Evaluate RAG response accuracy with OpenAI: document groundedness metric

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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 "RAG document groundedness" which in this scenario, measures the ability to provide or reference information included only in r...

Best for

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

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.httprequest, @n8n/n8n-nodes-langchain.vectorstoreinmemory, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai

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 RAG response accuracy with OpenAI: document groundedness metric
Workflow name: Evaluate RAG response accuracy with OpenAI: document groundedness metric

This n8n template demonstrates how to calculate the evaluation metric "RAG document groundedness" which in this scenario, measures the ability to provide or reference information included only in retrieved vector store documents.

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

How it works

This evaluation works best for an agent that requires document retrieval from a vector store or similar source.
For our scoring, we need to collect the agent's response and the documents retrieved and use an LLM to assess if the former is based off the latter.
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 - When clicking ‘Execute workflow’

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

Block 2 - Get Datasheet

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

Block 3 - Simple Vector Store

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

Block 4 - Embeddings OpenAI

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

Block 5 - Default Data Loader

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

Block 6 - Recursive Character Text Splitter

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

Block 7 - AI Agent

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

Block 8 - OpenAI Chat Model

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

Block 9 - Simple Vector Store1

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

Block 10 - Embeddings OpenAI1

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

Block 11 - When chat message received

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

Block 12 - Evaluation

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

Block 13 - No Operation, do nothing

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

Block 14 - Get Documents

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

Block 15 - When fetching a dataset row

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

Block 16 - Remap Input

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

Block 17 - OpenAI Chat Model1

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

Block 18 - Structured Output Parser

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

Block 19 - Document Grounding

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

Block 20 - Set Outputs

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

Block 21 - Set Metrics

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

Block 22 - Sticky Note

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

Block 23 - Sticky Note1

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

Block 24 - Sticky Note2

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

Showing the first 24 of 25 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Evaluate RAG response accuracy with OpenAI: document groundedness metric
Complexity	advanced
Nodes	25
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/4426/4426.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 RAG response accuracy with OpenAI: document groundedness metric 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.manualtrigger n8n-nodes-base.httprequest @n8n/n8n-nodes-langchain.vectorstoreinmemory @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai

Complexity

advanced

Published 27 May 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/4426/4426.json

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Evaluate RAG response accuracy with OpenAI: document groundedness metric

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 "RAG document groundedness" which in this scenario, measures the ability to provide or reference information included only in retrieved vector store documents.

How it works

Requirements

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking ‘Execute workflow’

Block 2 - Get Datasheet

Block 3 - Simple Vector Store

Block 4 - Embeddings OpenAI

Block 5 - Default Data Loader

Block 6 - Recursive Character Text Splitter

Block 7 - AI Agent

Block 8 - OpenAI Chat Model

Block 9 - Simple Vector Store1

Block 10 - Embeddings OpenAI1

Block 11 - When chat message received

Block 12 - Evaluation

Block 13 - No Operation, do nothing

Block 14 - Get Documents

Block 15 - When fetching a dataset row

Block 16 - Remap Input

Block 17 - OpenAI Chat Model1

Block 18 - Structured Output Parser

Block 19 - Document Grounding

Block 20 - Set Outputs

Block 21 - Set Metrics

Block 22 - Sticky Note

Block 23 - Sticky Note1

Block 24 - Sticky Note2

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