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
