Shared Embedding Space: Optimizing AI Retrieval with Voyage 4

February 19
12 p.m. EST

Traditional embedding models force a compromise between accuracy, latency, and cost. You either pay for high-accuracy models or you settle for faster, cheaper models that miss critical semantic nuances. The Voyage 4 series changes this dynamic by introducing a shared embedding space, an industry-first capability that allows each model in the series to create embeddings within a common multi-dimensional space. This means you can finally optimize your document storage and your query latency independently, creating a RAG architecture that is as cost-effective as it is precise.

In this technical webinar, we will explore:

• Asymmetric retrieval: Learn how to search document embeddings generated by a flagship model using a lightweight query model—without any re-indexing.
• Mixture of Experts (MoE): Discover how the new voyage-4-large uses MoE architecture to deliver state-of-the-art accuracy at 40% lower cost than dense models.
• Multi-scale precision: See how Matryoshka learning and quantization allow you to reduce storage costs with minimal quality loss.
• End-to-end evaluation: Review the data from 29 datasets in the Retrieval Embedding Benchmark (RTEB) to see how these optimizations perform in the real world.

Join Apoorva Joshi, Senior AI Developer Advocate at MongoDB, for an educational session designed for architects and engineers who need to scale AI without breaking the bank. There will be a dedicated Q&A session to address your specific implementation challenges. Even if you can't join us live, register today to receive the full recording and evaluation results automatically once the session concludes.