This New Embedding Model Cuts Vector DB Costs by ~200x!
Last Updated on November 6, 2025 by Editorial Team
Author(s): Avi Chawla
Originally published on Towards AI.
It also outperforms OpenAI and Cohere models.
RAG is 80% retrieval and 20% generation.
This article discusses the challenges and solutions related to Retrieval-Augmented Generation (RAG) setups, particularly focusing on the new voyage-context-3 embedding model by MongoDB, which addresses retrieval issues through contextualized chunk embeddings, allowing for improved performance in various domains, ultimately showcasing its capabilities and practical applications in projects involving audio data processing.
Read the full blog for free on Medium.
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