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Create a RAG system with Paul Essays, Milvus, and OpenAI for cited answers

n8nn8n20 modulesv1.0
OpenAI

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: 1. Data collection/processing 2. 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. St

At a glance

Create a RAG system with Paul Essays, Milvus, and OpenAI for cited answers is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects OpenAI. It's free to download. Follow the 5-step import below to go live in minutes.

Platform
n8n
Connects
OpenAI
Modules
20
Price
Free
Version
v1.0
Create a RAG system with Paul Essays, Milvus, and OpenAI for cited answers workflow diagram

About this workflow

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: 1. Data collection/processing 2. 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 1. Set up a Milvus server using the official guide 2. Create a collection named "mycollection" 3. 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. Step 2: Retrieval and Response Generation When a chat message is received, the system: Sets chunks to send to the model Retrieves relevant information from the Milvus Vector Store Prepares chunks Answers the query based on those chunks Composes citations Generates a comprehensive response This process uses OpenAI embeddings and models to ensure accurate and relevant answers with proper citations. For more information on vector databases and similarity search, visit Milvus documentation.

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How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n → click the menu → Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

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