Vector Database for Large Language Models in Production

Abstract:

Large Language Models (LLMs), such as ChatGPT, leverage the power of vector embeddings and databases to address the challenges posed by evolving data. Vector embeddings capture the essence of unstructured information. These embeddings, when combined with a vector database or search algorithm, offer a valuable means of retrieving contextually relevant data for a LLM.

By dynamically linking vector embeddings to specific information in the database, LLMs gain access to an up-to-date and ever-expanding knowledge base. This continuous updating process ensures that the LLMs remain capable of generating accurate and contextually appropriate outputs, even in the face of constantly changing information.

In summary, the integration of vector databases with LLMs allows these models to tap into a vast external source of knowledge. This combination empowers LLMs to adapt, provide accurate responses, and effectively handle data domains that require constant updates. This talk will detail how Redis provides such a solution for large langauge models with examples across domains.

Bio:

Sam Partee is a Principal Applied AI Engineer at Redis with backgrounds in Machine Learning and High Performance Computing + AI.