This workflow automates the extraction of essays from Paul Graham’s articles page, processes the content, loads it into a Milvus vector store, and facilitates an AI-powered Q&A using OpenAI’s models. It begins with manual trigger or webhook, scrapes the latest essays, fetches and extracts relevant text, then prepares and embeds the data for vector database storage. A separate trigger supports real-time questions, allowing users to interact with the stored content via natural language queries. This setup is ideal for research, content analysis, or creating a searchable knowledge base from web articles.
Automated Paul Graham Essays Scraper & AI Q&A Workflow
| Node Count | >20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.chainRetrievalQa, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.retrieverVectorStore, @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter, @n8n/n8n-nodes-langchain.vectorStoreMilvus, html, httpRequest, limit, manualTrigger, splitOut, stickyNote |
Be the first to review “Automated Paul Graham Essays Scraper & AI Q&A Workflow”
Reviews
There are no reviews yet.