        vector,
+                        payload: {
+                            label,
+                            content,
+                            timestamp: new Date().toISOString()
+                        }
+                    }
+                ]
+            });
+            return true;
+        } catch (e) {
+            console.error('Failed to store memory:', e);
+            return false;
+        }
+    }
+
+    /**
+     * Retrieves memory entries relevant to the query
+     */
+    async retrieveMemory(query: string, limit: number = 5): Promise<Array<{ label: string, content: string, score: number }>> {
+        try {
+            const vector = await this.getEmbedding(query);
+            const searchResults = await this.client.search(this.collectionName, {
+                vector,
+                limit,
+                with_payload: true
+            });
+
+            return searchResults.map(result => ({
+                label: String(result.payload?.label || ''),
+                content: String(result.payload?.content || ''),
+                score: result.score
+            }));
+        } catch (e) {
+            console.error('Failed to retrieve memory:', e);
+            return [];
+        }
+    }
+}