Save Costs In RAG Workflows using the Q&A Tool With Multiple Models

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Important notice

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

1. Workflow Overview

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents throug...

Best for

Internal Wiki automation workflows
AI RAG automation workflows
intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.formtrigger, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.vectorstoreinmemory, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.toolvectorstore

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Save Costs In RAG Workflows using the Q&A Tool With Multiple Models
Workflow name: Save Costs In RAG Workflows using the Q&A Tool With Multiple Models

This template shows how to use the Question and Answer tool to save costs in RAG use cases.

Who is this for?

This template is for everyone who wants to start giving knowledge to their Agents through RAG.

Requirements

Have a PDF with custom knowledge that you want to provide to your agent.

Setup

No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting.

How to customize this to your needs

Add custom instructions to your Agent by changing the prompts in it.
Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table.
Describe your data properly in the Q&A tool
Exchange the Simple Vector Store nodes with your own vector store tools ready for production.
Add a more sophisticated way to rank files found in the vector store.

For more information read our docs on RAG in n8n.

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 - Upload your file here

Type / Role: n8n-nodes-base.formTrigger - formTrigger
Config choices: Version 2.2

Block 2 - Embeddings OpenAI

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

Block 3 - Default Data Loader

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

Block 4 - Sticky Note

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

Block 5 - Sticky Note1

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

Block 6 - Insert Data to Store

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

Block 7 - Query Data Tool

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

Block 8 - AI Agent

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

Block 9 - When chat message received

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

Block 10 - Sticky Note2

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

Block 11 - Answer questions with a vector store

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

Block 12 - Expensive model

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

Block 13 - Cheap Model

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

3. Summary Table

Workflow	Save Costs In RAG Workflows using the Q&A Tool With Multiple Models
Complexity	intermediate
Nodes	13
Categories	Internal Wiki, AI RAG
Author	Niklas Hatje
Published	17 Jun 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/5011/5011.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 Internal Wiki workflows AI RAG workflows

Frequently asked questions

What does Save Costs In RAG Workflows using the Q&A Tool With Multiple Models 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 Internal Wiki, AI RAG use case.

Niklas Hatje

13 workflows

Nodes

n8n-nodes-base.formtrigger @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader n8n-nodes-base.stickynote @n8n/n8n-nodes-langchain.vectorstoreinmemory @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.toolvectorstore

Complexity

intermediate

Published 17 Jun 2025

Likes 0

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Install path: /data/workflows/5011/5011.json

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Save Costs In RAG Workflows using the Q&A Tool With Multiple Models

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Who is this for?

Requirements

Setup

How to customize this to your needs

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Upload your file here

Block 2 - Embeddings OpenAI

Block 3 - Default Data Loader

Block 4 - Sticky Note

Block 5 - Sticky Note1

Block 6 - Insert Data to Store

Block 7 - Query Data Tool

Block 8 - AI Agent

Block 9 - When chat message received

Block 10 - Sticky Note2

Block 11 - Answer questions with a vector store

Block 12 - Expensive model

Block 13 - Cheap Model

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