LLM¶

Building an LLM-based Reranker for your RAG pipeline

Are you struggling with irrelevant search results in your Retrieval-Augmented Generation (RAG) pipeline?

Imagine having a powerful tool that can intelligently reassess and reorder your search results, significantly improving their relevance to user queries.

In this blog post, we'll show you how to create an LLM-based reranker using Instructor and Pydantic. This approach will:

• Enhance the accuracy of your search results
• Leverage the power of large language models (LLMs)
• Utilize structured outputs for precise information retrieval

By the end of this tutorial, you'll be able to implement a llm reranker to label your synthetic data for fine-tuning a traditional reranker, or to build out an evaluation pipeline for your RAG system. Let's dive in!

Building a Pairwise LLM Judge with Instructor and Pydantic

In this blog post, we'll explore how to create a pairwise LLM judge using Instructor and Pydantic. This judge will evaluate the relevance between a question and a piece of text, demonstrating a practical application of structured outputs in language model interactions.

Introduction

Evaluating text relevance is a common task in natural language processing and information retrieval. By leveraging large language models (LLMs) and structured outputs, we can create a system that judges the similarity or relevance between a question and a given text.