What it is: 

Automated text embedding allows MongoDB Community users to create vector search indexes that automatically generate, store, and query text embeddings using Voyage AI models. This feature eliminates the need for manual embedding pipelines by managing the transformation of documents into vectors directly through a new autoEmbed field type in vectorSearch index definitions. Use with your favorite MongoDB language drivers, AI frameworks like LangChain and LangGraph, and the MongoDB MCP server.

Who it’s for: 

This is for MongoDB Community Edition developers who want to implement semantic search but lack the specialized machine learning infrastructure to manage external embedding generation. It specifically serves teams looking to rapidly build AI-native applications or migrate to the latest embedding models with minimal integration complexity.

Why it matters: 

The integration simplifies the developer workflow by replacing a multi-step, error-prone manual process with a single-click experience for semantic search. By handling vector synchronization and query embedding automatically, the product reduces maintenance overhead and accelerates the time to market for local and on-premises AI applications.

How to get started:
Automated Embedding in MongoDB Vector Search in Community Edition is available now, with MongoDB Atlas and MongoDB Vector Search in Enterprise Edition access coming soon. Jump in with our quick start guide.