I Built the Ultimate n8n RAG AI Agent Template: Agentic RAG Explained

Introduction to Agentic RAG in n8n

Retrieval Augmented Generation (RAG) is a go-to method for making AI agents domain experts by connecting them to your knowledge base. Tools like n8n, a no-code workflow automation platform, make RAG implementation a breeze. But traditional RAG has flaws—it struggles with context, misses key data (e.g., trends in spreadsheets), and fails to connect documents for broader insights. Enter agentic RAG, a smarter solution. In this guide, I’ll show you how to build an ultimate agentic RAG AI agent in n8n, complete with a downloadable template!

Watch the full tutorial on YouTube and grab the template!

Why Traditional RAG Falls Short

Here’s why standard RAG can frustrate:

• Limited Context: It pulls chunks, not entire documents, missing the big picture (e.g., only a quarter of a spreadsheet).
• Weak Data Analysis: No ability to analyze tables or trends—just text lookups.
• Poor Document Linking: Can’t connect related info across files (e.g., wrong meeting notes despite clear titles).

“Good luck analyzing trends when RAG only grabs part of the data,” the creator notes.

What is Agentic RAG?

Agentic RAG supercharges traditional RAG by giving agents:

• Reasoning Power: Tools to explore the knowledge base beyond simple lookups.
• Improved Queries: Ability to refine RAG lookups iteratively.
• Multiple Tools: Options like listing documents, fetching full contents, or querying tables as SQL.

This template upgrades a basic RAG agent (v2) with PostgreSQL tools, fixing its reliance on a single, often-failing lookup.

Key Features of the Agentic RAG Template

This n8n workflow includes:

• RAG Lookup: Enhanced with source citing.
• List Documents: View all files in the knowledge base.
• Get File Contents: Retrieve entire documents by ID.
• Query Tables: Treat CSV/Excel files as SQL tables for sums, maxes, etc.

It pulls data from Google Drive, stores it in Supabase, and handles multiple file types—text, PDFs, and spreadsheets.

Step-by-Step Guide to Building the Agentic RAG Agent

1. Workflow Overview

The workflow has three main parts:

• Database Setup: Creates Supabase tables (documents, metadata, rows).
• RAG Pipeline: Ingests files from Google Drive into Supabase.
• Agent Setup: Configures the AI agent with tools.

2. Database Setup

In the red box:

• Documents Table: Stores embeddings, content, and metadata for RAG.
• Metadata Table: Holds titles, URLs, and schemas for high-level reasoning.
• Rows Table: Stores CSV/Excel data in JSONB for SQL queries.

Use PostgreSQL nodes to create these tables in Supabase.

3. RAG Pipeline

In the blue box:

1. Google Drive Trigger: Polls for new/updated files every minute (handles multiple files via a loop).
2. Clear Old Data: Deletes existing records for the file ID to avoid stale data.
3. Upsert Metadata: Adds title, URL, and schema (for tables).
4. Download File: Fetches the file from Google Drive.
5. Switch by Type: Routes to extraction nodes (CSV, PDF, etc.).
6. Extract & Process:
   • For CSVs: Extracts rows, sets schema, and converts to text for RAG.
   • For others: Extracts text directly.
7. Insert into Supabase: Chunks text, generates embeddings (OpenAI’s text-embedding-3), and stores with metadata.

Tip: Use Supabase’s transaction pooler (port 6543) for PostgreSQL credentials.

4. Agent Configuration

In the green section:

• Triggers: Webhook (API) and chat interface.
• Agent Node: Uses GPT-4o-mini with a system prompt guiding tool use.
• Tools:
  • RAG: Queries Supabase vector store, includes metadata for citations.
  • List Documents: Fetches all metadata entries.
  • Get File Contents: Combines chunks by file ID.
  • Query Tables: Writes SQL for JSONB row data.

Conversation history is auto-stored in PostgreSQL.

Testing the Agent

Examples from the video:

• “Which month had the most new customers?”: Writes SQL to query a spreadsheet, finds December (129 customers).
• “Areas we can do better with?”: Uses RAG to pull feedback from a survey doc.
• “Action items from product meeting minutes?”: Fetches full contents and cites the source.

“It switches tools seamlessly—RAG fails, so it lists docs and queries the right one,” the creator demonstrates.

Sponsor Highlight: Unstructured

Unstructured enhances RAG by extracting data from complex files (e.g., PDFs, images). Build prompts in Prompt Studio, create workflows, and deploy as APIs or ETL pipelines—perfect for messy documents!

Customizing the Template

This isn’t plug-and-play—tweak it for your needs:

• Prompts: Refine the system prompt and tool descriptions.
• File Types: Add extraction nodes (e.g., JSON, HTML).
• Chunking: Adjust the text splitter for your data.
• LLM: Upgrade to GPT-4o or Claude 3.5 Sonnet for tougher queries.

Want a local version? Comment below—I’ll build it with the LocalAI package!

Conclusion

This agentic RAG template in n8n overcomes traditional RAG’s limits, offering a robust starting point for AI agents. Download it, adapt it, and explore your knowledge base like never before. For more n8n and AI content, like and subscribe on YouTube!