Principle:Marker Inc Korea AutoRAG API Based Passage Reranking
| Knowledge Sources | |
|---|---|
| Domains | RAG, Information_Retrieval, Passage_Reranking |
| Last Updated | 2026-02-12 14:00 GMT |
Overview
Technique that leverages cloud-based reranking APIs to re-score and reorder retrieved passages by relevance to a query, improving precision over initial retrieval scores.
Description
API-Based Passage Reranking delegates the relevance scoring of retrieved passages to an external cloud service (such as Cohere Rerank). After an initial retrieval step produces a candidate set of passages, the reranker sends each query-passage pair to the API, which returns a relevance score computed by a purpose-trained cross-encoder model. The passages are then reordered by these scores, and only the top-k most relevant are kept. This approach avoids the need for local GPU inference of cross-encoder models while achieving high reranking quality.
Usage
Use this principle when you need high-quality passage reranking without running local GPU inference. It is appropriate when you have access to a commercial reranking API (e.g., Cohere, Jina, Voyage AI, Mixedbread AI) and want to add a reranking stage to an AutoRAG pipeline. It trades API cost for computational simplicity and strong out-of-the-box multilingual support.
Theoretical Basis
Cross-encoder reranking computes a joint relevance score for a (query, passage) pair by processing both together through a transformer model:
# Abstract algorithm (NOT implementation code)
for each query q:
for each candidate passage p in retrieved_set(q):
score(q, p) = cross_encoder_model(concat(q, p))
reranked = sort(retrieved_set(q), key=score, descending=True)[:top_k]
Unlike bi-encoder retrieval (which scores query and passage embeddings independently), cross-encoders attend to both inputs jointly, capturing fine-grained token-level interactions. API-based rerankers host these models as a service, abstracting away model loading, batching, and GPU management.