Evaluate AI agent response relevance using OpenAI and cosine similarity

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

This n8n template demonstrates how to calculate the evaluation metric "Relevance" which in this scenario, measures the relevance of the agent's response to the user's question. The scoring approach...

Best for

Engineering automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.evaluationtrigger, n8n-nodes-base.set, n8n-nodes-base.evaluation, n8n-nodes-base.noop, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.outputparserstructured

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Jimleuk.

Original n8n.io source

1.1 Workflow description

Title: Evaluate AI agent response relevance using OpenAI and cosine similarity
Workflow name: Evaluate AI agent response relevance using OpenAI and cosine similarity

This n8n template demonstrates how to calculate the evaluation metric "Relevance" which in this scenario, measures the relevance of the agent's response to the user's question.

The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_relevance.py

How it works

This evaluation works best for Q&A agents.
For our scoring, we analyse the agent's response and ask another AI to generate a question from it. This generated question is then compared to the original question using cosine similarity.
A high score indicates relevance and the agent's successful ability to answer the question whereas a low score means agent may have added too much irrelevant info, went off script or hallucinated.

Requirements

n8n version 1.94+
Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - OpenAI Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 2 - When fetching a dataset row

Type / Role: n8n-nodes-base.evaluationTrigger - evaluationTrigger
Config choices: Version 4.6

Block 3 - Remap Input

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 4 - Evaluation

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 5 - Set Input Fields

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 6 - No Operation, do nothing

Type / Role: n8n-nodes-base.noOp - noOp
Config choices: Version 1

Block 7 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 8 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 9 - Update Output

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 10 - Update Metrics

Type / Role: n8n-nodes-base.evaluation - evaluation
Config choices: Version 4.6

Block 11 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 12 - Structured Output Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.2

Block 13 - Answer Relevance

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.7

Block 14 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 15 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 16 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 17 - Get Embeddings

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 18 - Calculate Similarity Score

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 19 - Questions to Items

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 20 - Calculate Relevance Score

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

3. Summary Table

Workflow	Evaluate AI agent response relevance using OpenAI and cosine similarity
Complexity	advanced
Nodes	20
Categories	Engineering, AI Summarization
Author	Jimleuk
Published	27 May 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/4425/4425.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI Summarization workflows

Frequently asked questions

What does Evaluate AI agent response relevance using OpenAI and cosine similarity do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.

Jimleuk

100 workflows

Nodes

@n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.evaluationtrigger n8n-nodes-base.set n8n-nodes-base.evaluation n8n-nodes-base.noop @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.outputparserstructured

Complexity

advanced

Published 27 May 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/4425/4425.json

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Evaluate AI agent response relevance using OpenAI and cosine similarity

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

This n8n template demonstrates how to calculate the evaluation metric "Relevance" which in this scenario, measures the relevance of the agent's response to the user's question.

How it works

Requirements

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - OpenAI Chat Model1

Block 2 - When fetching a dataset row

Block 3 - Remap Input

Block 4 - Evaluation

Block 5 - Set Input Fields

Block 6 - No Operation, do nothing

Block 7 - AI Agent

Block 8 - When chat message received

Block 9 - Update Output

Block 10 - Update Metrics

Block 11 - OpenAI Chat Model

Block 12 - Structured Output Parser

Block 13 - Answer Relevance

Block 14 - Sticky Note1

Block 15 - Sticky Note

Block 16 - Sticky Note3

Block 17 - Get Embeddings

Block 18 - Calculate Similarity Score

Block 19 - Questions to Items

Block 20 - Calculate Relevance Score

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions