Weaviate Podcast #3: Vector search use cases, GraphQL API UX, multimodal models, and more...

Join ‪Connor Shorten and Bob van Luijt for the third Weaviate vector database Podcast. During the show, they will be discussing use cases, the GraphQL API, knowledge graphs, Weaviate as a product, vector database use cases, and a vision for the future of vector search. - Weaviate docs: https://link.semi.technology/3xy57fi - Weaviate Github: https://link.semi.technology/3EtTlGn - Weaviate slack: https://link.semi.technology/3d3x7iU - Weaviate newsletter: https://link.semi.technology/2ZS9ypI - SeMI Technologies: https://semi.technology - Weaviate: https://weaviate.io/ 0:00 Introduction 1:28 What was your vision? 4:50 How did you think of Graph QL Knowledge Graphs 7:33 Target data object by embedding 9:58 Weaviate as neurosymbolic system 17:05 The role of modules in the gap between human intuition and computer intuition. 22:26 Pretraining foundation models 29:25 General models vs fine-tuned models 34:20 Risotto Metaphor 36:14 Weaviate as a product and science platform 37:14 The exciting process with Wikidata 40:25 Do you have other examples of graph embeddings that inspire you? 43:53 How do we combine the Wikidata graph-structured embeddings with the Wikipedia text embeddings? 46:48 Scientific literature mining 47:14 Multimodal learning 52:24 Image Nearest Neighbor search with the metadata annotation 54:16 As a CEO and co-founder, what is your vision? - Weaviate docs: https://link.semi.technology/3xy57fi - Weaviate Github: https://link.semi.technology/3EtTlGn - Weaviate slack: https://link.semi.technology/3d3x7iU - Weaviate newsletter: https://link.semi.technology/2ZS9ypI - SeMI Technologies: https://semi.technology

Видео Weaviate Podcast #3: Vector search use cases, GraphQL API UX, multimodal models, and more... автора Python криптовалюты