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.
Run this with your team's AIWhat 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 your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
Http, Universal Summarizer by Kagi, Neon Postgres connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect Http and Universal Summarizer by Kagi once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
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.
Coming from n8n?
This recipe uses nodes like Langchain.documentDefaultDataLoader, StickyNote, ExtractFromFile, Langchain.memoryPostgresChat and 17 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork