Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o

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Important notice

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

1. Workflow Overview

How it works 1. Provide your S3 bucket containing documents such as PDFs and MS Word in the "Get Files from S3" node. You will need to provide AWS credentials that will allow the node to access the...

Best for

Document Extraction automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.splitinbatches, n8n-nodes-base.awss3, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o
Workflow name: Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o

How it works

Provide your S3 bucket containing documents such as PDFs and MS Word in the "Get Files from S3" node. You will need to provide AWS credentials that will allow the node to access the bucket and download the files in the specified location.
Choose document processing options in the Aryn node. The main options are for text and table extraction. You can also provide a JSON schema for property extraction. You can refer to https://docs.aryn.ai/docparse/processing_options for details on these options. You will also need an Aryn API key which you can obtain by going to https://aryn.ai/signup. Please note that use of vision models for OCR and table extraction is restricted to paid tiers.
The resulting content of parsing and extraction is then chunked and ingested into Pinecone.
Once at least one document has been ingested into a Pinecone index, you can start asking questions about anything that may be found in ingested documents in the chat box.

Setup steps

For data retrieval, you will need a "folder" in a bucket on AWS S3 as well as valid AWS credentials with permission to fetch those files.
For document parsing, you will need to obtain an Aryn API key. You can sign up for free at https://aryn.ai/signup.
For the Pinecone vector database, head over to https://pinecone.io and create an account and create a sample index for free. You will also need to generate an API key.
For the AI agent and RAG, you will also need an OpenAI API key. Please go to https://openai.com and get a free API key.

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 clicking ‘Test workflow’

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

Block 2 - Loop Over Items

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

Block 3 - Download Files from AWS

Type / Role: n8n-nodes-base.awsS3 - awsS3
Config choices: Version 2

Block 4 - Get Files from S3

Type / Role: n8n-nodes-base.awsS3 - awsS3
Config choices: Version 2

Block 5 - Embeddings OpenAI

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

Block 6 - Default Data Loader

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

Block 7 - Recursive Character Text Splitter

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

Block 8 - When chat message received

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

Block 9 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.9

Block 10 - OpenAI Chat Model

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

Block 11 - Pinecone Vector Store Tool

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

Block 12 - Pinecone Vector Store

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

Block 13 - Aryn

Type / Role: @aryn-ai/n8n-nodes-aryn.aryn - aryn
Config choices: Version 1

Block 14 - Sticky Note

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

Block 15 - Sticky Note1

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

Block 16 - Sticky Note2

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

3. Summary Table

Workflow	Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o
Complexity	advanced
Nodes	16
Categories	Document Extraction, AI RAG
Author	Austin Lee
Published	07 Jan 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/12531/12531.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 Document Extraction workflows AI RAG workflows

Frequently asked questions

What does Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o 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 Document Extraction, AI RAG use case.

Austin Lee

1 workflow

Nodes

n8n-nodes-base.manualtrigger n8n-nodes-base.splitinbatches n8n-nodes-base.awss3 @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent

Complexity

advanced

Published 07 Jan 2026

Likes 0

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Install path: /data/workflows/12531/12531.json

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Build a RAG chat system using Aryn DocParse, AWS S3, Pinecone and GPT-4o

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How it works

Setup steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Block 2 - Loop Over Items

Block 3 - Download Files from AWS

Block 4 - Get Files from S3

Block 5 - Embeddings OpenAI

Block 6 - Default Data Loader

Block 7 - Recursive Character Text Splitter

Block 8 - When chat message received

Block 9 - AI Agent

Block 10 - OpenAI Chat Model

Block 11 - Pinecone Vector Store Tool

Block 12 - Pinecone Vector Store

Block 13 - Aryn

Block 14 - Sticky Note

Block 15 - Sticky Note1

Block 16 - Sticky Note2

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