# Automated Embedding in MongoDB Vector Search

MongoDB has released Automated Embedding in MongoDB Vector Search as a public preview in Community Edition: a developer chooses an embedding model while creating a vector search index, and text vectors are generated and kept in sync during initial data sync, document inserts and updates, and query processing. Previously developers called external embedding providers from application code before inserting data or running a vector query, and built custom pipelines to keep source data and vectors aligned on every insert, update or delete, plus an extra API call per query. The feature ships Voyage AI's voyage-4-large, voyage-4, voyage-4-lite and voyage-code-3 models and uses asynchronous dynamic batching that adjusts batch size to document shape.

MongoDB has released Automated Embedding in MongoDB Vector Search as a public preview in Community Edition: a developer chooses an embedding model while creating a vector search index, and text vectors are generated and kept in sync during initial data sync, document inserts and updates, and query processing. Previously developers called external embedding providers from application code before inserting data or running a vector query, and built custom pipelines to keep source data and vectors aligned on every insert, update or delete, plus an extra API call per query. The feature ships Voyage AI's voyage-4-large, voyage-4, voyage-4-lite and voyage-code-3 models and uses asynchronous dynamic batching that adjusts batch size to document shape.