We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm

submited by

Style Pass

2024-05-06 22:00:06

We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top of your game.

The rise of powerful large language models (LLMs) like GPT-4, Gemini 1.5 and Claude 3 has been a game-changer in AI and technology. With some models capable of processing over 1 million tokens, their ability to handle long contexts is truly impressive. However:

Retrieval-augmented generation (RAG) helps address these issues, but retrieval accuracy is a major bottleneck for end-to-end performance. One solution is integrating LLMs with big data through advanced SQL vector databases. This type of synergy between LLMs and big data not only makes LLMs more effective but also enables people to gain better intelligence from big data. Moreover, it further reduces model hallucination while providing data transparency and reliability.

As the cornerstone of RAG systems, vector databases have developed rapidly in the past year. They can generally be categorized into three types: dedicated vector databases, keyword and vector retrieval systems, and SQL vector databases. Each has advantages and limitations.