Evaluations metric: answer 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 "Similarity" which in this scenario, measures the consistency of the agent. The scoring approach is adapted from the open sourc...

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-nodes-base.stickynote

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: Evaluations metric: answer similarity
Workflow name: Evaluations metric: answer similarity

This n8n template demonstrates how to calculate the evaluation metric "Similarity" which in this scenario, measures the consistency of the agent.

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_similarity.py

How it works

This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation.
For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them.
A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination.

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 - Sticky Note1

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

Block 12 - Sticky Note

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

Block 13 - Sticky Note3

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

Block 14 - Get Embeddings

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

Block 15 - GroundTruth to Items

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

Block 16 - Get Embeddings1

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

Block 17 - Aggregate

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

Block 18 - Remap Embeddings

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

Block 19 - Remap Embeddings1

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

Block 20 - Create Embeddings Result

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.1

Block 21 - Calculate Similarity Score

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

3. Summary Table

Workflow	Evaluations metric: answer similarity
Complexity	advanced
Nodes	21
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/4423/4423.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 Evaluations metric: answer 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-nodes-base.stickynote

Complexity

advanced

Published 27 May 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/4423/4423.json

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Evaluations metric: answer 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 "Similarity" which in this scenario, measures the consistency of the agent.

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 - Sticky Note1

Block 12 - Sticky Note

Block 13 - Sticky Note3

Block 14 - Get Embeddings

Block 15 - GroundTruth to Items

Block 16 - Get Embeddings1

Block 17 - Aggregate

Block 18 - Remap Embeddings

Block 19 - Remap Embeddings1

Block 20 - Create Embeddings Result

Block 21 - Calculate Similarity 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