---
title: "MongoDB.local San Francisco 2026: Ship Production AI, Faster"
description: "MongoDB announced several features aimed at streamlining AI application development across its database and search ecosystem. The Voyage 4 embedding model family is now generally available, introducing cross-model compatibility within a shared embedding space alongside the video-capable voyage-multimodal-3.5 model. For in-database operations, MongoDB Community Edition introduced Automated Embedding in public preview to eliminate external synchronization pipelines for vector generation. Vector Search gained Lexical Prefilters, replacing the knnBeta operator with a dedicated vectorSearch operator and vector data type inside $search aggregations. Additionally, MongoDB made its intelligent assistant generally available in Compass and released the source code for its mongot search engine under the SSPL."
---

# MongoDB.local San Francisco 2026: Ship Production AI, Faster

[MongoDB](https://yomu.fyi/company/mongodb) · Peter Richards · Jan 15, 2026

**Type:** Announcement

## Summary

MongoDB announced several features aimed at streamlining AI application development across its database and search ecosystem. The Voyage 4 embedding model family is now generally available, introducing cross-model compatibility within a shared embedding space alongside the video-capable voyage-multimodal-3.5 model. For in-database operations, MongoDB Community Edition introduced Automated Embedding in public preview to eliminate external synchronization pipelines for vector generation. Vector Search gained Lexical Prefilters, replacing the knnBeta operator with a dedicated vectorSearch operator and vector data type inside $search aggregations. Additionally, MongoDB made its intelligent assistant generally available in Compass and released the source code for its mongot search engine under the SSPL.

## Context

Building production AI applications introduces operational overhead around managing separate vector infrastructure, maintaining conversational context, syncing data across external systems, and applying precise lexical filters alongside vector search.

## Approach / What changed

MongoDB introduced the Voyage 4 embedding family and voyage-multimodal-3.5, launched Automated Embedding in public preview for MongoDB Community Edition, introduced Lexical Prefilters using a vectorSearch operator in $search, made its intelligent assistant generally available in Compass and Atlas Data Explorer, and released the mongot search engine source code under the SSPL.

## Takeaways

- The Voyage 4 model family shares an embedding space for cross-model compatibility and includes voyage-4-nano on Hugging Face, alongside voyage-multimodal-3.5 with native video support.
- Lexical Prefilters for Vector Search replace the knnBeta operator by introducing a vector data type in $search index definitions and a vectorSearch operator in $search aggregations.
- Mongot, the standalone engine powering MongoDB Search and Vector Search alongside the core mongod process, is now publicly available under the SSPL in preview.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Open Source](https://yomu.fyi/topic/open-source), [Search](https://yomu.fyi/topic/search)

- Source: [MongoDB](https://www.mongodb.com/company/blog/events/mongodb-local-san-francisco-2026-ship-production-ai-faster)
- Source URL: https://www.mongodb.com/company/blog/events/mongodb-local-san-francisco-2026-ship-production-ai-faster
- Ingested by Yomu: 2026-08-27T15:04:31.321Z

[Read original post](https://www.mongodb.com/company/blog/events/mongodb-local-san-francisco-2026-ship-production-ai-faster)