Templates / Create a Paul Graham essay Q&A system with OpenAI and Milvus vector database

Create a Paul Graham essay Q&A system with OpenAI and Milvus vector database

Ready for 3.0

by Cheney Zhang ✓ · 1,262 views · created 2025-04-16 · Engineering, AI RAG

Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.

What this workflow does

This AI workflow has 17 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects OpenAI Chat Model, Milvus Vector Store and Embeddings OpenAI. It also calls paulgraham.com over HTTP.

How it runs

Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).

  1. Fetch Essay List (HTTP Request): calls paulgraham.com
  2. Q&A Chain to Retrieve from Milvus and Answer Question (Question and Answer Chain): answers questions over your documents (RAG), with model: OpenAI Chat Model; retriever: Milvus Vector Store Retriever
  3. Extract essay names (HTML): parses or builds HTML
  4. Split Out: splits a list into separate items
  5. Limit: keeps the first few items
  6. Fetch essay texts (HTTP Request): calls an HTTP API
  7. Extract Text Only (HTML): parses or builds HTML
  8. Milvus Vector Store: stores documents in the Milvus vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader

Services it uses

OpenAI Chat ModelMilvus Vector StoreEmbeddings OpenAI

HTTP calls to: paulgraham.com

Scraping pages by hand? Firecrawl is a simpler option (10% off for new users).

Before you upgrade to n8n 3.0

Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.

Nodes used (13)

From the author

Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database How It Works This workflow creates a question-answering system based on Paul Graham essays. It has two main steps: 1. Data Collection & Processing: - Scrapes Paul Graham essays -…

Excerpt from Cheney Zhang's description on n8n.io.

View the original on n8n.io → Check your own copy

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