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25min chapter
The Future of Search with Nils Reimers and Erika Cardenas - Weaviate Podcast #97!
Weaviate Podcast
Challenges and Solutions in Embeddings for Search Models
The chapter discusses the difficulties faced with embeddings in search models when dealing with longer texts and introduces Compass as a multi-aspect solution utilizing Dense Embeddings. It explores issues like loss of nuances in traditional search methods, the concept of chunking, technical aspects of producing multiple embeddings, and the importance of diverse representations for search accuracy. The conversation also touches on knowledge graphs, task-aware retrieval with instruct embeddings, advancements in multi-vector search like cross encoder re-rankers, and the use of metadata attributes for improving search results.
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