Build multi-client agentic RAG document processing pipeline with Supabase Vector DB

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Important notice

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

1. Workflow Overview

Ultimate n8n Agentic RAG Template Author: Cole Medin What is this? This template provides a complete implementation of an Agentic RAG (Retrieval Augmented Gene...

Best for

AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.embeddingsopenai, n8n-nodes-base.googledrive, n8n-nodes-base.googledrivetrigger, n8n-nodes-base.extractfromfile, n8n-nodes-base.supabase, n8n-nodes-base.set, n8n-nodes-base.aggregate

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Build multi-client agentic RAG document processing pipeline with Supabase Vector DB
Workflow name: Build multi-client agentic RAG document processing pipeline with Supabase Vector DB

Ultimate n8n Agentic RAG Template

Author: Cole Medin

What is this?

This template provides a complete implementation of an Agentic RAG (Retrieval Augmented Generation) system in n8n that can be extended easily for your specific use case and knowledge base. Unlike standard RAG which only performs simple lookups, this agent can reason about your knowledge base, self-improve retrieval, and dynamically switch between different tools based on the specific question.

Why Agentic RAG?

Standard RAG has significant limitations:

Poor analysis of numerical/tabular data
Missing context due to document chunking
Inability to connect information across documents
No dynamic tool selection based on question type

What makes this template powerful:

Intelligent tool selection: Switches between RAG lookups, SQL queries, or full document retrieval based on the question
Complete document context: Accesses entire documents when needed instead of just chunks
Accurate numerical analysis: Uses SQL for precise calculations on spreadsheet/tabular data
Cross-document insights: Connects information across your entire knowledge base
Multi-file processing: Handles multiple documents in a single workflow loop
Efficient storage: Uses JSONB in Supabase to store tabular data without creating new tables for each CSV

Getting Started

Run the table creation nodes first to set up your database tables in Supabase
Upload your documents through Google Drive (or swap out for a different file storage solution)
The agent will process them automatically (chunking text, storing tabular data in Supabase)
Start asking questions that leverage the agent's multiple reasoning approaches

Customization

This template provides a solid foundation that you can extend by:

Tuning the system prompt for your specific use case
Adding document metadata like summaries
Implementing more advanced RAG techniques
Optimizing for larger knowledge bases

I do intend on making a local version of this agent very soon!

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 - Default Data Loader

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

Block 2 - Embeddings OpenAI1

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

Block 3 - Download File

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

Block 4 - File Created

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

Block 5 - File Updated

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

Block 6 - Extract Document Text

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

Block 7 - Delete Old Doc Rows

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

Block 8 - Set File ID

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

Block 9 - Extract PDF Text

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

Block 10 - Aggregate

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

Block 11 - Character Text Splitter

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

Block 12 - Summarize

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

Block 13 - Switch

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

Block 14 - Insert into Supabase Vectorstore

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

Block 15 - Extract from Excel

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

Block 16 - Set Schema

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

Block 17 - Extract from CSV

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

Block 18 - Loop Over Items

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

Block 19 - Delete Old Data Rows

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

Block 20 - Insert Document Metadata

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.5

Block 21 - Insert Table Rows

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.5

Block 22 - Update Schema for Document Metadata

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.5

Block 23 - Create Document Metadata Table1

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.5

Block 24 - Create Document Rows Table (for Tabular Data)1

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.5

Showing the first 24 of 38 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Build multi-client agentic RAG document processing pipeline with Supabase Vector DB
Complexity	advanced
Nodes	38
Categories	AI RAG
Author	Growth AI
Published	25 Sept 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/8916/8916.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 AI RAG workflows

Frequently asked questions

What does Build multi-client agentic RAG document processing pipeline with Supabase Vector DB do?

Ultimate n8n Agentic RAG Template Author: Cole Medin What is this? This template provides a complete implementation of an Agentic RAG (Retrieval Augmented Gene...

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 AI RAG use case.

Growth AI

39 workflows

Nodes

@n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.embeddingsopenai n8n-nodes-base.googledrive n8n-nodes-base.googledrivetrigger n8n-nodes-base.extractfromfile n8n-nodes-base.supabase n8n-nodes-base.set n8n-nodes-base.aggregate

Complexity

advanced

Published 25 Sept 2025

Likes 0

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Install path: /data/workflows/8916/8916.json

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Build multi-client agentic RAG document processing pipeline with Supabase Vector DB

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Ultimate n8n Agentic RAG Template

What is this?

Why Agentic RAG?

What makes this template powerful:

Getting Started

Customization

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Default Data Loader

Block 2 - Embeddings OpenAI1

Block 3 - Download File

Block 4 - File Created

Block 5 - File Updated

Block 6 - Extract Document Text

Block 7 - Delete Old Doc Rows

Block 8 - Set File ID

Block 9 - Extract PDF Text

Block 10 - Aggregate

Block 11 - Character Text Splitter

Block 12 - Summarize

Block 13 - Switch

Block 14 - Insert into Supabase Vectorstore

Block 15 - Extract from Excel

Block 16 - Set Schema

Block 17 - Extract from CSV

Block 18 - Loop Over Items

Block 19 - Delete Old Data Rows

Block 20 - Insert Document Metadata

Block 21 - Insert Table Rows

Block 22 - Update Schema for Document Metadata

Block 23 - Create Document Metadata Table1

Block 24 - Create Document Rows Table (for Tabular Data)1

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