Evaluation metric: summarization

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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 "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on ...

Best for

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

Tools used

n8n-nodes-base.evaluation, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.evaluationtrigger, n8n-nodes-base.noop, @n8n/n8n-nodes-langchain.lmchatgooglegemini, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.chainllm

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: Evaluation metric: summarization
Workflow name: Evaluation metric: summarization

This n8n template demonstrates how to calculate the evaluation metric "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on an incoming Youtube transcript.

The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_summarization_quality

How it works

This evaluation works best for an AI summarization workflows.
For our scoring, we simple compare the generated response to the original transcript.
A key factor is to look out information in the response which is not mentioned in the documents.
A high score indicates LLM adherence and alignment whereas a low score could signal inadequate prompt or 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 - Is Evaluating?

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

Block 2 - OpenAI Chat Model

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

Block 3 - When fetching a dataset row

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

Block 4 - Respond to User

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

Block 5 - LLM

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 6 - Output

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

Block 7 - Set Outputs

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

Block 8 - Set Metrics

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

Block 9 - Download Transcript

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 10 - Summarise Agent

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

Block 11 - Extract from File

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

Block 12 - Webhook

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

Block 13 - Get Gdrive URL

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

Block 14 - Evaluate Summarisation

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

Block 15 - Sticky Note1

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

Block 16 - Sticky Note2

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

Block 17 - Sticky Note3

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

3. Summary Table

Workflow	Evaluation metric: summarization
Complexity	advanced
Nodes	17
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/4428/4428.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 Evaluation metric: summarization 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-nodes-base.evaluation @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.evaluationtrigger n8n-nodes-base.noop @n8n/n8n-nodes-langchain.lmchatgooglegemini @n8n/n8n-nodes-langchain.outputparserstructured n8n-nodes-base.googledrive @n8n/n8n-nodes-langchain.chainllm

Complexity

advanced

Published 27 May 2025

Likes 0

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Install path: /data/workflows/4428/4428.json

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Evaluation metric: summarization

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 "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on an incoming Youtube transcript.

How it works

Requirements

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Is Evaluating?

Block 2 - OpenAI Chat Model

Block 3 - When fetching a dataset row

Block 4 - Respond to User

Block 5 - LLM

Block 6 - Output

Block 7 - Set Outputs

Block 8 - Set Metrics

Block 9 - Download Transcript

Block 10 - Summarise Agent

Block 11 - Extract from File

Block 12 - Webhook

Block 13 - Get Gdrive URL

Block 14 - Evaluate Summarisation

Block 15 - Sticky Note1

Block 16 - Sticky Note2

Block 17 - Sticky Note3

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