Reranker Node

Overview

The Reranker Node optimizes document retrieval by reordering a set of documents based on their relevance to a specific query. It uses specialized reranking models to improve the accuracy and relevance of search results by considering semantic similarity and contextual information.

Usage cost: 1 credit

Configuration

Settings

1. Model Selection

   • Reranker Model*: Select the reranking model to use

   • Top K: Number of documents to return after reranking (default: 3)

   • Query*: The search query used to rerank documents

   • Documents*: List of documents to be reranked

Output Ports

• reranked_documents (Document[]): Array of reranked documents ordered by relevance

Best Practices

1. Document Preparation

   • Keep document segments concise and focused

   • Ensure documents contain meaningful content

   • Remove duplicate or near-duplicate content

2. Query Optimization

   • Use specific, targeted queries

   • Include key terms and concepts

   • Consider query expansion when needed

   • Use consistent query formatting

3. Performance Tuning

   • Adjust Top K based on use case requirements

   • Consider document batch size

4. Integration Tips

   • Place after retrieval nodes

   • Connect to document transformation nodes when needed