Vector reranking is a technique used to improve the quality of search results by reordering an initial set of candidate items based on a more precise relevance calculation. It typically follows an initial retrieval step, where a system uses a fast but approximate method (like approximate nearest neighbor search) to fetch a broad set of potential matches from a vector database. Reranking then applies a slower, more accurate model to score and reorder these candidates, ensuring the final results better align with the user’s intent. This two-step approach balances speed and accuracy: the initial retrieval quickly narrows down candidates, while reranking refines them for precision.
You should apply vector reranking in scenarios where the initial search results lack sufficient relevance or context. For example, in e-commerce search, a query like “waterproof hiking boots under $100” might return items based on keyword matches or basic vector similarity. However, the initial search could miss nuances like price filters, material properties, or user intent (e.g., prioritizing “waterproof” over “water-resistant”). Reranking with a model trained on user behavior or product attributes can better weigh these factors. Similarly, in document retrieval, a semantic search might fetch broadly related articles, but reranking can prioritize documents that address specific subtopics mentioned in the query, like technical details or recent updates. Another use case is chatbots, where reranking helps select the most contextually appropriate responses from a pool of candidates generated by a language model.
The decision to use reranking depends on trade-offs between latency, computational cost, and accuracy. If your application requires real-time responses (e.g., autocomplete suggestions), the added latency from reranking might be prohibitive. However, for tasks where precision is critical—such as legal document lookup, medical information retrieval, or personalized recommendations—reranking is worth the cost. Implement it by first retrieving a larger candidate set (e.g., 100–200 items) using a fast method, then applying a smaller, specialized model (like a cross-encoder or a fine-tuned transformer) to score and reorder the top 50–100 candidates. This approach ensures you don’t sacrifice speed while significantly improving result quality. For instance, platforms like search engines often use reranking to handle ambiguous queries, ensuring the top results align with the most likely interpretation of the user’s input.