Paul Graham Essay Search & Chat with Milvus Vector DB
Creates a RAG system to scrape Paul Graham essays, embed them in Milvus vector DB, and enable semantic search with AI chat.
This n8n workflow implements a Retrieval-Augmented Generation (RAG) pipeline for Paul Graham's essays. It starts by scraping the essay list from paulgraham.com, extracts content, generates vector embeddings, and stores them in a Milvus vector database. Once loaded, users can perform semantic searches and engage in AI-powered conversations about the essays via an integrated chat interface.
Key benefits include efficient knowledge retrieval without manual indexing, scalable vector search for precise matches, and natural language querying powered by AI agents. It saves time for researchers, writers, or fans analyzing PG's ideas on startups, programming, and life.
Use cases: Academic research on essays, content inspiration for bloggers, startup idea brainstorming via chat, or building custom knowledge bases. Setup requires Milvus installation; execute once to ingest data, then query indefinitely. Integrates HTTP requests, HTML parsing, embeddings, Milvus ops, and AI tools for a complete RAG solution.
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How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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