# MongoDB Vector Search

MongoDB released and open-sourced an integration that turns MongoDB Atlas into the persistent memory store for AI applications and agents built with the Vercel AI SDK. The SDK itself ships no database, so an application previously started every conversation with no knowledge of the last one, and developers had to design schemas, write cleanup jobs and set up vector search themselves. The integration sorts memories into session, semantic, procedural, episodic and scratchpad types with separate retention policies, and creates vector indexes automatically while detecting embedding dimensions at startup.

MongoDB released and open-sourced an integration that turns MongoDB Atlas into the persistent memory store for AI applications and agents built with the Vercel AI SDK. The SDK itself ships no database, so an application previously started every conversation with no knowledge of the last one, and developers had to design schemas, write cleanup jobs and set up vector search themselves. The integration sorts memories into session, semantic, procedural, episodic and scratchpad types with separate retention policies, and creates vector indexes automatically while detecting embedding dimensions at startup.

MongoDB Atlas has moved Automated Embedding into Public Preview, so developers no longer have to run a parallel embedding pipeline to search their own data. Previously an edited document left a stale vector behind, and the fix was a human-written, human-scheduled backfill job, with hours rather than seconds between a data change and the index reflecting it; the Atlas version uses field-level delta detection to re-embed only when an indexed field actually changes, and splits index builds and live queries onto two inference mechanisms. On storage it offers configurable quantization, with scalar quantization roughly 4x smaller than float32 by default and binary quantization roughly 32x smaller.

MongoDB Atlas has moved Automated Embedding into Public Preview, so developers no longer have to run a parallel embedding pipeline to search their own data. Previously an edited document left a stale vector behind, and the fix was a human-written, human-scheduled backfill job, with hours rather than seconds between a data change and the index reflecting it; the Atlas version uses field-level delta detection to re-embed only when an indexed field actually changes, and splits index builds and live queries onto two inference mechanisms. On storage it offers configurable quantization, with scalar quantization roughly 4x smaller than float32 by default and binary quantization roughly 32x smaller.