An Architect's vision

I n the context of reranking in Elastic, "refining" and "adjusting" refer to improving the initial search results to better meet the user's needs. Let's break down these terms: Refining Refining means enhancing the quality and accuracy of search results. This involves: Improving Relevance : By considering multiple relevance indicators, the system can better determine which documents are most pertinent to the query. For example, combining keyword relevance with vector similarity can provide a more nuanced understanding of which documents are truly relevant. Removing Noise : Initial search results might contain documents that are not very relevant. Refining helps to filter out less relevant results. This process helps in focusing on the most useful documents, enhancing the overall quality of the search results. Combining Strengths : Each search method has its own strengths. By refining, you can combine the best aspects of different search methodologies. For in

Reranking in Elastic refers to the process of refining and adjusting the initial search results by combining multiple result sets obtained from different search queries or methods, using techniques like Reciprocal Rank Fusion (RRF). The goal of reranking is to improve the relevance and quality of the final ranked list of documents by considering various relevance indicators from multiple searches. Reranking is a process that takes initial search results and reorders them to improve their relevance. Imagine you have a list of search results. Reranking looks at these results and adjusts their order to better match what you’re looking for. How Does Reranking Work? After the initial search, the system examines the results and applies additional criteria to reorder them. For example, it might combine results from multiple searches using a technique like Reciprocal Rank Fusion (RRF), which adjusts the ranking based on how documents are scored across different searches. This helps in pushing