Summary
After a deep exploration of the numerous technicalities and advanced methods involved in building CookBot, we can conclude that the application of RAG, ELSER, BM25, and RRF has significantly contributed to CookBot’s unique ability to answer culinary queries with enhanced precision and depth.
Throughout the course of this chapter, we’ve uncovered the potential of RAG as a retriever for finding relevant documents and as a generator for crafting detailed responses. By incorporating ELSER and BM25, the retrieval component gains the advantage of both semantic context and keyword efficiency. The fusion of these retrieval methods with RRF leads to the curation of a highly relevant set of recipes, even when faced with complex or vague queries.
The integration of RAG into CookBot’s architecture has further amplified its capabilities, demonstrating the value of an iterative approach where knowledge is refined over multiple steps. By employing GPT-4 as the generator...
