Build a RAG document chatbot with Supabase vector search and OpenRouter

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1. Workflow Overview

What this workflow does This workflow builds a Retrieval Augmented Generation (RAG) document chat assistant inside n8n using Supabase Vector Store and AI models. The workflow allows users to upload...

Best for

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

Tools used

n8n-nodes-base.stickynote, n8n-nodes-base.webhook, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenrouter, @n8n/n8n-nodes-langchain.vectorstoresupabase, n8n-nodes-base.respondtowebhook, n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.documentdefaultdataloader

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Build a RAG document chatbot with Supabase vector search and OpenRouter
Workflow name: Build a RAG document chatbot with Supabase vector search and OpenRouter

What this workflow does

This workflow builds a Retrieval-Augmented Generation (RAG) document chat assistant inside n8n using Supabase Vector Store and AI models.

The workflow allows users to upload documents, convert them into embeddings, store them inside Supabase pgvector, and query them through an AI chat interface using semantic search.

When a user sends a question through the webhook endpoint, the workflow retrieves the most relevant document chunks from Supabase and uses an AI model to generate a grounded response based on the uploaded documents.

This template includes:

Document ingestion pipeline
Recursive text chunking
AI embeddings generation
Supabase vector storage
Semantic retrieval
AI-powered document question answering
Webhook API integration for frontend apps

How it works

The workflow is split into two main parts:

Document ingestion pipeline

Downloads documents from Google Drive
Extracts text from PDFs
Splits text into smaller chunks
Generates embeddings using AI models
Stores embeddings inside Supabase pgvector

RAG chat pipeline

Receives user questions through a webhook
Searches Supabase vector database for relevant content
Retrieves matching document chunks
Sends retrieved context to the AI model
Returns grounded responses back to the frontend

Requirements

n8n instance
Supabase account with pgvector enabled
Google Drive account
AI provider credentials (OpenRouter, Gemini, or OpenAI)

Setup

Create a Supabase project and enable pgvector
Create the required documents table and match_documents function
Connect your Supabase credentials in n8n
Connect your AI model credentials
Add your Google Drive credentials
Upload your documents and run the ingestion workflow
Use the webhook endpoint to connect your frontend application

Setup typically takes around 15–30 minutes.

How to customize

You can customize this workflow by:

Switching AI providers (Gemini, OpenRouter, OpenAI)
Adjusting chunk size and retrieval count
Connecting your own frontend UI
Adding support for multiple document sources
Expanding the workflow into a multi-user knowledge assistant

This workflow is designed as a practical starting point for building AI-powered document assistants and RAG applications inside 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 - Sticky Note

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

Block 2 - Sticky Note1

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

Block 3 - Chat Request Webhook

Type / Role: n8n-nodes-base.webhook - webhook
Config choices: Version 2.1

Block 4 - RAG Document Assistant

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 5 - OpenRouter LLM

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

Block 6 - Supabase Vector Retriever

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

Block 7 - Return AI Response

Type / Role: n8n-nodes-base.respondToWebhook - respondToWebhook
Config choices: Version 1.5

Block 8 - Google Drive PDF Downloader

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

Block 9 - Supabase Vector Store

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

Block 10 - Document Loader

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

Block 11 - Gemini Embedding Model

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

Block 12 - Recursive Text Splitter

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

Block 13 - Sticky Note2

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

Block 14 - Sticky Note3

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

Block 15 - Sticky Note4

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

3. Summary Table

Workflow	Build a RAG document chatbot with Supabase vector search and OpenRouter
Complexity	advanced
Nodes	15
Categories	Internal Wiki, AI RAG
Author	Mariyan Nijan
Published	16 May 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/15751/15751.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 Build a RAG document chatbot with Supabase vector search and OpenRouter 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.

Mariyan Nijan

1 workflow

Nodes

n8n-nodes-base.stickynote n8n-nodes-base.webhook @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenrouter @n8n/n8n-nodes-langchain.vectorstoresupabase n8n-nodes-base.respondtowebhook n8n-nodes-base.googledrive @n8n/n8n-nodes-langchain.documentdefaultdataloader

Complexity

advanced

Published 16 May 2026

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/15751/15751.json

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Build a RAG document chatbot with Supabase vector search and OpenRouter

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

What this workflow does

How it works

Requirements

Setup

How to customize

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Sticky Note

Block 2 - Sticky Note1

Block 3 - Chat Request Webhook

Block 4 - RAG Document Assistant

Block 5 - OpenRouter LLM

Block 6 - Supabase Vector Retriever

Block 7 - Return AI Response

Block 8 - Google Drive PDF Downloader

Block 9 - Supabase Vector Store

Block 10 - Document Loader

Block 11 - Gemini Embedding Model

Block 12 - Recursive Text Splitter

Block 13 - Sticky Note2

Block 14 - Sticky Note3

Block 15 - Sticky Note4

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