Paul Graham essay search & chat with Milvus vector database

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Paul Graham Essay Search & Chat with Milvus Vector Database How It Works This workflow creates a RAG (Retrieval Augmented Generation) system using Milvus vector database to se...

Best for

Engineering automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.httprequest, n8n-nodes-base.html, n8n-nodes-base.splitout, n8n-nodes-base.limit, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.vectorstoremilvus

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheney Zhang.

Original n8n.io source

1.1 Workflow description

Title: Paul Graham essay search & chat with Milvus vector database
Workflow name: Paul Graham essay search & chat with Milvus vector database

Paul Graham Essay Search & Chat with Milvus Vector Database

How It Works

This workflow creates a RAG (Retrieval-Augmented Generation) system using Milvus vector database to search Paul Graham essays:

Scrape & Load: Fetches Paul Graham essays, extracts text, and stores them as vector embeddings in Milvus
Chat Interface: Enables semantic search and AI-powered conversations about the essays

Set Up Steps

Set up Milvus server following the official installation guide, then create a collection
Execute the workflow to scrape essays and load them into your Milvus collection
Chat with the AI agent using the Milvus tool to query and discuss essay content

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When clicking "Execute Workflow"

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 2 - Fetch Essay List

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 3 - Extract essay names

Type / Role: n8n-nodes-base.html - html
Config choices: Version 1.2

Block 4 - Split out into items

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Block 5 - Fetch essay texts

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 6 - Limit to first 3

Type / Role: n8n-nodes-base.limit - limit
Config choices: Version 1

Block 7 - Extract Text Only

Type / Role: n8n-nodes-base.html - html
Config choices: Version 1.2

Block 8 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 9 - Sticky Note5

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 10 - Recursive Character Text Splitter1

Type / Role: @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - textSplitterRecursiveCharacterTextSplitter
Config choices: Version 1

Block 11 - Milvus Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreMilvus - vectorStoreMilvus
Config choices: Version 1.1

Block 12 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.9

Block 13 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 14 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 15 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 16 - Milvus Vector Store as tool

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreMilvus - vectorStoreMilvus
Config choices: Version 1.1

Block 17 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 18 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 19 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 20 - Embeddings OpenAI1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 21 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

3. Summary Table

Workflow	Paul Graham essay search & chat with Milvus vector database
Complexity	advanced
Nodes	21
Categories	Engineering, AI RAG
Author	Cheney Zhang
Published	16 Apr 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/3576/3576.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI RAG workflows

Frequently asked questions

What does Paul Graham essay search & chat with Milvus vector database do?

Paul Graham Essay Search & Chat with Milvus Vector Database How It Works This workflow creates a RAG (Retrieval Augmented Generation) system using Milvus vector database to se...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.

Cheney Zhang

3 workflows

Nodes

n8n-nodes-base.manualtrigger n8n-nodes-base.httprequest n8n-nodes-base.html n8n-nodes-base.splitout n8n-nodes-base.limit n8n-nodes-base.stickynote @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter @n8n/n8n-nodes-langchain.vectorstoremilvus

Complexity

advanced

Published 16 Apr 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/3576/3576.json

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Paul Graham essay search & chat with Milvus vector database

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Paul Graham Essay Search & Chat with Milvus Vector Database

How It Works

Set Up Steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking "Execute Workflow"

Block 2 - Fetch Essay List

Block 3 - Extract essay names

Block 4 - Split out into items

Block 5 - Fetch essay texts

Block 6 - Limit to first 3

Block 7 - Extract Text Only

Block 8 - Sticky Note3

Block 9 - Sticky Note5

Block 10 - Recursive Character Text Splitter1

Block 11 - Milvus Vector Store

Block 12 - AI Agent

Block 13 - When chat message received

Block 14 - Sticky Note

Block 15 - Default Data Loader

Block 16 - Milvus Vector Store as tool

Block 17 - Sticky Note1

Block 18 - Embeddings OpenAI

Block 19 - OpenAI Chat Model

Block 20 - Embeddings OpenAI1

Block 21 - Sticky Note2

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions