Vector Embedding at Scale
Scaling Large Language Models (LLMs) requires more than just compute; it requires a data warehouse that can handle billions of vector embeddings with millisecond latency.
The Hybrid Storage Approach
We combine traditional relational data with high-dimensional vector stores to provide a unified context for RAG (Retrieval-Augmented Generation) applications.