Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker

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Important notice

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

1. Workflow Overview

This workflow contains community nodes that are only compatible with the self hosted version of n8n. This comprehensive RAG workflow enables your AI agents to answer user questions with contextual ...

Best for

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

Tools used

n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.documentdefaultdataloader, n8n-nodes-base.extractfromfile, n8n-nodes-base.code, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.lmchatopenrouter, @n8n/n8n-nodes-langchain.rerankercohere, @n8n/n8n-nodes-langchain.vectorstoresupabase

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker
Workflow name: Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker

This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

This comprehensive RAG workflow enables your AI agents to answer user questions with contextual knowledge pulled from your own documents — using metadata-rich embeddings stored in Supabase.

🔧 Key Features: RAG Agents powered by GPT-4.5 or GPT-3.5 via OpenRouter or OpenAI.

Supabase Vector Store to store and retrieve document embeddings.

Cohere Reranker to improve response relevance and quality.

Metadata Agent to enrich vectorized data before ingestion.

PDF Extraction Flow to automatically parse and upload documents with metadata.

✅ Setup Steps: Connect your Supabase Vector Store.

Use OpenAI Embeddings (e.g. text-embedding-3-small).

Add API keys for OpenAI and/or OpenRouter.

Connect a reranker like Cohere.

Process documents with metadata before embedding.

Start chatting — your AI agent now returns context-rich answers from your own knowledge base!

Perfect for building AI assistants that can reason, search and answer based on internal company data, academic papers, support docs, or personal notes.

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 - Download File

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

Block 2 - Default Data Loader

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

Block 3 - Extract from File

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

Block 4 - Code

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

Block 5 - When chat message received

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

Block 6 - GPT 4.1-mini

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

Block 7 - Reranker Cohere

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

Block 8 - Upload to Supabase

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

Block 9 - Supabase Vector Store

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

Block 10 - GPT 4.1-mini1

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

Block 11 - Reranker Cohere1

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

Block 12 - Embeddings OpenAI2

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

Block 13 - Supabase Vector Store1

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

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

Block 17 - Embeddings OpenAI1

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

Block 18 - Embeddings OpenAI

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

Block 19 - Metadata Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 20 - RAG Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 21 - RAG Agent 2

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 22 - Sticky Note3

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

Block 23 - Sticky Note4

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

Block 24 - Sticky Note5

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

Showing the first 24 of 26 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker
Complexity	advanced
Nodes	26
Categories	Internal Wiki, AI RAG
Author	Luan Correia
Published	23 Jul 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/6345/6345.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 Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker 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.

Luan Correia

3 workflows

Nodes

n8n-nodes-base.googledrive @n8n/n8n-nodes-langchain.documentdefaultdataloader n8n-nodes-base.extractfromfile n8n-nodes-base.code @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.lmchatopenrouter @n8n/n8n-nodes-langchain.rerankercohere @n8n/n8n-nodes-langchain.vectorstoresupabase

Complexity

advanced

Published 23 Jul 2025

Likes 0

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Install path: /data/workflows/6345/6345.json

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Answer questions from documents with RAG using Supabase, OpenAI & Cohere reranker

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Download File

Block 2 - Default Data Loader

Block 3 - Extract from File

Block 4 - Code

Block 5 - When chat message received

Block 6 - GPT 4.1-mini

Block 7 - Reranker Cohere

Block 8 - Upload to Supabase

Block 9 - Supabase Vector Store

Block 10 - GPT 4.1-mini1

Block 11 - Reranker Cohere1

Block 12 - Embeddings OpenAI2

Block 13 - Supabase Vector Store1

Block 14 - Sticky Note

Block 15 - Sticky Note1

Block 16 - Sticky Note2

Block 17 - Embeddings OpenAI1

Block 18 - Embeddings OpenAI

Block 19 - Metadata Agent

Block 20 - RAG Agent

Block 21 - RAG Agent 2

Block 22 - Sticky Note3

Block 23 - Sticky Note4

Block 24 - Sticky Note5

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