Cognica Blog

Exploring how function-based distributed database architectures become structural constraints in the AI era. We examine the limitations and complexity of traditional approaches combining OLTP, OLAP, FTS, and Vector DB, and introduce Cognica's unified database as a technical turning point.

Moving beyond the complexity and limitations of the era of specialized databases, we explore why a unified database is now essential. Discover the value of Cognica's integrated database that provides OLTP, OLAP, cache, FTS, and vector search in a single engine, and the future direction of databases in the AI era.

We discuss why NOT operations are difficult in vector search.

Explains the limitations and characteristics of vector embeddings and covers the improvements made to store them.

Explains how to build a natural language search service by applying vector search to a case law search demo using FTS.

You can easily create RAG (Retrieval Augmented Generation) with just one AI database without complex infrastructure setup.