Koduj razem z nami na
Code Along ↗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
Utwórz bezpłatne konto
Ćwicz i monitoruj swoje postępy.
Kontynuuj z GoogleKontynuując, akceptujesz nasz Warunki użytkowania, nasz Polityka prywatności i że Twoje dane są przechowywane w USA.Powiązane
podcast
The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
RIchie and Elan explore LLMs, vector databases and the best use-cases for them, semantic search, the tech stack for AI applications, emerging roles within the AI space, the future of vector databases and AI, and much more.
Richie Cotton · 36 min
Samouczek
Weaviate Tutorial: Unlocking the Power of Vector Search
Explore the functionalities of Weaviate, an open-source, real-time vector search engine, with our comprehensive beginner's guide.
Moez Ali · 11 min
code-along
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: