SDOF Knowledge Base

/** * Setup script for MongoDB database and vector search index */ import { MongoClient } from 'mongodb'; import dotenv from 'dotenv'; dotenv.config(); async function setupDatabase() { console.log('Setting up MongoDB database and vector search index...'); // Get MongoDB connection string from environment const uri = process.env.MONGODB_URI; if (!uri) { console.error('Error: MONGODB_URI environment variable is not set'); process.exit(1); } // Get database and collection names from environment const dbName = process.env.MONGODB_DB_NAME || 'sdof_knowledge'; const collectionName = process.env.MONGODB_COLLECTION || 'knowledge_entries'; console.log(`Using database: ${dbName}`); console.log(`Using collection: ${collectionName}`); let client: MongoClient | null = null; try { // Connect to MongoDB console.log('Connecting to MongoDB...'); client = new MongoClient(uri); await client.connect(); console.log('Connected to MongoDB'); // Get database and collection const db = client.db(dbName); const collection = db.collection(collectionName); // Check if the collection exists, create it if it doesn't const collections = await db.listCollections({ name: collectionName }).toArray(); if (collections.length === 0) { console.log(`Collection '${collectionName}' does not exist, creating it...`); await db.createCollection(collectionName); console.log(`Collection '${collectionName}' created`); } else { console.log(`Collection '${collectionName}' already exists`); } // Check if the vector search index exists const indexExists = await collection.indexExists('embedding_vector_index'); if (!indexExists) { console.log('Creating vector search index...'); // Use createIndex with any type to bypass TypeScript's strict typing // MongoDB Atlas supports vector search which may not be in the TypeScript definitions await (collection as any).createIndex( { embedding: "vector" }, { name: "embedding_vector_index", vectorDimension: 1536, // Default dimension for Claude/OpenAI embeddings vectorDistanceMetric: "cosine" } ); console.log('Vector search index created successfully'); } else { console.log('Vector search index already exists'); } console.log('Database setup completed successfully!'); } catch (error) { console.error('Error setting up database:', error); } finally { if (client) { await client.close(); console.log('Disconnected from MongoDB'); } process.exit(0); } } // Run the setup setupDatabase();