Vibeleaderboard
← All Intel
Intel / post

Voyage 4 shares one embedding space across large, lite and nano

Source
x.com
Date

Building production AI means balancing accuracy, cost, and deployment flexibility. Today, we're making that easier: ✨ Voyage 4 series: Shared embedding space lets you mix models across pipeline stages. Use voyage-4-large for indexing, voyage-4-lite for queries, and voyage-4-nano locally. 🎥 voyage-multimodal-3.5: Native video retrieval and the first production-grade video embedding model to support Matryoshka embeddings for flexible dimensionality. 🌍 New availability: Deploy on MongoDB Atlas, Google Cloud, Amazon Web Services (AWS), or Microsoft Azure. The most accurate models. Available everywhere you build. Read the full announcement: https://t.co/ZeAeEshaLG

Context

Voyage AI's Voyage 4 family allows different models to handle document indexing and incoming queries. Their numerical representations are compatible: a query encoded by voyage-4-lite can search documents encoded by voyage-4-large. The release explanation calls this a shared space.

That compatibility matters because documents may be indexed infrequently while searches arrive continuously. A team can use the larger model to represent its document collection, then choose a smaller query model to reduce recurring computation. It can later change the query model within the family without recalculating all the document representations, separating two costs that would otherwise be coupled.

Terms in this piece · Glossary
  • embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
  • multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
More from Voyage AI by MongoDB
Recommended reads
Comments

Checking sign-in…

Loading comments…