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コードアロング ↗Building Multi-Modal Search with Weaviate
In this session, you'll learn how to use the Weaviate vector database to store these different content types, then perform search queries on them.
JP Hwang
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コードアロング
Vector Databases for Data Science with Weaviate in Python
In this code-along, JP shows you how to use Weaviate, a leading open source vector database, to build apps that can understand and manipulate them based on meaning.
JP Hwang
Search is essential in many places, from documents to shopping websites, to the internet itself. One of the great benefits of generative AI and vector databases is that search is no longer restricted to text. It is now possible to search audio, images, and video ("multi-modal") data. Many people are describing multi-modal search as the next big thing for 2024.
In this session, you'll learn how to use the Weaviate vector database to store these different content types, then perform search queries on them. You'll see how some simple Python code can be used to perform audio-to-image and image-to-text searches, and see how this fits into a data science workflow.
Key Takeaways: