by Tim Yang | December 9, 2025
This article provides a technical analysis of why legal case search is challenging in the legal services market. We examine the structural characteristics of legal case data and the limitations of existing distributed architectures (RDB + ElasticSearch + Vector DB), and explain why integrated search based on a single database is necessary.
Read Postby Tim Yang | July 11, 2024
On June 21, 2024, OpenAI announced the acquisition of database startup Rockset. According to OpenAI, the background of the Rockset acquisition is to improve search infrastructure to make AI more useful. Specifically, what advantages led OpenAI to acquire Rockset?
Read Postby Tim Yang | December 11, 2023
You can easily create RAG (Retrieval Augmented Generation) with just one AI database without complex infrastructure setup.
Read Postby Tim Yang | September 17, 2023
Methods to overcome the limitations of Large Language Models (LLMs) by utilizing Vector Databases (VectorDBs) are gaining attention. To provide accurate answers on specialized information such as law firm case precedents or company communication records—domain data that is not included in the training data—we can use a Vector Database that can convert, store, and search all kinds of data into vector embeddings, serving as a long-term memory storage for LLMs. To illustrate this, we examine a concrete case of how a vector database can complement an LLM through processes like data preprocessing, vectorization, storage, and search, using a Q&A system based on Wikipedia.
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