ChatGPT, the artificial intelligence chatbot from OpenAI, has taken the world by storm and has transformed the way we perform everyday tasks. With its advanced language processing capabilities, it has become a go-to solution for people looking for quick answers, whether it's about the weather, the news, or how to cook a recipe.
New open source plugins have made it possible for developers to easily build their own ChatGPT knowledge base solutions. At the heart of these solutions is a data model that converts unstructured data from text, videos, images and more into vector embeddings.
Vector databases are designed to efficiently store and retrieve these high-dimensional embeddings, making them an ideal solution for chat applications that require fast and accurate vector search results. Join Frank Liu, ML architect at Zilliz, for a session on why vector databases are critical to the success of these LLM-based chat solutions and how developers can build powerful chat solutions that deliver quick answers.
In this session, you'll learn:
- What is a vector database
- Why is it important to store your embeddings in a purpose-built database
- Why dumping your embeddings into Postgres is a bad idea
- How we built a chat knowledge base for open-source projects using Zilliz, prompts-as-code, and ChatGPT