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 your own embedding model.3. Tournament style evaluation with human annotators -- the top 5 retrieved texts from one model are concatenated with the top 5 from another model, these 10 are given to human annotators to pick 5 and this is how the authors are reporting the performance of their models rather than traditional benchmarks. This m ay be a more productive evaluation technique for most real world search applications.Thank you so much for watching, here are some links mentioned in the video!Retrieving Texts based on Abstract Descriptions: https://arxiv.org/abs/2305.12517Weaviate Blog - Combining LangChain and Weaviate: https://weaviate.io/blog/combining-langchain-and-weaviateWeaviate Blog - Generative Feedback Loops: https://weaviate.io/blog/generative-feedback-loops-with-llmsJerry Liu in Llama Index Blog - A New Document Summary Index for LLM-powered QA Systems: https://medium.com/llamaindex-blog/a-new-document-summary-index-for-llm-powered-qa-systems-9a32ece2f9ecLearning to Retrieve Passages without Supervision (Spider): https://arxiv.org/pdf/2112.07708.pdfWeaviate Blog - Analysis of Spider - https://weaviate.io/blog/research-insights-spiderChapters