Evaluate AI Agent Response Relevance using OpenAI and Cosine Similarity

Introduction

This n8n template demonstrates how to calculate the AI agent’s response relevance to a user’s question by employing cosine similarity and an evaluation metric adapted from the open-source RAGAS project. Designed primarily for Q&A AI agents, the template automates the process of analyzing the relevance of agent responses by generating a question from the response and comparing it to the original user query. A high cosine similarity score signifies a relevant and accurate answer, while a low score points to irrelevant or hallucinated content. This template requires n8n version 1.94 or higher and leverages advanced natural language processing techniques to streamline AI evaluation.

Key Benefits

• Automated and objective assessment of AI response relevance
• Improves quality control for Q&A AI agents
• Leverages open-source RAGAS scoring methodology
• Facilitates continuous monitoring of AI accuracy
• Easy integration within n8n workflows

Ideal For

• AI Trainers
• Data Scientists
• Machine Learning Engineers
• Product Managers overseeing AI tools
• QA Analysts working with conversational AI

Relevant Industries

• Artificial Intelligence
• Software Development
• Customer Support Automation
• Research and Development
• Education Technology

Included Products

• n8n (Automation Platform)

Alternative Products

• Automation Platforms: Make, Zapier
• AI Evaluation Tools: Hugging Face, OpenAI API

Expansion Options

• Integrate with external AI platforms for response generation like OpenAI GPT models
• Expand evaluation to multi-turn conversations beyond single Q&A pairs
• Add sentiment analysis or bias detection alongside relevance scoring
• Build dashboards for ongoing monitoring and visualization of relevance metrics
• Automate feedback loops to retrain AI agents based on low relevance scores