What are MongoDB Atlas Search and Vector Search?

Atlas Search is a full-text search capability built directly into MongoDB Atlas, powered by Apache Lucene. It eliminates the need for a separate search infrastructure (like Elasticsearch) for many use cases. How it works: Atlas creates and maintains Lucene indexes on your MongoDB data in a synchronized Atlas Search node (local or on a separate dedicated tier). Changes to MongoDB documents are automatically propagated to the Lucene index via the oplog. Searches use the $search aggregation stage. $search example: db.products.aggregate([ { $search: { index: "default", text: { query: "wireless headphones", path: ["name", "description"], fuzzy: { maxEdits: 1 } } } }, { $limit: 10 }, { $project: { name: 1, score: { $meta: "searchScore" } } } ]). Atlas Search features: fuzzy matching, autocomplete, highlighting, facets, filters with AND/OR/NOT, relevance scoring, synonym support, phrase queries, wildcard, regex, date/geo range queries, language-specific analyzers (stemming, stop words). Atlas Vector Search (MongoDB 6.0+): store, index, and query vector embeddings for AI-powered semantic search, recommendations, and RAG (Retrieval Augmented Generation). Store embeddings as arrays in documents: { text: "...", embedding: [0.1, 0.3, ...] }. Create a vector index specifying the dimensions and similarity function (cosine, euclidean, dotProduct). Query with $vectorSearch stage: find documents with embeddings most similar to a query vector. Enables: semantic search, image similarity, recommendation systems, LLM memory/retrieval.