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.

31 janv. 2024

Code along with us onCode Along

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:

Learn about common business use cases for multi-modal search
Learn how to embed text, image, audio, and video data into the Weaviate vector database.
Learn how to search these mixed data types.

Additional Resources:

Sujets

Machine Learning

Artificial Intelligence

Data Scientist

Apparenté

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