feat(mcps): add kabelfachmann MCP with Kabelhandbuch integration and remove legacy PM2 orchestration

packages/kabelfachmann-mcp/src/qdrant.ts

@@ -0,0 +1,104 @@
+import { pipeline, env } from "@xenova/transformers";
+import { QdrantClient } from "@qdrant/js-client-rest";
+import crypto from "crypto";
+
+env.allowRemoteModels = true;
+env.localModelPath = "./models";
+
+export class QdrantMemoryService {
+  private client: QdrantClient;
+  private collectionName = "kabelfachmann";
+  private embedder: any = null;
+
+  constructor(
+    url: string = process.env.QDRANT_URL || "http://qdrant-mcp:6333",
+  ) {
+    this.client = new QdrantClient({ url });
+  }
+
+  async initialize() {
+    console.error("Loading embedding model...");
+    this.embedder = await pipeline(
+      "feature-extraction",
+      "Xenova/all-MiniLM-L6-v2",
+    );
+
+    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,
+            distance: "Cosine",
+          },
+        });
+        console.error("Collection created successfully.");
+      }
+    } catch (e) {
+      console.error("Failed to initialize Qdrant collection:", e);
+      throw e;
+    }
+  }
+
+  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);
+  }
+
+  async storeMemory(label: string, content: string): Promise<boolean> {
+    try {
+      const vector = await this.getEmbedding(content);
+      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;
+    }
+  }
+
+  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 [];
+    }
+  }
+}