MongoDB Atlas Vector Search provides native vector similarity search within the MongoDB document database platform. Available in Atlas (cloud) across all tiers including M0 free tier with limited vector capabilities. Full vector search requires M10+ clusters. The feature enables storing embeddings alongside document data with combined queries—filter by document fields and rank by vector similarity in single aggregation pipeline. Uses hierarchical navigable small world (HNSW) algorithm for approximate nearest neighbor search. Supports up to 4096 dimensions with float32 precision. Integration with LangChain, LlamaIndex, and major ML frameworks. For text-to-SQL contexts: natural fit for applications already using MongoDB that need semantic search—no separate vector database required. Key advantage is operational simplicity—single database for application data and AI features. Page should cover: vector index configuration, aggregation pipeline syntax for vector search, free tier limitations, LangChain integration, comparison with dedicated vector databases, pricing impact of vector search, and use cases for MongoDB-native AI features.