Local document question answering with Ollama AI, Agentic RAG & PGVector
n8n35 modulesv1.0n8n Local AI Agentic RAG Template Author: Jadai kongolo What is this? This template provides an entirely local 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 differ
At a glance
Local document question answering with Ollama AI, Agentic RAG & PGVector is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects UNSUPPORTED_SLUG_FORMAT. It's free to download. Follow the 5-step import below to go live in minutes.
- Platform
- n8n
- Connects
- UNSUPPORTED_SLUG_FORMAT
- Modules
- 35
- Price
- Free
- Version
- v1.0
About this workflow
n8n Local AI Agentic RAG Template Author: Jadai kongolo What is this? This template provides an entirely local 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 1. Run the table creation nodes first to set up your database tables in Supabase 2. Upload your documents to the folder on your computer that is mounted to /data/shared in the n8n container. This folder by default is the "shared" folder in the local AI package. 3. The agent will process them automatically (chunking text, storing tabular data in Supabase) 4. 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 --- The non-local ("cloud") version of this Agentic RAG agent can be found here.
How to import this n8n workflow
- 1
Download the workflow JSON file after purchase.
- 2
Open n8n → click the menu → Import from File.
- 3
Select the downloaded JSON and import.
- 4
Set up credentials for each node that requires them.
- 5
Click Execute Workflow to test, then activate.
Setup guide
Setup guide included
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- JSON blueprint — instant download
- Setup guide PDF included
- 5 downloads · valid 30 days
- Works with n8n
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