Reranker

Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.

Learn how to build better retrieval augmented generation (RAG) pipelines for LLMs, search, and recommendation. In this chapter we explore two-stage retrieval and the incredible accuracy of reranker models.

Explore how reranker for RAG systems by refining results, reducing hallucinations, and improving relevance and accuracy.

Breadcrumbs list

Learn how to fine-tune Cohere's reranker and generate synthetic data using DSPy!

It’s been a while since I last wrote a blog post, and I’m excited to be back!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface to all commonly used re-ranking approaches.