Upsert huge documents in a vector store with Supabase and Notion

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Important notice

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

1. Workflow Overview

Purpose This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vecto...

Best for

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

Tools used

@n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.textsplittertokensplitter, n8n-nodes-base.splitinbatches, @n8n/n8n-nodes-langchain.chainretrievalqa, @n8n/n8n-nodes-langchain.retrievervectorstore, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.chattrigger, n8n-nodes-base.scheduletrigger

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Upsert huge documents in a vector store with Supabase and Notion
Workflow name: Upsert huge documents in a vector store with Supabase and Notion

Purpose

This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store.

It can also be fairly easily adapted to PGVector, Pinecone, or Qdrant by using a custom HTTP request for the latter two.

Demo

How it works

A trigger checks every minute for changes in the Notion Database. The manual polling approach improves accuracy and prevents changes from being lost between cached polling intervals.
Afterwards every updated page is processed sequentially
The Vector Database is searched using the Notion Page ID stored in the metadata of each embedding. If old entries exist, they are deleted.
All blocks of the Notion Database Page are retrieved and combined into a single string
The content is embedded and split into chunks if necessary. Metadata, including the Notion Page ID, is added during storage for future reference.
A simple Question and Answer Chain enables users to ask questions about the embedded content through the integrated chat function

Prerequisites

To setup a new Vector Store in Supabase, follow this guide
Prepare a simple Database in Notion with each Database Page containing at least a title and some content in the blocks section. You can of course also connect this to an existing Database of your choice.

Setup

Select your credentials in the nodes which require those
If you are on an n8n cloud plan, switch to the native Notion Trigger by activating it and deactivating the Schedule Trigger along with its subsequent Notion Node
Choose your Notion Database in the first Node related to Notion
Adjust the chunk size and overlap in the Token Splitter to your preference
Activate the workflow

How to use

Populate your Notion Database with useful information and use the chat mode of this workflow to ask questions about it. Updates to a Notion Page should quickly reflect in future conversations.

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 - Embeddings OpenAI

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

Block 2 - Token Splitter

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

Block 3 - Loop Over Items

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

Block 4 - Question and Answer Chain

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

Block 5 - Vector Store Retriever

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

Block 6 - OpenAI Chat Model

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

Block 7 - When chat message received

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

Block 8 - Schedule Trigger

Type / Role: n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices: Version 1.2

Block 9 - Sticky Note

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

Block 10 - Limit

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

Block 11 - Limit1

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

Block 12 - Delete old embeddings if exist

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

Block 13 - Get page blocks

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

Block 14 - Default Data Loader

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

Block 15 - Sticky Note1

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

Block 16 - Input Reference

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

Block 17 - Notion Trigger

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

Block 18 - Get updated pages

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

Block 19 - Sticky Note23

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

Block 20 - Sticky Note24

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

Block 21 - Sticky Note25

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

Block 22 - Sticky Note26

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

Block 23 - Sticky Note27

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

Block 24 - Sticky Note28

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

Showing the first 24 of 34 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Upsert huge documents in a vector store with Supabase and Notion
Complexity	advanced
Nodes	34
Categories	Internal Wiki, AI RAG
Author	Mario
Published	24 Nov 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2568/2568.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 Upsert huge documents in a vector store with Supabase and Notion 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.

Mario

18 workflows

Nodes

@n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.textsplittertokensplitter n8n-nodes-base.splitinbatches @n8n/n8n-nodes-langchain.chainretrievalqa @n8n/n8n-nodes-langchain.retrievervectorstore @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.chattrigger n8n-nodes-base.scheduletrigger

Complexity

advanced

Published 24 Nov 2024

Likes 0

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Install path: /data/workflows/2568/2568.json

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Upsert huge documents in a vector store with Supabase and Notion

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Purpose

Demo

How it works

Prerequisites

Setup

How to use

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Embeddings OpenAI

Block 2 - Token Splitter

Block 3 - Loop Over Items

Block 4 - Question and Answer Chain

Block 5 - Vector Store Retriever

Block 6 - OpenAI Chat Model

Block 7 - When chat message received

Block 8 - Schedule Trigger

Block 9 - Sticky Note

Block 10 - Limit

Block 11 - Limit1

Block 12 - Delete old embeddings if exist

Block 13 - Get page blocks

Block 14 - Default Data Loader

Block 15 - Sticky Note1

Block 16 - Input Reference

Block 17 - Notion Trigger

Block 18 - Get updated pages

Block 19 - Sticky Note23

Block 20 - Sticky Note24

Block 21 - Sticky Note25

Block 22 - Sticky Note26

Block 23 - Sticky Note27

Block 24 - Sticky Note28

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