rag-with-rerank

Name: rag-with-rerank
Availability: InStock
Author: Zilliz

Add a cross-encoder reranking stage to RAG retrieval pipelines.

3|2|Updated Jan 29, 2026

Search Tags:#qa #rag #retrieval #vector-search #rerank #milvus #cross-encoder

Ready to run

npx skills add https://github.com/zilliztech/milvus-marketplace --skill rag-with-rerank

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-with-rerank
Source: https://github.com/zilliztech/milvus-marketplace/tree/main/plugins/rag-toolkit/skills/rag-with-rerank
Command: npx skills add https://github.com/zilliztech/milvus-marketplace --skill rag-with-rerank

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill improves RAG accuracy by adding a reranking stage to retrieval pipelines, enabling more precise results when exact relevance matters.

Core Features & Use Cases

Two-stage recall and rerank: Retrieve a broad set of candidates and apply a cross-encoder reranker to produce a short, high-quality result list.
Domain-robust scoring: Works across legal, medical, finance, and other precision-critical domains by allowing model and language choices per use case.
End-to-end pipeline generation: Integrates with generation components to deliver final answers after reranked candidates are scored.
Use Case: In a high-stakes Q&A setting, such as regulatory compliance or clinical guidelines, the reranker reduces irrelevant results and improves trust.

Quick Start

To begin, activate the skill and provide a query. The system will fetch candidates, rerank them, and generate an answer using the configured models.

Frequently Asked Questions about rag-with-rerank

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does reranking improve RAG retrieval accuracy?▼

Reranking improves RAG accuracy by applying a two-stage architecture: retrieve a broad candidate set, then use a cross-encoder to precisely score and rank results. This reduces irrelevant matches and surfaces the most relevant documents, especially critical in domains like legal, medical, and compliance where precision matters.

When should I add a reranking stage to my retrieval pipeline?▼

Add reranking when top-1 relevance is essential—legal discovery, medical guidelines, regulatory compliance, or finance. Use it when broad vector search retrieval returns mixed-quality candidates that need precise ranking before generation, trading retrieval speed for answer quality.

Can I configure how many results to retrieve and rerank?▼

Yes. Configure retrieve_k to set the initial candidate pool size and rerank_k to specify how many top-ranked results feed into generation. This lets you tune recall breadth and final result quality per use case.

Does reranking work with different languages and domains?▼

Yes. The cross-encoder reranker supports domain-specific model and language choices, making it adaptable to legal, medical, finance, and other precision-critical sectors with multilingual content.

How does the reranking pipeline connect to answer generation?▼

After reranking scores candidates, the top-ranked results flow into a generation component to produce final answers. This end-to-end integration ensures generated responses are grounded in the most relevant reranked documents.

What's the difference between vector search alone and reranking?▼

Vector search retrieves semantically similar candidates quickly but may miss exact relevance. Reranking uses a cross-encoder to apply fine-grained scoring after retrieval, catching nuanced relevance that vector embeddings miss—critical for high-stakes Q&A.