Retrieving Texts based on Abstract Descriptions Explained!
This video explores a new paper exploring the use of summarization chains to represent long texts and use (original text, summary) pairs for optimizing text embeddings models! Here are 3 main takeaways I think everyone working with Weaviate may get value from: 1. Understanding of Summary Indexing and the Prompts (as well as Prompt Chains) used to build them. 2. Continued development of LLM-generated data for search -- creating (full text, summary) pairs gives you (1) data to build a summary index with as mentioned, (2) data to compare different embedding models with, and (3) data to train…
The skinny
The skinny isn't ready yet — notes appear once the transcript is processed.