함께 코드 따라하기
코드 따라하기 ↗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
무료 계정 만들기
실습 문제하고 트랙하세요.
Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.관련
팟캐스트
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
튜토리얼
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
코딩 실습
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: