rerank_service_skill

Overview

This skill is a document reranking service that refines retrieval results by scoring candidate documents with a deep learning reranker. It boosts Top-K accuracy by assigning fine-grained relevance scores and returning a reordered list or top indices. The service exposes simple client calls for full results, compact tuples, or just top indices.

How this skill works

The reranker takes a query and a set of candidate documents, computes relevance scores using a pretrained deep learning model, and sorts documents by score. It can return structured results with metadata, compact (index, score, text) tuples, or only the top document indices for downstream use. Responses include model id, per-item scores, and original document text.

When to use it

• After an initial retrieval step to improve precision of top results.
• When you need fine-grained relevance ordering for user-facing search results.
• To rerank candidates from vector or lexical search before ranking fusion.
• When downstream tasks depend on most-relevant documents (QA, summarization).
• To measure or improve Top-K accuracy during evaluation.

Best practices

• Feed a focused candidate set (dozens to a few hundred) rather than the entire corpus for cost and latency efficiency.
• Normalize or truncate long documents to avoid input length issues and maintain consistent scoring.
• Combine with a fast retriever (BM25 or vector search) to produce high-quality candidates before reranking.
• Use the returned relevance scores to threshold or merge results across sources.
• Cache reranker outputs for repeated queries to reduce repeated computation.

Example use cases

• Rerank top 100 vector-search results to surface the most relevant passages for a QA system.
• Improve search result ordering in an enterprise document portal for better user satisfaction.
• Select the top-N supporting documents to feed into a summarization or evidence-aggregation pipeline.
• Filter and rank multilingual or noisy retrieval outputs where lexical signals are weak.
• Evaluate model impact by comparing Top-K accuracy before and after reranking.