Local document question answering with Ollama AI, Agentic RAG & PGVector
This workflow sets up a powerful, locally-hosted Agentic RAG system that goes beyond simple keyword searches to intelligently query your documents. By leveraging Ollama AI and PGVector, it can analyze complex tabular data and connect insights across multiple files for precise answers. It’s an ideal solution for users who need a private, self-improving knowledge base that handles both text and structured spreadsheets.
Start BuildingWhat This Recipe Does
The n8n Local AI Agentic RAG (Retrieval-Augmented Generation) Template transforms your private business documents into an intelligent, searchable knowledge base. By combining local file processing with advanced AI reasoning, this automation allows your team to query complex internal datasets and receive precise, context-aware answers instantly. Unlike standard search tools, this system understands the nuances of your specific documentation, extracting and summarizing relevant information to provide actionable insights. It eliminates the time wasted manually searching through folders and files, ensuring that your organization's collective intelligence is always accessible. This solution is particularly valuable for businesses that prioritize data privacy, as it manages the ingestion and retrieval process locally while providing a seamless interface for users to interact with their data through a web-based portal.
What You'll Get
Forms, dashboards, and UI components ready to use
Background automations that run on your schedule
REST APIs for external integrations
HTTP / Webhook, Universal Summarizer by Kagi, Neon Postgres configured and ready
How It Works
- 1
Click "Start Building" and connect your accounts
Runwork will guide you through connecting HTTP / Webhook and Universal Summarizer by Kagi
- 2
Describe any customizations you need
The AI will adapt the recipe to your specific requirements
- 3
Preview, test, and deploy
Your app is ready to use in minutes, not weeks
Who Uses This
- Customer support teams can instantly retrieve specific product details or policy information from technical manuals to resolve tickets faster.
- Operations managers can query internal process documents and standard operating procedures to ensure compliance and training accuracy.
- Legal and HR departments can search through large volumes of contracts or employee handbooks to find specific clauses or regulations without manual reading.
Frequently Asked Questions
Do I need to be a developer to use this application?
No. While the backend uses complex AI logic, the final application provides a simple chat-like interface for business users to ask questions and receive answers.
Can I use my own internal documents with this system?
Yes. The automation is designed to read and process your local files, including text and document formats, to build your custom knowledge base.
Is my data shared with external AI providers?
This template is configured for local processing, meaning your sensitive business documents stay within your controlled environment during the extraction and retrieval phase.
How accurate are the answers provided by the AI?
The system uses Retrieval-Augmented Generation, which means it only answers based on the specific documents you provide, significantly reducing the risk of incorrect or fabricated information.
Importing from n8n?
This recipe uses nodes like Langchain.documentDefaultDataLoader, StickyNote, ExtractFromFile, Langchain.memoryPostgresChat and 17 more. With Runwork, you don't need to learn n8n's workflow syntax. Just describe what you want in plain English.
Based on n8n community workflow. View original
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Local document question answering with Ollama AI, Agentic RAG & PGVector
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