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Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers

by Cheney Zhangadapted from n8n official workflow galleryUpdated Aug 2026
RequiresCCodeDDefault Data LoaderEmbeddings OpenAIEmbeddings OpenAIHTMLHTMLHTTP RequestHTTP RequestInformation ExtractorInformation ExtractorMilvus Vector StoreMilvus Vector StoreOpenAI Chat ModelOpenAI Chat ModelRecursive Character Text SplitterRecursive Character Text Splitter
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MaWhen clicking "Execute Workflow"When clicking "…HRFetch Essay ListHtExtract essay namesExtract essay n…SOSplit out into itemsSplit out into …HRFetch essay textsFetch essay tex…LiLimit to first 3HtExtract Text OnlyExtract Text On…TSRecursive Character Text Splitter1Recursive Chara…SeGenerate responseGenerate respon…SeCompose citationsCompose citatio…IEAnswer the query based on chunksAnswer the quer…CoPrepare chunksSeSet max chunks to send to modelSet max chunks …Embeddings OpenAI2Embeddings Open…ChWhen chat message receivedWhen chat messa…VSMilvus Vector Store in retrievalMilvus Vector S…VSMilvus Vector StoreMilvus Vector S…Embeddings OpenAIEmbeddings Open…DDDefault Data LoaderDefault Data Lo…OpenAI Chat ModelOpenAI Chat Mod…12345678910
1/5
FLOWS
STEPS · 10
Run manually by an operator

Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: Data collection/processing Retrieval/response generation The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations. Step 1: Data Collection and Processing Set up a Milvus server using the official guide Create a collection named "my_collection" Execute the workflow to scrape Paul Graham essays: Fetch essay lists Extract names Split content into manageable items Limit results (if needed) Fetch texts Extract content Load everything into Milvus Vector Store This step uses OpenAI embeddings for vectorization.

Tags

n8nreference-onlydocument-default-data-loaderembeddings-open-aihtmlinformation-extractorlm-chat-open-aitext-splitter-recursive-character-text-splittervector-store-milvus
Connects
CCodeDDefault Data LoaderembeddingsopenaiEmbeddings OpenAIhtmlHTMLWHTTP RequestinformationextractorInformation Extractor
CategoryAI Automation
Triggermanual
Complexityadvanced
Nodes20
AddedApr 16, 2025

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