You Don't Need Re-Ranking: Understanding the Superlinked Vector Layer

Summary

When it comes to vector search, it's not just about matching words. Understanding the meaning behind them is equally important. But there are challenges. Sometimes, factors like text meaning, popularity, and recency can lead to results that aren't quite right. This is because vector search isn't always perfect at making precise matches. To fix this, many systems use a technique called re-ranking. This involves using a separate model that processes both the query and the initial results together to reorder them based on their relevance to the query. However, this process comes with its own set of problems. It takes up a lot of computing power and slows things down, especially when working with large datasets. Now, imagine if your search infrastructure was smarter even before you hit the database. The key idea here is that with Superlinked, your search system can understand what you want and adjust accordingly. This reduces the need for re-ranking altogether. Superlinked improves search results by embedding multiple signals directly into the search index. This makes the results more relevant, faster, and more efficient, all without the need for extra steps. This article discusses how Superlinked eliminates the need for re-ranking by embedding multiple signals directly into unified vector spaces. Understanding vector re-ranking in modern search systems Vector re-ranking improves initial search results by adding a secondary scoring process. It uses neural networks or cross-encoders to prioritize documents that are most relevant to the context. Unlike the initial vector search, which converts documents into vectors before querying, re-ranking processes the query and documents together. It then reorders the results more precisely based on relevance. This method is commonly used in Retrieval-Augmented Generation (RAG) pipelines and semantic search systems to fill the gaps of traditional retrieval methods like vector similarity search. However, as with everything, reranking...