Automate Paul Graham Essays Processing with n8n and Milvus

• Content Creators: Those who want to gather and analyze essays for inspiration or research.
• Students: Individuals looking for quality essays to reference in their academic work.
• Developers: Programmers interested in integrating essay scraping into their applications.
• Researchers: Academics needing a streamlined process to access and store essay content for analysis.
• Data Scientists: Professionals who require a structured way to gather and utilize textual data for machine learning models.

This workflow automates the process of scraping essays from a website, extracting their content, and storing them in a vector database. It provides a seamless way to gather information without manual effort, enabling users to quickly access and analyze a collection of essays.

• Step 1: Trigger the workflow manually by clicking "Execute Workflow".
• Step 2: Fetch a list of essays from Paul Graham's articles page.
• Step 3: Extract the names of the essays using HTML parsing techniques.
• Step 4: Split the essay names into individual items for processing.
• Step 5: Limit the process to the first 3 essays for efficiency.
• Step 6: Fetch the full text of each essay from their respective URLs.
• Step 7: Extract only the textual content from the fetched essays.
• Step 8: Prepare the text for further processing by splitting it into manageable chunks.
• Step 9: Generate embeddings for the essay texts using OpenAI's embedding model.
• Step 10: Store the embeddings in a Milvus vector store for efficient retrieval.
• Step 11: When a chat message is received, query the vector store to find relevant essay content.
• Step 12: Answer the user's query based on the retrieved chunks, including citations where applicable.