Hey everyone! Thank you so much for watching the 63rd Weaviate Podcast, I couldn't be more excited to welcome Nils Reimers back to the podcast!! Similar to our debut episode together, we began by describing the latest collaboration of Weaviate and Cohere (episode 1, new multilingual embedding models; episode 2, rerankers!), and then continued into some of the key questions around search technology. In this one, we discussed the importance of temporal queries and metadata extraction, long document representation, and future directions for Retrieval-Augmented Generation! I hope you enjoy the podcast, as always I am more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Thank you so much for watching! Learn more about Cohere Rerankers and how to use it in Weaviate here: https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/reranker-cohere Chapters 0:00 Introduction 1:30 Cohere Rerankers 7:02 Dataset Curation at Cohere 10:30 New Rerankers and XGBoost 14:35 Temporal Queries 17:55 Metadata Extraction from Unstructured Text Chunks 21:52 Soft Filters 24:58 Chunking and Long Document Representation 38:00 Retrieval-Augmented Generation 45:40 Retrieval-Aware Training to solve Hallucinations 49:50 Learning to Search and End-to-End RAG 54:35 RETRO 59:25 Foundation Model for Search