Automate Essay Retrieval and AI Analysis with n8n
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Streamline the process of fetching, extracting, and analyzing content from web articles. This workflow automates the retrieval of Paul Graham's essays, processes their text, and prepares them for AI-driven insights using Milvus.
About This Workflow
This n8n workflow offers a powerful solution for content automation and AI integration. It begins by scraping a list of essays from Paul Graham's website, then meticulously extracts the relevant essay links. The workflow intelligently limits the scope to the first three essays for focused analysis. It proceeds to fetch the full text of each essay, cleans up the HTML to isolate pure content, and then leverages advanced text splitting techniques. Finally, the processed text is prepared for ingestion into a Milvus vector store, enabling sophisticated AI-powered question answering and analysis. This is an ideal tool for researchers, content curators, and anyone looking to harness the power of AI on unstructured text data.
Key Features
- Automated Web Scraping: Effortlessly fetch content from specified web pages.
- Intelligent Content Extraction: Precisely extract links and pure text from HTML.
- AI-Ready Data Preparation: Split large texts into manageable chunks for AI processing.
- Vector Store Integration: Prepare data for seamless integration with Milvus for advanced AI capabilities.
- Configurable Processing: Easily adjust parameters like the number of essays to process.
How To Use
- Trigger Workflow: Manually execute the workflow by clicking 'Execute Workflow'.
- Fetch Essay List: The workflow automatically fetches the list of articles from
http://www.paulgraham.com/articles.html. - Extract Essay Links: The 'Extract essay names' node uses CSS selectors to get the
hrefattribute of essay links. - Process Essays: The workflow limits the processing to the first 3 essays and fetches their full content.
- Clean HTML: The 'Extract Text Only' node cleans the HTML, removing unnecessary elements like images and navigation.
- Split Text: Utilize the 'Recursive Character Text Splitter' to break down the essay content into smaller, AI-digestible chunks (configurable via
chunkSize). - Prepare for AI: The 'Prepare chunks' code node formats the text chunks for context, and the 'Set max chunks' node defines how many chunks are sent to the AI.
- AI Analysis: The 'Answer the query based on chunks' node (using Langchain's Information Extractor) takes the prepared context and a user query to generate an answer and identify relevant citations.
- Format Output: The 'Compose citations' and 'Generate response' nodes format the AI's answer and its source citations for clear presentation.
Apps Used
Workflow JSON
{
"id": "dcbb4cc8-6e18-43d0-a2dd-2d6400715fab",
"name": "Automate Essay Retrieval and AI Analysis with n8n",
"nodes": 25,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: dcbb4cc8-6e18...
About the Author
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Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
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