The Ultimate Guide to Building a RAG Pipeline with n8n

by Tom Crawshaw

This n8n RAG tutorial will show you how to automatically convert any document you upload to Google Drive into searchable embeddings stored in a Pinecone n8n vector store. Your AI agents can then query this n8n knowledge base for precise, contextual responses instead of generic outputs. This is a powerful alternative to building with LangChain in n8n.

What this n8n RAG Workflow Does

• Monitors Google Drive for new files
• Downloads and processes documents automatically
• Splits text into optimal chunks
• Creates embeddings using OpenAI
• Stores everything in Pinecone for lightning-fast retrieval

Why Not Use LangChain?

While you can use LangChain in n8n, building the RAG pipeline natively in n8n gives you more control and a deeper understanding of the process. This tutorial focuses on a pure n8n implementation for maximum flexibility.

Prerequisites

Before we start, make sure you have the following:

• n8n instance: A self-hosted or cloud account.
• Google Drive API access: For document monitoring and retrieval.
• Pinecone account: The free tier works perfectly for this setup.
• OpenAI API key: For generating embeddings.

Step 1: Get Your API Keys

First, you'll need to collect your API keys from Pinecone and OpenAI.

Pinecone Setup

1. Create Account: Go to pinecone.io and create a free account.
2. Copy Credentials: In your dashboard, copy your API Key and Environment name.
3. Create Index: Create a new index with the following settings:

Name: knowledge-base (or your preference)

Dimensions: 1536 (this is required for OpenAI's text-embedding-ada-002 model)

Metric: cosine

OpenAI Setup

1. Go to platform.openai.com.
2. Create a new API key and copy it.

Security Note: Keep your API keys secure. Use n8n's built-in credential management or environment variables instead of hardcoding them in your workflows.

Google Drive Setup

1. Go to the Google Cloud Console and create or select a project.
2. Enable the Google Drive API for your project.
3. Create OAuth 2.0 credentials.
4. In n8n, add a Google Drive credential and log in with OAuth to connect your account.

Step 2: Import the Workflow

Download the JSON file and import it into your n8n instance. This will create the complete workflow for you.

Step 3: Configure Each Node

Go through each node in the imported workflow to add your credentials and check the settings.

Google Drive Trigger

• Connect your Google account.
• Set the trigger event to "File Created".
• Choose the folder you want to monitor.

Google Drive Download

• Select the same Google account.
• Operation should be set to "Download".
• File ID should be set using an expression: ={{ $json.id }}

Recursive Character Text Splitter

• Chunk Size: 500 (a good starting point).
• Chunk Overlap: 20 (prevents losing context between chunks).

OpenAI Embeddings

• Add your OpenAI API key credential.
• Model: text-embedding-ada-002

Pinecone Vector Store

• Add your Pinecone API key credential.
• Environment: Your Pinecone environment name.
• Index Name: knowledge-base (or the name you chose).
• Mode: Insert

Step 4: Test the Workflow

Now it's time to test your new RAG pipeline.

1. Activate the workflow using the toggle in the top right.
2. Upload a test document to your monitored Google Drive folder.
3. Check your Pinecone index to see if the new vectors have been added.

Advanced Configuration & Troubleshooting

Here are a few tips for optimizing and troubleshooting your workflow.

Optimize Chunk Size

Adjusting chunk size based on document type improves retrieval accuracy. Here are some good starting points:

• Technical Docs: 800-1000 characters
• Marketing Content: 1200-1500 characters
• Legal Documents: 600-800 characters

Common Issues

• Pinecone connection failed: Check your API key, environment, and index name. Verify the index dimensions are 1536.
• OpenAI rate limit exceeded: Add a delay node in your workflow for bulk processing or consider upgrading your OpenAI plan.
• Text splitting failed: Ensure your file is UTF-8 encoded and not corrupted.

Next Steps

This workflow is just the beginning. The real power comes from building a second workflow to query this knowledge base. From there, you can:

• Build AI agents that reference this knowledge for decision-making.
• Add more data sources like web pages, databases, and other APIs.
• Set up automated cleanup systems to manage storage costs.