Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan

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Important notice

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

1. Workflow Overview

![Peliqan n8n chatbot with RAG and Text To SQL](https://images.spr.so/cdn cgi/imagedelivery/j42No7y dcokJuNgXeA0ig/99465134 7cb5 46cd a26c c44c11a3317e/Peliqan n8n AI Agent template with RAG and Te...

Best for

Internal Wiki automation workflows
AI Chatbot automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.memorybufferwindow

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan
Workflow name: Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan

How it works

This template is an end-to-end demo of a chatbot using business data from multiple sources (e.g. Notion, Chargebee, Hubspot etc.) with RAG + SQL.

Peliqan.io is used as a "cache" of all business data. Peliqan uses one-click ELT to sync all your business data to its built-in data warehouse, allowing for fast & accurate RAG and "Text to SQL" queries.

The workflow will write source data to Supabase as a vector store, for RAG searches by the chatbot. The source URL (e.g. the URL of a Notion page) is added in metadata.

The AI Agent will decide for each question to use either RAG or Text-to-SQL or a combination of both. Text-to-SQL is performed via the Peliqan node, added as a tool to the AI Agent. The question of the user in natural language is converted to an SQL query by the AI Agent. The query is executed by Peliqan.io on the source data and the result is interpreted by the AI Agent.

RAG is typically used to answer knowledge questions, often on non-structured data (Notion pages, Google Drive etc.). Text-to-SQL is typically used to answer analytical questions, for example "Show list of customers with number of open support tickets and add customer revenue based on invoiced amounts".

Preconditions

You signed up for a Peliqan.io free trial account
You have one or more data sources, e.g. a CRM, ERP, Accounting software, files, Notion, Google Drive etc.

Set up steps

Sign up for a free trial on peliqan.io: https://peliqan.io
Add one or more sources in Peliqan (e.g. Hubspot, Pipedrive...)
Copy your Peliqan API key under settings and use it here to add a Peliqan connection
Run the "RAG" workflow to feed Supabase, change the name of the table in the Peliqan node "Get table data".
Update the list of tables & columns that can be used for SQL in the System Message of the AI Agent.

Visit https://peliqan.io/n8n for more information.

Disclaimer: This template contains a community node and therefore only works for n8n self-hosted users.

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 chat message received

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

Block 2 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.6

Block 3 - OpenAI Chat Model

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

Block 4 - Embeddings OpenAI for RAG retrieval

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

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 - Sticky Note5

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

Block 8 - Sticky Note4

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

Block 9 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 10 - When clicking ‘Execute workflow’

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

Block 11 - Supabase Vector Store to store vectors for RAG

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

Block 12 - Get table data

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

Block 13 - Sticky Note

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

Block 14 - Sticky Note1

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

Block 15 - Sticky Note2

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 - Execute an SQL query via Peliqan

Type / Role: n8n-nodes-peliqan.peliqanTool - peliqanTool
Config choices: Version 1

Block 18 - Supabase Vector Store for search

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

Block 19 - Embeddings OpenAI for RAG

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

3. Summary Table

Workflow	Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan
Complexity	advanced
Nodes	19
Categories	Internal Wiki, AI Chatbot
Author	Peliqan
Published	30 Jul 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/6706/6706.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 Chatbot workflows

Frequently asked questions

What does Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan 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 Chatbot use case.

Peliqan

3 workflows

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter n8n-nodes-base.stickynote @n8n/n8n-nodes-langchain.memorybufferwindow

Complexity

advanced

Published 30 Jul 2025

Likes 0

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Install path: /data/workflows/6706/6706.json

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Query business data with OpenAI chatbot using RAG and text-to-SQL via Peliqan

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How it works

Preconditions

Set up steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When chat message received

Block 2 - AI Agent

Block 3 - OpenAI Chat Model

Block 4 - Embeddings OpenAI for RAG retrieval

Block 5 - Default Data Loader

Block 6 - Recursive Character Text Splitter

Block 7 - Sticky Note5

Block 8 - Sticky Note4

Block 9 - Simple Memory

Block 10 - When clicking ‘Execute workflow’

Block 11 - Supabase Vector Store to store vectors for RAG

Block 12 - Get table data

Block 13 - Sticky Note

Block 14 - Sticky Note1

Block 15 - Sticky Note2

Block 16 - Sticky Note3

Block 17 - Execute an SQL query via Peliqan

Block 18 - Supabase Vector Store for search

Block 19 - Embeddings OpenAI for RAG

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