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Agent Milvus Tool

This workflow automatically scrapes the latest articles from the Paul Graham website, extracts and processes the text content, and converts it into vectors stored in the Milvus database. By integrating OpenAI's embedding model, it enables intelligent Q&A and information retrieval based on the knowledge base. It supports manual triggers and chat message triggers for AI responses, making it suitable for researchers, businesses, and content creators, enhancing information management and retrieval efficiency, and streamlining the knowledge base construction process.

Workflow Diagram
Agent Milvus Tool Workflow diagram

Workflow Name

Agent Milvus Tool

Key Features and Highlights

This workflow automatically scrapes the latest article list from Paul Graham’s website, extracts article links, and retrieves the full text of each article. After text processing and chunking, the content is loaded into the Milvus vector database. Leveraging OpenAI’s embedding models, the text is converted into vector representations. Combined with an AI Agent and Milvus vector retrieval, the system enables intelligent question answering and information retrieval based on the knowledge base. The workflow supports manual trigger for scraping as well as AI responses triggered by chat messages, integrating powerful vector search and natural language processing capabilities.

Core Problems Addressed

  • Automated scraping and updating of long-form textual content (e.g., articles, papers)
  • Efficient transformation of unstructured text into vectorized data to support semantic search
  • Intelligent Q&A powered by AI Agent to enhance information retrieval efficiency
  • Simplified knowledge base construction enabling rapid content loading and real-time querying

Application Scenarios

  • Researchers automatically scraping and managing academic papers or technical documents
  • Enterprises building internal knowledge bases for intelligent Q&A and information retrieval
  • Content creators automatically collecting reference materials to assist content generation
  • AI chatbots integrating professional documents to improve answer accuracy and expertise

Main Workflow Steps

  1. Manually trigger the workflow execution
  2. Access Paul Graham’s article list page and scrape article links
  3. Extract article URLs, limiting to the first three articles
  4. Visit each article link and retrieve the full webpage content
  5. Filter webpage content to extract plain text segments
  6. Use a recursive character text splitter to chunk the text
  7. Generate text vector embeddings using OpenAI
  8. Insert vector data into the Milvus vector database (overwriting old data)
  9. Configure Milvus as the AI Agent’s knowledge retrieval tool
  10. Monitor chat messages to trigger AI Agent calls to the Milvus tool for intelligent Q&A

Involved Systems or Services

  • Paul Graham’s personal article website (http://www.paulgraham.com)
  • Milvus vector database (for storing and retrieving vectorized text)
  • OpenAI (for generating text embeddings and language models, supporting GPT-4o-mini)
  • n8n automation platform nodes (HTTP requests, HTML parsing, text splitting, vector storage, AI Agent, etc.)

Target Users and Value

  • Data scientists and AI engineers: rapidly build semantic search and knowledge Q&A systems
  • Content managers and researchers: automate management and retrieval of large text corpora
  • Enterprise technical teams: develop intelligent customer service or internal knowledge assistants
  • AI developers and product managers: integrate vector databases with large language models to enhance product intelligence

This workflow centers on automated scraping and vectorization of text, combining the powerful capabilities of Milvus and OpenAI to create a scalable intelligent knowledge base and Q&A system, significantly improving information processing efficiency and intelligent interaction experience.